GraphRAG

What is GraphRAG

GraphRAG is a next-generation architecture that combines knowledge graphs with vector search to leverage relationships between entities in retrieval—relationships that conventional RAG (Retrieval-Augmented Generation) has been unable to capture.

Scenarios Where Traditional RAG Struggles

In a standard RAG pipeline, chunks are retrieved based on embedding similarity against a query. It performs poorly on questions that require reasoning across relationships spanning multiple documents, such as "Who are Company A's competitors?" Individual retrieved chunks often do not contain the answer on their own, making them a common source of hallucinations.

What Graph Structure Solves

GraphRAG first extracts entities (people, organizations, concepts, etc.) and their relationships from a corpus to construct a knowledge graph. At retrieval time, it performs path traversal over the graph in addition to vector similarity search, enabling multi-hop reasoning such as "A → competitive relationship → B." It is also a technology that Gartner has designated as a "Critical Enabler" for generative AI in 2026.

Key Considerations for Adoption

Building the graph requires processing the entire corpus with an LLM, resulting in significant upfront costs. For small-scale FAQ chatbots, hybrid search is often sufficient, so the practical approach is to first assess whether cross-document relationships are operationally significant—for example, cross-references between clauses in legal documents or supplier relationships in a supply chain—before deciding to adopt GraphRAG.