Vector RAG has dominated retrieval-augmented generation implementations, but it hits a wall when answering questions requiring synthesis across multiple documents. GraphRAG addresses this limitation by converting unstructured text into knowledge graphs before retrieval, enabling AI models to answer complex, multi-document questions that vector search cannot.

Traditional RAG chunks documents and embeds them as vectors, then retrieves semantically similar chunks based on query embedding. This approach works for straightforward lookups like "What was our Q3 refund policy?" but fails for questions requiring pattern recognition across documents, such as "What are the recurring themes across two years of customer complaints?"

GraphRAG converts source documents into structured knowledge graphs using LLMs. The process extracts entities, relationships, and hierarchies from text, then organizes them into graph structures. When a user queries the system, the retrieval step operates on this graph rather than vector similarity, identifying relevant subgraphs and communities that connect to the query topic.

The performance difference emerges on multi-hop questions. A query about customer complaint themes across time requires connecting multiple document sections and recognizing patterns. Vector RAG retrieves similar chunks independently, missing connections between documents. GraphRAG traverses the graph structure to identify clusters and relationships that contain distributed answers.

The tradeoff involves computational cost and implementation complexity. Building knowledge graphs requires additional LLM calls to extract entities and relationships, increasing latency and expense. Vector RAG remains faster and cheaper for simple retrieval tasks. GraphRAG justifies this overhead when users ask complex analytical questions that vector retrieval cannot handle.

The choice depends on use case specificity. Customer support systems answering factual questions benefit from vector RAG's speed. Analytics platforms requiring cross-document synthesis justify GraphRAG's overhead. Many organizations will run both systems in parallel, routing queries based on type. GraphRAG represents not a vector RAG replacement,