Choosing the Right Retrieval Architecture: Graph RAG vs Vector RAG

This article compares the strengths and weaknesses of two approaches to Retrieval Augmented Generation (RAG) - vector-based and graph-based. It provides guidance on when to use each method based on the structure of the knowledge base and the nature of the questions being asked.

Details

The article explains that LLMs have a knowledge boundary, and RAG solves this by retrieving relevant context from an external source at inference time. Vector RAG encodes documents as dense vector embeddings and retrieves the closest chunks to the query vector. This approach works well for broad, semantically-driven queries across a large corpus. However, it struggles with multi-hop questions, queries about relationships between entities, and cases where context is important. In contrast, Graph RAG models knowledge as a graph, with nodes representing entities/concepts and edges representing relationships. This allows it to better handle questions requiring logical chain traversal and situations where the connections between information are crucial. The article discusses the trade-offs, such as the higher construction cost and latency of Graph RAG compared to the simplicity and speed of Vector RAG. It provides guidance on when to choose each approach based on the nature of the knowledge base and the types of questions being asked.