Vector RAG vs. GraphRAG: Which retrieval do you need?

A vector-only pipeline can be a great fit for your agentic AI project — right up until an accurate, explainable response depends on how disparate facts connect. Take a support example, where a manager asks which vendor’s outage caused three tickets last week. The tickets, the vendor record, and the outage report are all in the index, but none mention each other by name, so a similarity search has no way to string them together. The result: an incomplete or inaccurate answer, or none at all. That’s the limit of similarity search, which is what vector RAG uses to provide an LLM with relevant information at query time. It ranks text by how closely it reads like the question, not by how the underlying facts relate. GraphRAG takes a different approach, which is why it has been shown to more than double factual accuracy, offer better token efficiency, and provide a traceable path back to each answer’s sources. So what’s the difference between GraphRAG and vector RAG? And will adding GraphRAG to…