The team at Neo4j and WhyHow.AI explores how graph and vector search systems can work together to improve retrieval-augmented generation (RAG) systems. Using a financial report RAG example, we explore the differences in response between graph and vector search, benchmark the two types of answer outputs, show how depth and breadth can be optimized through graph structures, and discover why combining graph and vector search is the future of RAG.
Graph databases like Neo4j are built on the conc...
Read it: Knowledge Graph vs. Vector RAG: Benchmarking, Optimization Levers, and a Financial Analysis Example