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…

The Neo4j Blog offers insights, tutorials, and updates on graph database technology and its applications in various industries. From graph data modeling to query optimization and graph analytics, the Neo4j Blog provides resources for developers, data scientists, and business analysts looking to leverage the power of graph databases to unlock new insights from their connected data.

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Exploring the use of graph and vector search in retrieval-augmented generation (RAG) systems, focusing on their application in financial analysis. Discusses the differences between graph and vector search, optimization levers for graph search, and the combination of both methods in RAG. Highlights the benefits of graph databases for modeling complex relationships and dependencies, as well as the limitations and complementarity of vector search. Demonstrates the application of graph and vector search in a financial report RAG example.

Graph vs. Vector RAG — Benchmarking, Optimization Levers, and a Financial Analysis Example

Answer Completeness and Limitations of Graph and Vector Querying