The article explores the differences between graph search systems and vector search systems in retrieval-augmented generation (RAG) systems. It uses a financial report RAG example to compare the two types of answer outputs, optimizing depth and breadth through graph structures, and discovering why combining graph and vector search is the future of RAG. Graph databases like Neo4j store data and relationships, enabling explicit querying and reducing hallucinations. Knowledge graphs provide a comprehensive view of all relevant information, enhancing reasoning and extraction capabilities. By defining a schema, users can create well-scoped knowledge graphs efficiently, ensuring that the graph accurately reflects the relationships inherent in the raw data. The article also explores how combining graph and vector search using graph structures can augment retrieval, providing more complete answers in both depth and breadth, while creating a semantically consistent, accurate, and deterministic way to perform information retrieval.