One query, not two stores: how vector + graph in SurrealDB makes agents more accurate

SurrealDB streamlines the process of retrieval-augmented generation (RAG) by integrating vector and graph databases into a single engine, eliminating the need for application code to reconcile data from separate sources. This approach allows agents to retrieve semantically relevant records and their relationships in one coherent query, enhancing accuracy by maintaining consistent data without the need for fusion heuristics, which often lead to inaccuracies and latency. SurrealDB's capability to perform vector KNN searches and graph traversals within a single SurrealQL statement ensures that agents receive a transactional snapshot of data, thereby reducing the risk of stale or inconsistent information. This unified system improves retrieval quality through features such as graph-based corrections and authorization checks, and supports complex queries that require multi-hop traversals, providing a more efficient and accurate alternative to traditional two-store setups, especially for agent corpora that fit within SurrealDB's capacity.

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