Agentic retrieval for structured data with text-to-surql

Agentic retrieval for structured data using text-to-SurrealQL provides a solution for AI agents to effectively manage and query structured data within databases by generating precise SurrealQL queries. This method improves upon traditional retrieval augmented generation (RAG) pipelines, which focus on unstructured data, by allowing agents to handle complex data relationships and aggregations in a single query using SurrealQL, SurrealDB's multi-model query language. By offering a more sophisticated way to handle structured data, agents can answer questions accurately, with the additional benefit of auditability and secured data access through built-in permissions. This approach empowers AI agents to deliver precise, traceable, and secure answers from structured data, making it a significant advancement over traditional vector-based retrieval methods.