Generative ai-based algorithm for improved cache hit performance in text-to-sql systems

Abstract

Techniques are disclosed herein for optimizing text-to-SQL systems using generative models to identify structurally equivalent requests in a cache to improve natural language conversion to SQL queries. In one aspect, a method includes: receiving a data request from a user. An embedding model embeds the data request generating a request embedding vector that is used to search a cache comprising stored requests. A candidate request is identified that comprises a curated natural language utterance that is structurally equivalent to the natural language utterance in the data request. The data request and the candidate request are transmitted to a generative model, that determines if the two requests are structurally equivalent and generates an SQL instruction corresponding to the data request using the SQL instruction associated with the candidate request as a template. The SQL instruction, a query result, or both are provided to the user.