A case study using sewage metagenomic data for assessment of text-to-SQL capabilities in large language models.

ABSTRACT: Relational databases offer an efficient solution for storing and retrieving complex data sets, yet the requirement for SQL programming expertise presents a significant challenge for many life science users. We explore whether a cutting-edge large language model can effectively translate plain English queries into SQL scripts (Text-to-SQL), thereby simplifying database interaction and eliminating the typical usage barriers. A complex database comprising 19 interconnected tables of metagenomic analyses from 239 sewage samples across five European cities was available. A large language model was provided with details of the database's structure and background information on its contents. We evaluated the functionalities of this "SewageGPT" tool and assessed the accuracy of its responses to co

SUBMITTER: Becsei A 

PROVIDER: S-EPMC12738697 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature