  Text-to-SQL conversion is a critical innovation, simplifying the transition
from complex SQL to intuitive natural language queries, especially significant
given SQL's prevalence in the job market across various roles. The rise of
Large Language Models (LLMs) like GPT-3.5 and GPT-4 has greatly advanced this
field, offering improved natural language understanding and the ability to
generate nuanced SQL statements. However, the potential of open-source LLMs in
Text-to-SQL applications remains underexplored, with many frameworks failing to
leverage their full capabilities, particularly in handling complex database
queries and incorporating feedback for iterative refinement. Addressing these
limitations, this paper introduces SQLfuse, a robust system integrating
open-source LLMs with a suite of tools to enhance Text-to-SQL translation's
accuracy and usability. SQLfuse features four modules: schema mining, schema
linking, SQL generation, and a SQL critic module, to not only generate but also
continuously enhance SQL query quality. Demonstrated by its leading performance
on the Spider Leaderboard and deployment by Ant Group, SQLfuse showcases the
practical merits of open-source LLMs in diverse business contexts.
