Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Tasks
This addresses integration issues for developers and researchers in database and AI fields, though it appears incremental as it builds on existing methods.
The paper tackles the challenge of deploying diverse Text-to-SQL methods in real-world systems by introducing Squrve, a unified and modular framework that standardizes interfaces and uses multi-actor collaboration, which outperforms original individual methods on benchmarks.
Text-to-SQL technology has evolved rapidly, with diverse academic methods achieving impressive results. However, deploying these techniques in real-world systems remains challenging due to limited integration tools. Despite these advances, we introduce Squrve, a unified, modular, and extensive Text-to-SQL framework designed to bring together research advances and real-world applications. Squrve first establishes a universal execution paradigm that standardizes invocation interfaces, then proposes a multi-actor collaboration mechanism based on seven abstracted effective atomic actor components. Experiments on widely adopted benchmarks demonstrate that the collaborative workflows consistently outperform the original individual methods, thereby opening up a new effective avenue for tackling complex real-world queries. The codes are available at https://github.com/Satissss/Squrve.