Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Tasks
By: Yihan Wang , Peiyu Liu , Runyu Chen and more
Potential Business Impact:
Lets computers answer questions from any data.
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.
Similar Papers
SQuARE: Structured Query & Adaptive Retrieval Engine For Tabular Formats
Computation and Language
Helps computers answer questions from messy spreadsheets.
SING-SQL: A Synthetic Data Generation Framework for In-Domain Text-to-SQL Translation
Artificial Intelligence
Lets computers understand any database questions.
DeepEye-SQL: A Software-Engineering-Inspired Text-to-SQL Framework
Databases
Makes computers understand questions to find data.