SQL思維：具備引導式錯誤修正的多代理文本轉SQL系統

SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction

August 30, 2025

作者: Saumya Chaturvedi, Aman Chadha, Laurent Bindschaedler

cs.AI

摘要

將自然語言查詢轉換為SQL查詢是工業界和學術界共同面臨的關鍵挑戰，旨在提升對數據庫和大規模應用程式的訪問能力。本研究探討如何利用上下文學習和思維鏈來開發一個穩健的文本到SQL系統解決方案。我們提出了SQL思維鏈：一個多代理框架，將Text2SQL任務分解為模式鏈接、子問題識別、查詢計劃生成、SQL生成以及引導式修正循環。與以往僅依賴基於執行的靜態修正系統不同，我們引入了基於上下文學習的、由分類法引導的動態錯誤修正機制。SQL思維鏈在Spider數據集及其變體上取得了最先進的成果，結合了引導式錯誤分類與基於推理的查詢規劃。

English

Converting natural language queries into SQL queries is a crucial challenge in both industry and academia, aiming to increase access to databases and large-scale applications. This work examines how in-context learning and chain-of-thought can be utilized to develop a robust solution for text-to-SQL systems. We propose SQL-of-Thought: a multi-agent framework that decomposes the Text2SQL task into schema linking, subproblem identification, query plan generation, SQL generation, and a guided correction loop. Unlike prior systems that rely only on execution-based static correction, we introduce taxonomy-guided dynamic error modification informed by in-context learning. SQL-of-Thought achieves state-of-the-art results on the Spider dataset and its variants, combining guided error taxonomy with reasoning-based query planning.