SQL-MARS:面向用户模糊需求的 Text2SQL 结构化数据推荐系统

With the maturity of LLM technology, natural language-based database interaction systems (e. g., Chat2DB, ChatExcel) have achieved wide application. However, existing systems generally rely on the "precise query" assumption and struggle to handle the ubiquitous ambiguous requirements in real-world scenarios, where users need to clarify their query needs during interaction with the system. To address this challenge, this paper proposes SQL-MARS(SQL-oriented Multi-Agent Recommender System), a multi-agent collaborative framework based on a "perception-action-evaluation" closed-loop mechanism for dynamic identification and adaptive processing of ambiguous database query requirements. The system introduces a three-layer metadata architecture to model user's requirements for ambiguous awareness. Based on this, it implements data navigation function, providing query recommendations at varying granularities based on users' ambiguous requirements to progressively guide them in clarifying their query needs. Additionally, the system proposes the fusion mechanism between external knowledge and local data to fully utilize valuable information from external sources. We also create the dataset named Bird-fuzzy for ambiguous requirements and implements automated evaluation. Experimental results show that SQL-MARS can effectively identify ambiguous requirements and guide users to clarify their data needs.