Chat2DB: Chatting to the Database with Interactive Agent Assisted Language Models

Cross-domain Text-to-SQL necessitates the capability of semantic parsers to generalize to unseen databases, thus simplifying the process of creating natural language interfaces for databases. The existing Text-to-SQL parser exhibits limitations in its adaptability to new databases, and its execution accuracy is not sufficient for building conversational applications, typically necessitating further fine-tuning for specific databases. In this paper, we introduce Chat2DB, a conversational system designed for database interactions that enhances parser capabilities, rendering them applicable in real-world contexts. Within Chat2DB, we implement an interactive schema-ranking agent that optimizes the performance of LMs-based parsers cost-effectively. We further propose an adaptive retraining stage to allow trained Text-to-SQL parsers to quickly adapt to the target database. Experimental evaluations were conducted to validate the performance of the key components of Chat2DB. In the demonstration, we showcase the interactive visualization interface of Chat2DB to achieve more accurate querying of databases by natural language.