Text-to-SQL is a type of artificial intelligence technology that automatically converts natural language questions into structured query language (SQL), aiming to enable users to directly access information in relational databases through natural language without manually writing SQL query statements. This technology combines fields such as natural language processing, semantic parsing, and database management, and is an important direction for achieving natural language database interaction. The development of text-to-SQL is built upon the foundations of Natural Language Interface to Databases (NLIDB), semantic parsing, and machine learning methods. Early research primarily relied on manually designed rules and templates to map fixed-form questions to database queries, but its generalization ability was limited when dealing with complex statements, different database structures, and cross-domain tasks. With the development of deep learning and large-scale datasets, researchers began to use neural network models to learn the mapping relationships between natural language and database structures.

In 2018, researchers at Yale University proposed the paper Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task , which constructed a large-scale Text-to-SQL benchmark dataset containing multi-domain databases and complex natural language queries, advancing research on cross-database generalization capabilities and becoming one of the important representative research works in the text-to-SQL field.

Modern Text-to-SQL systems typically need to complete multiple steps, including natural language understanding, database schema matching, SQL generation, and query result validation. Models not only need to understand the semantics in user questions, but also need to identify the tables, fields, relationships, and constraints in the database, thereby generating SQL queries that conform to the database structure requirements. In recent years, with the development of large language models (LLMs), Text-to-SQL methods based on prompting, retrieval-augmented generation (RAG), tool calling, and agents have further improved the ability to handle complex queries. Text-to-SQL mainly addresses the information access barrier between ordinary users and databases, enabling non-professionals to complete data querying and analysis through natural language. Currently, this technology has been widely applied in scenarios such as business intelligence (BI), enterprise data analysis, automated report generation, intelligent customer service, and data assistants, making it an important technical direction for natural language-driven data analysis.