A Text-to-SQL System Based on ChatGPT
Keywords:
Text-to-SQL, Large Language Model, database, ChatGPTAbstract
This research presents the development and evaluation of a prototype system capable of accurately translating Thai natural language queries into SQL statements. The system leverages a Large Language Model (LLM) implemented as a customized GPT on the ChatGPT platform and connected to a MySQL database through a Web API. Users interact with the system through a conversational interface by submitting queries in natural language. The system then translates these queries into executable SQL statements, executes them against the database, and returns the results in tabular form. The system was evaluated using a dataset of 30 Thai natural language queries. The experimental results demonstrated that the proposed system achieved an average SQL generation accuracy of 90.00%, a query execution success rate of 100.00%, and an average response time of 0.0045 seconds per query. These findings indicate the potential of the system for practical deployment, particularly in scenarios where users need efficient access to structured data without requiring specialized knowledge of database query languages.
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