This research introduces an innovative approach to simplify the process of converting natural language queries into SQL queries, utilizing advanced open-source large language models (LLMs). The proposed framework employs a robust Retrieval- Augmented Generation (RAG) methodology, incorporating prompt engineering and transfer learning techniques to accurately discern the identification of precise dataset and variables from an extensive array of variables spread across datasets.
The framework initiates the process by retrieving pertinent information to comprehensively grasp the user’s natural language query. Subsequently, it enhances the prompt to interpret various variables, operations, and criteria intended by the user. The retrieved information is then aggregated to identify the optimal datasets and variables for generating the SQL query based on the matched query context. Finally, the system generates the SQL query, ensuring both syntactic and semantic correctness.
Navigating the intricacies stemming from datasets with diverse structures, variables that share similar names across different datasets, and a myriad of real- world queries presents a formidable challenge. Furthermore, the framework demonstrates flexibility by seamlessly handling datasets with varying time periods and fostering continued conversations, allowing users to pose multiple follow-up questions. This adaptability establishes a versatile solution for querying databases with agility, comprehending the user’s specific analytical needs and insights.
The efficacy of this approach in accurately translating natural language queries into SQL statements is confirmed through rigorous validation via extensive experiments and case studies. The framework’s scalability and adaptability enhance its value as a valuable tool for organizations managing complex databases, fostering more intuitive and efficient interactions with data.
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