Enhanced Database Interaction Using Large Language Models for Improved Data Retrieval and Analysis

One of the difficult task for many users on SQL is to write the SQL Query due to its syntax and structure. If a person needs to query a database, they should know everything about how data is distributed and what the internal dependencies are. For this reason, it is not easy for everyone to access data in database without proper knowledge. This paper presents a novel application that leverages generative AI and natural language processing (NLP) to enable users to interact with databases using natural language queries. Built on the Gemini API, the application translates user queries into SQL queries, simplifying database interactions for non-technical users. The Python-based backend, SQLite database management, and Streamlit frontend provide a comprehensive solution for database querying and analysis. This approach democratizes data retrieval and analysis, offering automated insights and visualizations to users of all skill levels. The app also features automated data analysis, which boosts insight generation for users of all skill levels. Further, the traditional ways of querying SQL generally require specialized knowledge and are only accessible to those with technical backgrounds. When the application takes charge of the query construction process and offers data as UI elements, it can invigorate users to have a higher degree of insight into what their data actually is and explore it even more efficiently. The Python software stack combines Python for backend processing, SQLite for database management and a web-based frontend for user interaction to provide an all-encompassing database querying and analysis solution.