Data Analysis Using Large Language Models Through Natural Language Querying

Authors: Sai Manish Soma, Nithyananda Reddy Thalla, Harshavardhan Reddy Koppurapu, Tanishq Duddi, Chandra Sekhar Reddy V

DOI: https://doi.org/10.37082/IJIRMPS.v13.i3.232555

Short DOI: https://doi.org/g9pgvn

Country: India

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Abstract: Introducing the Data Analysis Web Application, a cutting-edge, full-stack platform meticulously engineered to revolutionize how users interact with their data by seamlessly integrating the power of Large Language Models (LLMs). This innovative application stands out by empowering users to query, analyze, and visualize complex datasets directly from a local database using intuitive, natural language inputs rather than complex code or commands. One of the primary goals of this application is to dramatically lower the barrier to entry for data exploration. By enabling users to simply ask questions in plain English or describe the analysis they need, the necessity for advanced technical skills, such as SQL programming or scripting, is significantly reduced. This approach effectively democratizes access to data-driven insights for a much wider audience within any organization. The backend serves as the sophisticated engine driving this capability. It strategically utilizes DSPy as a framework to effectively orchestrate complex interactions with the integrated LLMs. These powerful models are leveraged for critical tasks, including translating diverse natural language requests into precise SQL queries executable against the database, performing detailed trend analysis directly on the data, and interpreting intricate data patterns to synthesize clear, understandable, and actionable insights. Connectivity to the local PostgreSQL database is handled efficiently and reliably via Psycopg2, ensuring real-time data access essential for dynamic analysis and quick turnaround on queries. On the user-facing side, the application is built using the modern Streamlit framework, providing an interactive and highly user-friendly interface. This frontend design makes the process of exploring data, visualizing findings, and interacting with the analytical outputs generated by the LLMs remarkably seamless and efficient, allowing users to focus purely on understanding their data and its implications. Ultimately, by combining robust modern web development principles with the transformative capabilities of LLMs and a reliable local database setup (serving as a foundational proof of concept), this application fundamentally transforms data interaction. It equips teams and individuals with the tools needed to uncover valuable insights quickly and effectively, fostering a truly data-driven environment without the traditional technical overhead.

Keywords: Large Language Model, Structured Query Language, Database Management System, Artificial Intelligence, Machine Learning, Text to SQL, Natural Language Query.

Paper Id: 232555

Published On: 2025-06-07

Published In: Volume 13, Issue 3, May-June 2025

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