Enhancing Database Accessibility : A Llama-2 Powered Interface For Intelligent Database Querying

Author(s)	Ms. NEELAM PRAMOD PAWAR, Ms. ISHA ASHOK WAGHULDE, Mr. SOHAM SANTOSH NIMBALKAR, Mr. PRATHAMESH SHRIKANT SURYAWANSHI, Dr. MANISH - SHARMA
Country	India
Abstract	This research work introduces an AI-powered natural language to SQL (NL2SQL) interface to make database querying easy for end-users. Our system, which uses a fine-tuned LLaMA-2 model, generates SQL queries dynamically, with better adaptability and accuracy than rule-based systems. Based on a curated dataset from Hugging Face’s synthetic text-to-SQL corpus, our system maintains robust performance on various query structures. Implemented using Python, PyTorch, and Flask, and deployed using Gunicorn and Tensor Dock, the system exhibits high accuracy in converting natural language to executable SQL. Experimental results demonstrate improvements in query precision, execution efficiency, and deployment scalability. Future development will aim to improve contextual comprehension and real-time query performance.
Keywords	Natural Language to SQL (NL2SQL), LLaMA-2, Database Querying, AI-driven SQL Generation, Deep Learning, Text-to-SQL, Query Optimization
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-16
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.44909

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