AI POWERED TRANSLATOR: TRANSFORMING NATURAL LANGUAGE TO DATABASE QUERIES
G S N Murthy, K Anshu, T. Srilakshmi, Chiraag Sumanth, Mummadi Mounika · International Journal of Engineering Applied Sciences and Technology · 2025
In the modern digital landscape, interacting with databases often requires structured query languages SQL or NoSQL syntax, which can be a barrier for non-technical users. This project introduces an intelligent AI-powered system that seamlessly converts natural language questions into executable database queries, bridging the gap between human communication and database management. Leveraging advanced natural language processing (NLP) models, large language models (LLMs), the system dynamically interprets user queries and translates them into optimized SQL or NoSQL commands. Our approach involves extracting schema details from both relational (MySQL) and non-relational (MongoDB) databases to generate a contextualized query prompt for AI processing. The model ensures precise query formation while adhering to database constraints and syntax rules. Additionally, security measures, including authentication and input validation, are integrated to prevent SQL injection and unauthorized access. This AI-driven solution enhances accessibility, enabling users to retrieve, insert, update, and delete data without technical expertise. It finds applications in data analytics, customer support, and business intelligence, streamlining database interactions and improving user efficiency.