Text to SQL Query
Kartik Sharma · International Journal for Research in Applied Science and Engineering Technology · 2025
With the exponential growth of data in modern organizations, the ability to extract meaningful insights from databases has become crucial. However, interacting with structured databases often requires knowledge of SQL (Structured Query Language), which presents a barrier for non-technical users. To address this challenge, this project proposes a Text to SQL system that enables users to retrieve data from relational databases by simply expressing their queries in natural language. The goal is to bridge the gap between human language and machine-readable SQL commands through the use of Natural Language Processing (NLP) and machine learning techniques. The system is designed to accept a natural language input, process and understand its intent, and then convert it into an equivalent SQL query that can be executed on a target database. The core methodology involves text preprocessing, tokenization, semantic parsing, and SQL query generation. Modern NLP models, including transformer-based architectures, are explored to improve the understanding of context and the mapping between language and database schema.