Text-to-SQL Intelligent Query Generator: Operate Databases with Natural Language

An AI application based on Flask and Google Gemini API that automatically converts natural language text into SQL queries, enabling non-technical users to interact with databases using everyday language.

• Original Author/Maintainer: Kusuma-49
• Source Platform: GitHub
• Original Title: Text-to-SQL-Query-Generator
• Original Link: https://github.com/Kusuma-49/Text-to-SQL-Query-Generator
• Publication Time: June 5, 2026

SQL is the standard language for accessing relational databases, but for users without technical backgrounds, the threshold to learn SQL syntax is relatively high. Text-to-SQL technology aims to break this barrier, allowing users to describe their data needs in natural language and letting AI automatically convert it into correct SQL queries.

This project is a Flask-based AI-driven application that uses Google's Gemini API to convert English sentences into SQL queries, providing a user-friendly database interaction interface for non-technical personnel.

This is a concise and practical web application with core features including:

• Receiving natural language queries input by users
• Calling Google Gemini API for intelligent conversion
• Generating accurate SQL query statements
• Displaying results via a web interface

The application uses the classic Flask web framework combined with modern generative AI technology to achieve automatic conversion from natural language to structured query language.

Text-to-SQL (also known as NL2SQL, Natural Language to SQL) is an important task in the field of Natural Language Processing (NLP), with the goal of converting data query needs described in human language into executable SQL statements.

For example:

• Natural Language: Find customers with sales exceeding 100,000 yuan in 2023
• SQL: SELECT customer_name FROM sales WHERE year=2023 AND amount > 100000

Text-to-SQL faces many challenges:

1. Semantic Understanding: Accurately understanding the user's query intent
2. Schema Alignment: Mapping entities in natural language to database tables and columns
3. Complex Queries: Handling complex operations such as multi-table joins, subqueries, and aggregations
4. Ambiguity Resolution: Resolving ambiguous expressions in natural language
5. Domain Adaptation: Adapting to database structures in different domains

Text-to-SQL technology has gone through several development stages:

Rule-based Methods (Early Stage)

• Using templates and rule matching
• Limited scope, difficult to handle complex queries
• Requires a lot of manual rule writing

Deep Learning-based Methods (Mid Stage)

• Using Sequence-to-Sequence (Seq2Seq) models
• Introducing attention mechanisms
• Achieved significant progress on standard datasets

Large Language Model-based Methods (Current)

• Leveraging pre-trained large models like GPT and Gemini
• Strong semantic understanding and generation capabilities
• Can adapt to new databases with zero or few samples
• Became the current mainstream solution

Flask is a lightweight Python web framework with the following advantages:

• Concise and flexible, easy to get started
• Rich extensions, mature ecosystem
• Suitable for rapid prototyping
• Convenient deployment

In this project, Flask is responsible for:

• Handling HTTP requests and responses
• Rendering web page templates
• Managing user sessions
• Interacting with the frontend