AI-SQL is an intuitive, beginner-friendly tool that merges the power of Artificial Intelligence with SQL database queries. Developed as my 12th-grade Computer Science practical project, AI-SQL allows users to input natural language questions, which are intelligently converted into SQL commands for seamless database interaction.
- Accepts natural language input (e.g., "Show me all students with marks above 90")
- Automatically generates accurate SQL syntax
- Converts it into correct SQL syntax
- Displays results in a clean and readable format
- Simple Python + MySQL setup (perfect for students)
- Supports multiple query types, including SELECT, WHERE, ORDER BY, and aggregation functions like COUNT & AVG
- Handles common SQL errors gracefully with helpful messages
- Easy-to-use Python + MySQL setup, ideal for students and beginners
- Can be extended for larger datasets or more complex queries
- Educational tool for learning SQL and AI-powered query translation
AI-SQL is designed to demystify database interactions for beginners by bridging the gap between human language and structured SQL queries. It helps students and learners to:
Understand SQL syntax and logic without prior in-depth knowledge
Experiment with real-world queries in a safe and guided environment
Gain exposure to AI-powered natural language processing
Learn how AI can simplify complex database tasks
Explore the integration of AI with databases for practical applications
This project demonstrates the potential of combining AI and database management, providing a foundation for future projects involving data analytics, intelligent query systems, and AI-assisted database tools
🛠️ Built using:
- Python – Core logic, input handling, and AI integration
- MySQL – Backend database management and query execution
- Natural Language Processing (NLP) – Converts human-readable queries into SQL commands
- AI Concepts – Basic transformer or rule-based logic for understanding intent
- Command-Line Interface (CLI) – Simple interface for user input and output display
- Error Handling Mechanisms – Ensures smooth operation and clear guidance for incorrect queries
- Extensible Architecture – Can be scaled for more complex datasets, GUI integration, or web deployment
Shashank Somwanshi — 12th Grade CBSE | Passionate about code, logic, and real-world problem solving.
Shreyash Srivastava — 12th Grade CBSE| Fueled by coffee and code.|Building projects, learning daily, contributing to open source.|
GitHub: @KnotXaadi | Twitter: @KnotXaadi
GitHub: @ShreyashS1107|Linkedin:[Shreyash Srivastava](www.linkedin.com/in/ shreyash-srivastava-956350380)
🎓 Submitted as part of CBSE Class 12 Computer Science Practical Project – 2025.