SQL Translator

Abstract

1. Introduction

SQL Translator(SQL to Natural Language and Natural Language to SQL)

Welcome to the SQL and Natural Language Translator! This tool is designed to make it easy for anyone to translate SQL (Structured Query Language) commands into natural language and vice versa. SQL is a programming language used to manage and manipulate data in relational databases, and while it's a powerful tool, it can also be quite complex and difficult to understand. On the other hand, natural language is the language that we speak and write in everyday life, and it's often the preferred way to communicate for people who are not familiar with technical jargon.

With the SQL and Natural Language Translator, you don't need to be a SQL expert to understand what's going on in your database, or to write SQL queries. You can simply type in your query in natural language and get the corresponding SQL code, or type in your SQL code and get a human-readable translation.This project is 100% free and open source.

2. Objective

SQL Translator is a tool for converting natural language queries into SQL code using artificial intelligence. This project is 100% free and open source.

This project demonstrates how TypeScript, OpenAI API, PostgreSQL can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Dark mode
• Lowercase/uppercase toggle
• Copy to clipboard
• SQL syntax highlighting
• Schema awareness (beta)
• Query history

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Node.js (LTS) and npm / yarn / pnpm
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/whoiskatrin/sql-translator.git
cd sql-translator

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by whoiskatrin and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.