NL2SQLX — Natural Language → SQL Query Generator

Convert plain English questions into SQL queries and run them on any uploaded CSV dataset.
Backend powered by FastAPI + MySQL + RAG.
Frontend built using React/Next.js.

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🚀 Features

🔹 Upload CSV → Auto-Create SQL Table

• Upload any .csv file
• Backend automatically creates a SQL table in MySQL
• Schema is extracted and piped into RAG

🔹 Ask Natural Language Questions

Examples:

• “Show total revenue by category”
• “List top 10 customers by spending”
• “Give count of employees in each department”

Backend returns:

• ✔ Generated SQL
• ✔ Executed result
• ✔ RAG-improved query understanding

🔹 Secure Session Cleanup

• Clicking Finish drops all temporary uploaded tables
• Keeps database clean for next user

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🏗️ Tech Stack

Backend

• Python 3.12
• FastAPI
• SQLAlchemy
• MySQL (AlwaysData / free hosting)
• OpenAI (or Groq) LLMs
• ChromaDB (RAG embeddings)

Frontend

• React / Next.js
• Axios
• Deployed on Vercel / Render

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📌 Project Structure

NL2SQLX/ ├── backend/ │ ├── app.py │ ├── db.py │ ├── rag.py │ ├── schema_extractor.py │ ├── nl2sql.py │ ├── requirements.txt ├── frontend/ │ ├── app/ │ ├── components/ │ ├── public/ │ ├── package.json ├── README.md

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🧪 Run Backend Locally

1️⃣ Create & activate venv

cd backend
python -m venv venv
venv/Scripts/activate   # Windows

🛠️ Run Backend Locally

2️⃣ Install requirements

pip install -r requirements.txt

###3️⃣ Create .env file inside backend

MYSQL_USER=root
MYSQL_PASSWORD=xxxx
MYSQL_HOST=localhost
MYSQL_PORT=3306
MYSQL_DB=mydb
OPENAI_API_KEY=your_key

###4️⃣ Run FastAPI uvicorn app:app --reload

###🎨 Run Frontend Locally (React/Next.js)
cd frontend
npm install
npm run dev

Frontend runs on:

http://localhost:3000

Backend runs on:

http://localhost:8000

Edit API_BASE inside frontend:

export const API_BASE = "http://localhost:8000";

###👨‍💻 About the Developer

Prathamesh Shivaji Salokhe B.Tech Computer Engineering (Final Year)