AI SQL Assistant (Groq + MySQL + Streamlit)

AI SQL Assistant is a full-stack AI-powered SQL generator and executor built with FastAPI, Groq LLM, and Streamlit.
It converts natural language queries into optimized MySQL commands, executes them directly, and provides human-readable explanations and visualizations of query results.

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Key Features

• Natural Language to SQL: Generate SQL queries using the Groq API (llama-3.3-70b-versatile)
• Query Execution: Run generated or manual SQL queries securely against a MySQL database
• Query Explanation: Translate complex SQL into plain English
• Database Explorer: Browse databases, tables, and columns interactively
• Optimization Tips: Get automatic indexing and performance suggestions
• Visualization: Display query results as interactive tables, pie charts, or bar graphs
• Query History: Re-run and review past SQL queries

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Tech Stack

Layer	Technology
Frontend	Streamlit
Backend API	FastAPI
LLM	Groq (Llama 3.3 70B Versatile)
Database	MySQL (via SQLAlchemy + mysql-connector-python)
Environment Management	python-dotenv
Logging	loguru / logging
Testing	pytest

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Project Structure

ai-sql-assistant/
│
├── app.py                 # FastAPI entry point – routes for SQL generation, execution & schema
├── ui.py                  # Streamlit UI – interactive chat, explorer, results & charts
├── query_generator.py     # LLM prompts, SQL cleaning, execution, explanation & optimisation tips
├── database.py            # DB connection (SQLAlchemy), schema extraction, list-databases/tables/columns
├── .env                   # Secrets – GROQ_API_KEY, MySQL credentials (never commit!)
├── requirements.txt       # Pip dependencies (FastAPI, Streamlit, Groq, SQLAlchemy, …)
└── README.md              # You are here!

Installation & Setup

1. Clone the Repository

git clone https://github.com/<your-username>/ai-sql-assistant.git
cd ai-sql-assistant

2. Create and Activate Virtual Environment

python -m venv venv
source venv/bin/activate       # Linux / macOS
venv\Scripts\activate          # Windows

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment Variables Create a .env file in the root directory:

GROQ_API_KEY=your_groq_api_key_here
MYSQL_HOST=127.0.0.1
MYSQL_USER=root
MYSQL_PASSWORD=root456
MYSQL_DATABASE=sakila
MYSQL_PORT=3306

Running the Application Step 1 — Start FastAPI Backend

uvicorn app:app --reload

Step 2 — Start Streamlit Frontend

streamlit run ui.py

1. Generate SQL from Natural Language2. Execute Query

Run the SQL directly from the Streamlit interface View results in an interactive table Visualize with charts (bar, pie, etc.)

3. Explain Query Click “Explain SQL Query” to get a plain English summary

License

MIT License Free for personal and educational use.

Contributing Contributions are welcome! Feel free to:

Open issues

Submit pull requests

Improve documentation