🐬🛢️MySQL RAG System Using meta-llama/Llama-4-Scout-17B-16E-Instruct

📘Overview

MySQL-RAG is an advanced SQL chatbot application that combines the power of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to enable natural language interaction with MySQL databases. Built with a Python Flask backend and utilizing the meta-llama/Llama-4-Scout-17B-16E-Instruct model, this chatbot allows users to analyze, visualize, and manage database records using conversational queries—eliminating the need to write SQL manually.

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✨ Key Features

• 🤖 LLM-Powered Chatbot:

  • 🧠 Uses the meta-llama/Llama-4-Scout-17B-16E-Instruct model for advanced SQL reasoning.
  • 💬 Converts natural language queries into SQL commands for data retrieval, analysis, and modification.

• 🐍 Flask Backend:

  • ⚙️ Lightweight and easy-to-deploy Python backend for API and chatbot logic.

• 🔍 RAG Architecture:

  • 🧩 Integrates retrieval mechanisms with generative AI for precise and context-aware responses.

• 🛠️ Database Operations:

  • 📊 Supports SELECT, UPDATE, DELETE, and other SQL operations through chat.

• 🚀 Easy Deployment:

  • ⚡ Ready for platforms like Vercel (see vercel.json for config).

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📁 Folder Structure

• .gitignore – Git ignore rules
• app.py – Main Flask application and API endpoints
• requirements.txt – Python dependencies
• test.py – Test scripts for chatbot/database functionality

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🛠️ Getting Started

1. Clone the Repository

git clone https://github.com/YUGESHKARAN/MySQL-RAG.git
cd MySQL-RAG

2. Install Dependencies

It is recommended to use a Python virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Configure Environment

• Set up your MySQL database and update the connection parameters in app.py.
• Configure access to the meta-llama/Llama-4-Scout-17B-16E-Instruct model as required (API key or local model path).

4. Run the Application

python app.py

The Flask application should now be running (default: http://127.0.0.1:5000/). You can interact with the chatbot via the provided API endpoints or connect with your frontend.

5. Testing

To run test scripts:

python test.py

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💬 Example Usage

• 🗣️ Natural Language Query:

  • "Show me all students with attendance below 75% in March."

• 📥 Chatbot Output:

  • Returns a table/list based on your database records.
  • Confirms update and shows the modified record.

• ✏️ Database Modification:

  • "Update attendance for John Doe to 85% in Mathematics."

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🧠 Model Details

• LLM Used: meta-llama/Llama-4-Scout-17B-16E-Instruct
• Integration: The LLM is used for both SQL generation from natural language and for contextual, conversational responses.

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🚀 Deployment

I have deployed the application on Render, and the MySQL database is hosted on AWS RDS.

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📊 Monitoring AI Performance

• Using LangSmith to monitor Product Agent logs, operations, token usage, costs, and responses.

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🤝 Contribution

Contributions are welcome! Submit issues or pull requests for improvements or bug fixes.

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📬 Contact

For questions or support, contact Yugeshkaran.