README.md

Chatbot Agent with SQL and Gemini Integration

A powerful chatbot agent that integrates Google's Gemini language model with SQL database connectivity, enabling natural language to SQL query conversion and data visualization.

🌟 Features

• Natural Language to SQL: Convert natural language questions into SQL queries
• Database Integration: Connect to MySQL databases seamlessly
• Interactive Chat Interface: User-friendly Gradio-based web interface
• Data Visualization: Generate visualizations from query results
• Environment Configuration: Easy setup with environment variables

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• MySQL database (or compatible database)
• Google API key for Gemini

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/chatbot-agent-sql-gemini.git
   cd chatbot-agent-sql-gemini
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Create a .env file in the project root with your configuration:

   DB_USER=your_db_username
   DB_PASSWORD=your_db_password
   DB_HOST=your_db_host
   DB_NAME=your_database_name
   GOOGLE_API_KEY=your_google_api_key
   

Running the Application

1. Start the application:

   python app.py
   

2. Open your web browser and navigate to http://localhost:7860

🛠️ Configuration

The application can be configured using the following environment variables:

Variable	Description	Required
DB_USER	Database username	✅
DB_PASSWORD	Database password	✅
DB_HOST	Database host	✅
DB_NAME	Database name	✅
GOOGLE_API_KEY	Google API key for Gemini	✅

📦 Dependencies

• gradio >= 3.0.0
• langchain >= 0.1.0
• langchain-community >= 0.0.10
• langchain-google-genai >= 0.1.0
• langgraph >= 0.0.0
• matplotlib >= 3.7.0
• pandas >= 2.0.0
• sqlalchemy >= 2.0.0
• python-dotenv >= 1.0.0

🤖 How It Works

1. The application connects to your SQL database using the provided credentials
2. Users input natural language questions through the Gradio interface
3. The Gemini model converts these questions into SQL queries
4. Queries are executed against the database
5. Results are formatted and displayed to the user
6. For appropriate data, visualizations are automatically generated

📝 Example Queries

• "Show me the top 10 customers by total purchases"
• "What were our total sales last month?"
• "List all products with stock below minimum levels"
• "Generate a bar chart of monthly sales for the past year"

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Gradio for the web interface
• Google Gemini for the language model
• LangChain for the agent framework