🚀 SQL-Chat: Your AI-Powered Database Assistant

Chat with your SQL databases in plain English! SQL-Chat is an intuitive, powerful Streamlit application that leverages Large Language Models (LLMs) to translate natural language questions into SQL queries, execute them, and deliver answers in a human-friendly format. Analyze data, generate insights, and visualize results without writing a single line of SQL.

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✨ Key Features

• Natural Language to SQL: Ask complex questions about your data in everyday language.
• Multi-Database Support: Connect to SQLite, PostgreSQL, or MySQL databases. You can even upload your own SQLite file!
• AI-Powered by Groq & LangChain: Utilizes the speed of the Groq LPU™ Inference Engine and the power of LangChain for fast and accurate SQL generation.
• Interactive Chat Interface: A familiar chat-based UI for a seamless user experience.
• Data Visualization: Automatically generates charts and graphs from your query results.
• Query History & Favorites: Keep track of your past queries and save your favorites for quick access.
• Advanced Analytics: Get a quick overview of your database schema and statistics.
• Secure & Configurable: Manage your API keys and database credentials securely through the UI.

🛠️ Tech Stack

• Framework: Streamlit
• AI/LLM: Groq, LangChain
• Data Handling: Pandas
• Database Drivers: psycopg2-binary, mysql-connector-python
• Plotting: Plotly

⚙️ Getting Started

Follow these steps to get SQL-Chat running on your local machine.

1. Prerequisites

• Python 3.9+
• An account with Groq to get your free API key.

2. Installation

1. Clone the repository:

   git clone https://github.com/a-sh-dev/SQL-Chat.git
   cd SQL-Chat

2. Create a virtual environment and activate it: Using venv:

   python -m venv .venv
   source .venv/bin/activate

   Or using uv (a fast Python package installer):

   uv venv
   source .venv/bin/activate

3. Install the dependencies:

   pip install -r requirements.txt

   Or using uv:

   uv pip install -r requirements.txt

3. Configuration

No .env file needed! Simply run the application and configure everything from the sidebar UI:

• Enter your Groq API Key.
• Select your database type (Sample, Upload, MySQL, PostgreSQL).
• Enter your database credentials if connecting to a remote database.

▶️ Running the Application

Once the installation is complete, run the Streamlit app:

streamlit run app.py

Open your web browser and navigate to http://localhost:8501.

📖 How to Use

1. Configure your Database: Use the sidebar to connect to your desired database.
2. Set your API Key: Enter your Groq API key in the sidebar.
3. Start Chatting: Ask questions in the chat input box, like:
   • "How many students are there?"
   • "Show me the average GPA for each major."
   • "List all courses in the Computer Science department."
4. Explore: Use the tabs to view your query history, manage favorites, and see database analytics.

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License.

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This README was generated with the help of an AI assistant.