Add Chatbot Project with LLM Integration and MySQL Database Support

[Pritam3355]

This pull request introduces a chatbot application that interacts with users using an LLM (Large Language Model) service. The chatbot can store chat histories in a MySQL database and retrieve responses from various LLM services like Together and Groq. The project is modular and divided into separate components for easy maintenance, scalability, and future development.

### Features
- Connects with language models (LLMs) from services like Together and Groq to generate AI-driven conversations.
- Stores chat histories (both user and assistant messages) in a MySQL database.
- Automatically tracks the start and end of a conversation session.
- Modular design to separate database, chatbot logic, and LLM interaction.
- Efficient chat history handling (trimming older messages to improve performance during long conversations).

### Prerequisites
- Python 3.x
- MySQL
- pip (Python package manager)
- `dotenv` for environment variable management
- Together API key (if using Together LLM service)
- Groq API key (if using Groq LLM service)

### Installation
1. Clone the repository:
    ```bash
    git clone https://github.com/Hacktoberfest2024_avi_1/ChatDB-LLM-Assistant/chatbot_project.git
    cd chatbot_project

2. Install the required Python packages:

   pip3 install -r requirements.txt

3. Set up environment variables in a .env file:

   DB_USER=your_mysql_user
   DB_PASSWORD=your_mysql_password
   DB_HOST=your_mysql_host
   DB_NAME=your_database_name
   TOGETHER_API_KEY=your_together_api_key  # Only required if using Together API
   GROQ_API_KEY=your_groq_api_key  # Only required if using Groq API

4. Set up MySQL database and ensure it is running.

Usage

To start the chatbot, run:

python main.py

Project Structure

chatbot_project/
│
├── db.py               # Database handling logic (MySQL connection, data insertion, retrieval)
├── llm_service.py      # Logic for interacting with LLM services (Together, Groq)
├── chatbot.py          # Main chatbot logic (conversation flow management)
├── main.py             # Entry point for the chatbot application
├── .env                # Environment variables (API keys, DB credentials)
├── requirements.txt    # Python dependencies
└── README.md           # Project documentation