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# Chatbot with Chat history stored in Database
This project is a simple chatbot application built using Python, integrating a database for chat history storage and a language model service to generate responses. The chatbot can handle user messages, manage chat history, and terminate conversations upon receiving a `/stop` command.
# Chatbot with LLM Integration and Database Storage
This chatbot application integrates LLM (Large Language Model) API services, **Together** and **Groq**(you can use any one of them), to generate AI-driven responses. It stores conversation history in a MySQL database and manages chat sessions with triggers that update the status of conversations automatically.
## Features ## Features
- **Conversation Handling**: The bot processes user inputs and generates responses using a language model service. - Supports LLM response generation using **Together** and **Groq** APIs.
- **Database Integration**: Stores chat data (user messages and bot responses) and maintains chat history. - Stores chat sessions and message exchanges in MySQL database tables.
- **Session Management**: Supports starting and terminating chat sessions, including proper logging of start and end times. - Automatically updates chat session status using database triggers.
- **Message Truncation**: Limits conversation history to the last few messages if the conversation exceeds a large number of entries. - Manages conversation history with user-assistant interaction.
## Components ## Requirements
- **`Chatbot` Class**: Core logic for handling user messages and managing the chat lifecycle.
- **`Database` (Mocked in tests)**: Handles chat data storage (methods for inserting and retrieving data).
- **`LLM Service` (Mocked in tests)**: Generates responses to user input based on conversation history.
## Installation Before running the application, ensure the following dependencies are installed:
1. Clone the repository:
2. Install the necessary dependencies - Python 3.13+
```bash - MySQL Server
pip3 install requirements.txt - The following Python libraries:
``` ```bash
4. Run the bot or test it using `doctest`: pip3 install -r requirements.txt
```bash ```
python3 -m doctest -v chatbot.py
``` ## Setup Instructions
### Step 1: Set Up Environment Variables
Create a `.env` file in the root directory of your project and add the following entries for your database credentials and API keys:
## Usage
1. **Create Database**: Create a databse named `ChatDB` in Mysql
2. **Create .env**:
``` ```
# Together API key # Together API key
TOGETHER_API_KEY="YOUR_API_KEY" TOGETHER_API_KEY="YOUR_API_KEY"
# Groq API key # Groq API key
GROQ_API_KEY = "YOUR_API_KEY" GROQ_API_KEY = "YOUR_API_KEY"
# MySQL connectionDB (if you're running locally) # MySQL connectionDB (if you're running locally)
DB_USER = "<DB_USER_NAME>" DB_USER = "<DB_USER_NAME>"
DB_PASSWORD = "<DB_USER_NAME>" DB_PASSWORD = "<DB_USER_NAME>"
DB_HOST = "127.0.0.1" DB_HOST = "127.0.0.1"
DB_NAME = "ChatDB" DB_NAME = "ChatDB"
PORT = "3306" PORT = "3306"
```
7. **Handling Messages**: run below command to start the chat in console, you can login to your Database to check the chat history # API service to you(or use "Together")
```python API_SERVICE = "Groq"
python3 main.py
``` ```
10. **Ending the Chat**: When the user sends `/stop`, the chat will terminate and log the end of the conversation with the message 'conversation-terminated'
## Testing ### Step 2: Create MySQL Tables and Trigger
The code includes basic `doctests` to verify the chatbot's functionality using mock services for the database and language model:
- Run the tests: The `create_tables()` function in the script automatically creates the necessary tables and a trigger for updating chat session statuses. To ensure the database is set up correctly, the function is called at the beginning of the script.
```bash
python3 -m doctest -v chatbot.py Ensure that your MySQL server is running and accessible before running the code.
```
### Step 3: Run the Application
To start the chatbot:
1. Ensure your MySQL server is running.
2. Open a terminal and run the Python script:
```bash
python3 chat_db.py
```
The chatbot will initialize, and you can interact with it by typing your inputs. Type `/stop` to end the conversation.
### Step 4: Test and Validate Code
This project uses doctests to ensure that the functions work as expected. To run the doctests:
```bash
python3 -m doctest -v chatbot.py
```
Make sure to add doctests to all your functions where applicable, to validate both valid and erroneous inputs.
### Key Functions
- **create_tables()**: Sets up the MySQL tables (`Chat_history` and `Chat_data`) and the `update_is_stream` trigger.
- **insert_chat_history()**: Inserts a new chat session into the `Chat_history` table.
- **insert_chat_data()**: Inserts user-assistant message pairs into the `Chat_data` table.
- **generate_llm_response()**: Generates a response from the selected LLM API service, either **Together** or **Groq**.