irinamartynovar / universal-llm-chatbot

Universal LLM Telegram chatbot in Python

Universal LLM ChatBot

Universal LLM ChatBot is a versatile Telegram bot that leverages Large Language Models (LLMs) for natural language processing, voice transcription, and text-to-speech capabilities. It supports multiple languages and provides a customizable interface for interacting with AI models.

Features

• Multi-language support
• Voice message transcription
• Text-to-speech responses
• Customizable AI model settings
• User-specific configurations
• Rate limiting to prevent abuse
• Admin commands for user management

Prerequisites

• Python 3.8+
• Telegram Bot Token (obtain from BotFather)
• Ollama server running locally or remotely

Installation

1. Clone the repository:

   git clone https://github.com/KPEKEP/universal-llm-chatbot.git
   cd universal-llm-chatbot
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Copy the config_template.yml to config.yml and update it with your settings:

   cp config_template.yml config.yml
   

4. Edit config.yml to include your Telegram Bot Token and other configurations.

Configuration

The config.yml file contains all the necessary settings for the bot. Key configurations include:

• Telegram bot token
• Admin user IDs
• Access mode (public or whitelist)
• AI provider settings
• Rate limiting parameters

Refer to the comments in config_template.yml for detailed explanations of each setting.

Usage

To start the bot, run:

python main.py

The bot will now be active and respond to messages on Telegram.

Extending Beyond BasicProvider

The Universal LLM ChatBot is designed to be extensible. To create a new provider:

1. Create a new file in the bot/providers/ directory (e.g., custom_provider.py).
2. Implement a class that inherits from the Provider base class in bot/provider.py.
3. Override the abstract methods: generate_response, transcribe_voice, and text_to_speech.
4. Update the config.yml to use your new provider.

Example:

from bot.provider import Provider

class CustomProvider(Provider):
    def __init__(self, provider_name, config):
        super().__init__(provider_name, config)
        # Initialize your custom provider here

    async def generate_response(self, model, messages, options):
        # Implement your custom response generation logic

    async def transcribe_voice(self, input_filename):
        # Implement your custom voice transcription logic

    async def text_to_speech(self, text, output_filename, language="en", speaker=None):
        # Implement your custom text-to-speech logic

Ollama Setup

For detailed instructions on setting up Ollama for local inference, please refer to the Ollama documentation.

Acknowledgements

This project utilizes several open-source libraries and models:

• python-telegram-bot for Telegram integration
• Ollama for local LLM inference
• Whisper for voice transcription
• Coqui TTS for text-to-speech generation

Appreciation

If you use this project in your research or application, please give it a star

License

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