https://github.com/mscbuild/taskbot
🤖 TaskBot: A Telegram Task Management Bot (router -> analysis -> processing -> response)
https://github.com/mscbuild/taskbot
backend bot control messages python response service task telegram
Last synced: 11 months ago
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🤖 TaskBot: A Telegram Task Management Bot (router -> analysis -> processing -> response)
- Host: GitHub
- URL: https://github.com/mscbuild/taskbot
- Owner: mscbuild
- License: mit
- Created: 2025-07-10T08:55:10.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-07-10T10:06:44.000Z (11 months ago)
- Last Synced: 2025-07-10T17:14:42.843Z (11 months ago)
- Topics: backend, bot, control, messages, python, response, service, task, telegram
- Language: Python
- Homepage:
- Size: 22.5 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TaskBot: A Telegram Task Management Bot
## Overview
TaskBot is a Telegram bot built with Python 3.12, aiogram, SQLAlchemy, and LangChain. It allows users to manage tasks (create, read, update, delete, and list) using natural language, with LLM-driven intent analysis. The architecture follows a layered approach (router -> analysis -> processing -> response) with strict CRUD separation, memory-layer abstraction, and access control.
## Features
- Create, read, update, delete, and list tasks via Telegram commands or natural language.
- LLM-driven intent analysis using LangChain for processing user input.
- Strict CRUD operations with SQLAlchemy for database interactions.
- User-specific access control to ensure data privacy.
- Type-safe code with Pydantic models and Python 3.12 type hints.
- Asynchronous operations for scalability using aiogram and async SQLAlchemy.
- Minimal unit tests with pytest for core functionality.
## Project Structure
```
taskbot/
├── bot.py # Main bot code (router, analysis, processing, repository, tests)
├── README.md # Project documentation
└── requirements.txt # Project dependencies
```
- **Router**: Handles Telegram messages using aiogram.
- **Analysis**: Uses LangChain to parse user intent (e.g., "create task: buy groceries").
- **Processing**: Executes business logic with strict separation of concerns.
- **Memory Layer**: Abstracts database operations using SQLAlchemy.
- **Access Control**: Ensures users can only access their own tasks.
## Requirements
- Python 3.12+
- Telegram Bot Token (obtain from [BotFather](https://t.me/BotFather))
- Dependencies (listed in `requirements.txt`):
- aiogram
- sqlalchemy[asyncio]
- langchain
- langchain-community
- pydantic
- pytest
- pytest-asyncio
## Setup
1. **Clone the Repository**:
```bash
git clone https://github.com/mscbuild/TaskBot
cd taskbot
```
2. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
3. **Configure the Bot**:
- Replace `"YOUR_BOT_TOKEN"` in `bot.py` with your Telegram bot token.
- (Optional) Replace the `FakeListLLM` in `bot.py` with a real LLM (e.g., OpenAI or HuggingFace) for production use. Configure API keys as needed.
4. **Set Up the Database**:
- The bot uses an in-memory SQLite database by default (`sqlite+aiosqlite:///:memory:`).
- For production, update the database URL in `bot.py` to use a persistent database (e.g., PostgreSQL: `postgresql+asyncpg://user:password@localhost/dbname`).
5. **Run the Bot**:
```bash
python bot.py
```
## Usage
1. Start the bot by sending `/start` in Telegram.
2. Send natural language commands, e.g.:
- "Create a task: Buy groceries"
- "List my tasks"
- "Update task 1: Buy groceries and milk"
- "Delete task 1"
- "Show task 1"
3. The bot will respond with the result of the action (e.g., "Task created: Buy groceries (ID: 1)").
## Testing
Run unit tests to verify CRUD operations:
```bash
pytest bot.py
```
The tests cover:
- Creating a task
- Reading a task
- Updating a task
- Listing tasks
- Deleting a task
## Extending the Bot
- **Custom LLM**: Replace `FakeListLLM` with a production-ready LLM (e.g., `langchain_openai.OpenAI`) and configure API keys.
- **Database**: Switch to a persistent database like PostgreSQL for production.
- **Additional Features**: Extend the `TaskAnalyzer` to support more complex intents or add new commands in `TaskBot`.
## Notes
- The bot uses an in-memory database for simplicity. For production, use a persistent database to retain data.
- Ensure your Telegram bot token is kept secure and not exposed in version control.
- The `FakeListLLM` is used for testing. For real-world use, integrate a proper LLM and handle rate limits or errors.
## License
MIT License