Initial release

This commit is contained in:
Alessandro Santini
2025-02-02 10:21:16 +01:00
commit 96ba1dfa1b
9 changed files with 696 additions and 0 deletions

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.github/workflows/docker-publish.yml vendored Normal file
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name: Docker Build and Publish
on:
push:
branches: [ "main" ]
tags: [ 'v*.*.*' ]
pull_request:
branches: [ "main" ]
env:
REGISTRY: ghcr.io
IMAGE_NAME: ${{ github.repository }}
jobs:
build:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Log in to the Container registry
uses: docker/login-action@v3
with:
registry: ${{ env.REGISTRY }}
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Extract metadata (tags, labels) for Docker
id: meta
uses: docker/metadata-action@v5
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
tags: |
type=ref,event=branch
type=ref,event=pr
type=semver,pattern={{version}}
type=semver,pattern={{major}}.{{minor}}
type=sha
- name: Build and push Docker image
uses: docker/build-push-action@v5
with:
context: .
push: ${{ github.event_name != 'pull_request' }}
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}

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.env

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Dockerfile Normal file
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FROM python:3.13-rc-slim
# Set working directory
WORKDIR /app
# Install Poetry
RUN pip install poetry
# Copy project files
COPY pyproject.toml poetry.lock ./
COPY main.py .
COPY .env .
# Configure Poetry to not create a virtual environment
RUN poetry config virtualenvs.create false
# Install dependencies
RUN poetry install --no-dev --no-interaction --no-ansi
# Run the bot
CMD ["python", "main.py"]

3
Makefile Normal file
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all:
docker compose up -d
poetry run python main.py

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README.md Normal file
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# Flowise Telegram Bot
A Telegram bot that integrates with Flowise AI to provide conversational AI capabilities. This bot allows users to interact with your Flowise chatflows directly through Telegram.
## Features
- Seamless integration with Flowise AI
- Easy-to-use Telegram interface
- Support for custom chatflows
- Environment-based configuration
- Docker support for easy deployment
- Conversational memory support
## Prerequisites
- Python 3.13+
- Docker and Docker Compose
- Poetry for Python dependency management
- A Telegram Bot Token (obtained from [@BotFather](https://t.me/botfather))
- A running Flowise instance with API access
## Dependencies
- python-telegram-bot (>=21.10,<22.0)
- flowise (>=1.0.4,<2.0.0)
- python-dotenv (>=1.0.1,<2.0.0)
## Installation
1. Clone the repository:
```bash
git clone <repository-url>
cd flowise-telegram
```
2. Install dependencies using Poetry:
```bash
poetry install
```
3. Create a `.env` file in the root directory with the following variables:
```env
TELEGRAM_API_KEY=your_telegram_bot_token
FLOWISE_API_KEY=your_flowise_api_key
FLOWISE_API_URL=your_flowise_url
FLOWISE_CHATFLOW_ID=your_chatflow_id
```
## Configuration
### Environment Variables
- `TELEGRAM_API_KEY`: Your Telegram bot token from BotFather
- `FLOWISE_API_KEY`: API key from your Flowise instance
- `FLOWISE_API_URL`: URL of your Flowise instance (e.g., http://localhost:3000)
- `FLOWISE_CHATFLOW_ID`: ID of the chatflow you want to use (found in the Flowise dashboard URL when editing a flow)
### Docker Configuration
The project includes both a `docker-compose.yml` file for running Flowise and a `Dockerfile` for containerizing the bot.
To start Flowise:
```bash
docker compose up -d
```
#### Running with Local Build
To build and run the bot locally with Docker:
```bash
# Build the Docker image
docker build -t flowise-telegram-bot .
# Run the container
docker run --env-file .env --network host flowise-telegram-bot
```
#### Running with Pre-built Image
You can also use the pre-built image from GitHub Container Registry:
```bash
docker run --env-file .env --network host ghcr.io/OWNER/flowise-telegram:latest
```
Replace `OWNER` with your GitHub username.
Note: We use `--network host` to allow the bot container to communicate with Flowise running on localhost.
### CI/CD
The project includes a GitHub Actions workflow that automatically:
- Builds the Docker image
- Pushes it to GitHub Container Registry (ghcr.io)
- Tags images based on:
- Git branches
- Git tags (v*.*.*)
- Commit SHA
The workflow runs on:
- Push to main branch
- Pull requests to main branch
- New version tags
### Flowise Chatflow Setup
1. Access Flowise at http://localhost:3000 after starting Docker
2. Create a new chatflow
3. Set up the following nodes:
- **Chat Trigger**: Entry point for the conversation
- **LLM (Language Model)**: Choose your preferred model (e.g., OpenAI, Anthropic)
- **Conversation Memory**: Add memory to maintain context
- **Agent**: Configure the agent with appropriate tools and settings
- **Output Parser**: Format the response for Telegram
4. Configure the Agent node:
- Set "Agent Type" to "Conversational"
- Add system message to define bot behavior
- Connect memory to maintain conversation context
- Add relevant tools if needed
5. Enable API access for your chatflow:
- Click on the "Chat Trigger" node
- Enable "API Endpoint"
- Save the chatflow
6. Copy the chatflow ID from the URL when editing the flow
7. Configure your API key in Flowise settings
Example System Message for Agent:
```
You are a helpful AI assistant integrated with Telegram. Your responses should be:
1. Concise and clear
2. Formatted appropriately for Telegram
3. Helpful and informative
4. Natural and conversational
Always maintain a friendly and professional tone. If you don't know something, say so directly.
```
## Running the Bot
1. Start the bot using Python:
```bash
python main.py
```
2. Start a conversation with your bot on Telegram
3. Send a message to test the integration
## Project Structure
```
flowise-telegram/
├── main.py # Main bot implementation
├── docker-compose.yml # Docker configuration
├── .env # Environment configuration
├── README.md # Project documentation
└── .gitignore # Git ignore rules
```
## Development
### Local Development
1. Start Flowise using Docker:
```bash
docker compose up -d
```
2. Run the bot in development mode:
```bash
python main.py
```
### Making Changes
1. Update the code in `main.py`
2. Test your changes locally
3. Commit changes with descriptive messages
4. Push to your repository
## Troubleshooting
### Common Issues
1. **Bot not responding**
- Check if your Telegram token is correct
- Verify the bot is running
- Check Python logs for errors
2. **Flowise connection errors**
- Ensure Flowise container is running (`docker ps`)
- Verify your Flowise URL and API key
- Check if Flowise is accessible in your browser
3. **Missing responses**
- Verify your chatflow ID is correct
- Ensure the chatflow is properly configured in Flowise
- Check if the chatflow works in Flowise UI
4. **Memory Issues**
- Verify the Conversation Memory node is properly connected
- Check memory configuration in the Agent node
- Test the chatflow in Flowise UI to verify memory retention
### Logs
To view logs:
- Bot logs: Run the bot directly to see output
- Flowise logs: `docker compose logs -f flowise`
## Contributing
1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.

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docker-compose.yml Normal file
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services:
flowise:
image: flowiseai/flowise:2.2.4
ports:
- 3000:3000

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main.py Normal file
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import os
from dotenv import load_dotenv
from flowise import Flowise, PredictionData
from telegram import Update
from telegram.ext import Application, CommandHandler, ContextTypes, MessageHandler, filters
global flowiseClient
global chatflowId
async def start(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
"""Send a message when the command /start is issued."""
user = update.effective_user
await update.message.reply_markdown_v2(
fr"Hi {user.mention_markdown_v2()}\!",
)
async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
"""Handle a message."""
response = flowiseClient.create_prediction(PredictionData(question=update.message.text, chatflowId=chatflowId, streaming=False))
# Extract text from the response
response_text = ""
for item in response:
print(item)
response_text += item["text"]
await update.message.reply_text(response_text)
if __name__ == "__main__":
# Load environment variables
load_dotenv()
# Setup the Flowise client
FLOWISE_API_KEY = os.getenv("FLOWISE_API_KEY")
FLOWISE_API_URL = os.getenv("FLOWISE_API_URL")
chatflowId = os.getenv("FLOWISE_CHATFLOW_ID")
if not FLOWISE_API_KEY:
raise ValueError("FLOWISE_API_KEY not found in environment variables")
if not FLOWISE_API_URL:
raise ValueError("FLOWISE_API_URL not found in environment variables")
if not chatflowId:
raise ValueError("FLOWISE_CHATFLOW_ID not found in environment variables")
flowiseClient = Flowise(base_url=FLOWISE_API_URL, api_key=FLOWISE_API_KEY)
# Setup the Telegram bot
TELEGRAM_API_KEY = os.getenv("TELEGRAM_API_KEY")
if not TELEGRAM_API_KEY:
raise ValueError("TELEGRAM_API_KEY not found in environment variables")
application = Application.builder().token(TELEGRAM_API_KEY).build()
application.add_handler(MessageHandler(filters.TEXT, handle_message))
application.add_handler(CommandHandler("start", start))
# Run the bot
application.run_polling()

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19
pyproject.toml Normal file
View File

@@ -0,0 +1,19 @@
[project]
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version = "0.1.0"
description = ""
authors = [
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readme = "README.md"
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