# AIMQ
AIMQ (AI Message Queue) is a robust message queue processor designed specifically for Supabase's pgmq integration. It provides a powerful framework for processing queued tasks with built-in support for AI-powered document processing and OCR capabilities.
## Features
- **Supabase pgmq Integration**: Seamlessly process messages from Supabase's message queue
- **Document OCR Processing**: Extract text from images using EasyOCR
- **Queue-based Processing**: Efficient handling of document processing tasks
- **AI-powered Analysis**: Leverage machine learning for advanced text analysis
- **Flexible Architecture**: Easy to extend with new processing tools and capabilities
## Installation
This project uses Poetry for dependency management. To get started:
```bash
# Install Poetry if you haven't already
curl -sSL https://install.python-poetry.org | python3 -
# Clone the repository
git clone <your-repo-url>
cd aimq
# Install dependencies
poetry install
```
## Configuration
1. Create a `.env` file in the project root:
```env
SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_key
QUEUE_NAME=your_queue_name
```
2. Configure your Supabase project with pgmq following the [official documentation](https://supabase.com/docs/guides/database/extensions/pgmq).
## Usage
1. Start the processor:
```bash
poetry run python -m aimq.processor
```
2. Process documents by adding messages to your Supabase queue:
```python
from aimq.core import QueueMessage
message = QueueMessage(
type="ocr",
payload={
"image_url": "https://example.com/document.jpg"
}
)
```
## Development
To contribute to AIMQ:
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests: `poetry run pytest`
5. Submit a pull request
## License
MIT License - see [LICENSE](LICENSE) for details.
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