# TacoQ Python SDK

TacoQ Python SDK provides a simple and efficient way to create distributed task workers and publishers. This SDK allows you to build scalable task processing systems with minimal boilerplate.

## Installation

```bash
pip install tacoq
```

## Quick Start

The SDK provides two main components:

- **Worker**: Processes tasks
- **Publisher**: Submits tasks for processing

### Creating a Worker

Workers are created by defining task handlers and registering them with a `WorkerApplication`:

```python
from worker import WorkerApplication, WorkerApplicationConfig
from broker import BrokerConfig
from manager.config import ManagerConfig

# Configure the worker
config = WorkerApplicationConfig(
    name="my_worker",
    manager_config=ManagerConfig(url="http://localhost:3000"),
    broker_config=BrokerConfig(url="amqp://user:password@localhost:5672")
)

# Create application
app = WorkerApplication(config)

# Register task handlers
@app.task("my_task")
async def handle_task(input_data: dict) -> dict:
    return {"result": input_data["value"] * 2}
```

### Publishing Tasks

Tasks can be published using the `PublisherClient`:

```python
from publisher.client import PublisherClient
from manager.config import ManagerConfig

# Create publisher
publisher = PublisherClient(ManagerConfig(url="http://localhost:3000"))

# Publish task
task = await publisher.publish_task("my_task", {"value": 42})

# Check result
result = await publisher.get_task(task.id)
print(result)
```

## Running Workers

There are multiple ways to run your worker application:

### 1. Direct Execution

```python
if __name__ == "__main__":
    asyncio.run(app.entrypoint())
```

### 2. Using CLI

```bash
# Run worker in production mode
tacoq run "path.to.module:worker_application"

# Run worker in development mode with auto-reload
tacoq run "path.to.module:worker_application" --reload
```

## Complete Examples

Check out the `examples/` directory for complete working examples:

- `example_worker.py`: Demonstrates worker setup with multiple task handlers
- `example_producer.py`: Shows how to publish tasks and retrieve results

## Configuration

### Worker Configuration

- `name`: Unique identifier for the worker
- `manager_config`: Connection details for the TacoQ manager
- `broker_config`: Connection details for the message broker

### Manager Configuration

- `url`: URL of the TacoQ manager service

### Broker Configuration

- `url`: AMQP URL for the message broker

## Best Practices

1. Share task names between workers and publishers using constants
2. Handle errors appropriately in task handlers
3. Use development mode (`--reload`) for faster development iterations
4. Keep task handlers focused and modular

## License

MIT License - See LICENSE file for details
