qlofair 0.1.0
QLO-Fair: A Quota-Limited Orchestrated Fairness Algorithm for Distributed Task Orchestration
Requires Python: >=3.7

I'd be happy to update the README.md file with more comprehensive information about QLO-Fair. Here's an enhanced version:

```markdown
# QLO-Fair

QLO-Fair (Quota-Limited Orchestrated Fairness Algorithm) is a comprehensive framework for distributed task orchestration under strict sliding window quota constraints. Based on the paper by Gauthier Hubert, this library provides robust mechanisms for managing and enforcing quotas in distributed systems.

## Key Features

- **Sliding Window Quota Management**: Efficiently tracks operations within rolling time periods
- **Fair Task Distribution**: Balances load, quota headroom, and reliability for optimal worker selection
- **Stochastic Selection**: Prevents deterministic pathological behaviors through intelligent probabilistic selection
- **Quota Compliance Guarantees**: Ensures operations never exceed defined quota limits
- **Resilience to Worker Failures**: Gracefully handles worker failures and quota exhaustion
- **Integration with Popular Queue Systems**: Works with Celery and Temporal
- **Monitoring and Metrics**: Provides detailed insights into quota usage and system performance
- **Extensible Architecture**: Adapters for different platforms, customizable selection mechanisms

## Installation

```bash
# Basic installation
pip install qlofair

# With Celery support
pip install qlofair[celery]

# With Temporal support
pip install qlofair[temporal]

# With monitoring tools
pip install qlofair[monitoring]

# Full installation
pip install qlofair[all]
```

## Quick Start

### Using the CLI

QLO-Fair includes a command-line interface to help you get started:

```bash
# Create a template configuration file
qlofair create-config

# Generate a Celery configuration
qlofair create-celery --config qlofair_config.json --output celery_app.py

# Generate a Temporal configuration
qlofair create-temporal --config qlofair_config.json --output temporal_app.py

# Generate a dashboard configuration
qlofair create-dashboard --config qlofair_config.json --output dashboard_app.py
```

### Celery Integration

```python
from celery import Celery
from qlofair.adapters.celery.router import QLOFairRouter

# Initialize Celery app
app = Celery('myapp', broker='redis://localhost')

# Initialize QLO-Fair router
qlofair_router = QLOFairRouter(app)

# Register worker quotas
qlofair_router.register_worker_quota('worker1', 'API_CALLS', 100, 3600)  # 100 calls per hour
qlofair_router.register_worker_quota('worker1', 'DB_WRITES', 500, 3600)  # 500 writes per hour

# Configure Celery to use QLO-Fair router
app.conf.task_routes = qlofair_router.route_for_task

# Define tasks
@app.task
def api_heavy_task(data):
    """Task that makes many API calls"""
    # Task implementation
    return f"Processed API task: {data}"

# Register task quota requirements
qlofair_router.register_task_quotas(api_heavy_task, {
    'API_CALLS': 5,    # Task requires 5 API calls
    'DB_WRITES': 1     # Task requires 1 DB write
})
```

### Temporal Integration

```python
from temporalio import workflow, activity
from qlofair.adapters.temporal.task_queue_router import QLOFairTemporalRouter

# Create the QLO-Fair router
router = QLOFairTemporalRouter()

# Register task queues with dedicated workers
router.register_task_queue("api_worker_queue", "api_worker_1")
router.register_task_queue("db_worker_queue", "db_worker_1")

# Register worker quotas
router.register_worker_quota("api_worker_1", "API_CALLS", 100, 3600)  # 100 calls per hour
router.register_worker_quota("api_worker_1", "DB_WRITES", 50, 3600)   # 50 writes per hour

# Define activities
@activity.defn
async def api_heavy_activity(data: str) -> str:
    """Activity that makes many API calls"""
    # Activity implementation
    return f"Processed API activity: {data}"

# Register activity quota requirements
router.register_activity_quotas("api_heavy_activity", {
    'API_CALLS': 10,
    'DB_WRITES': 1
})
```

### Monitoring Dashboard

QLO-Fair includes monitoring tools to track quota usage and system performance:

```python
from fastapi import FastAPI
from qlofair.monitoring.metrics import QuotaMetricsCollector

# Initialize FastAPI app
app = FastAPI(title="QLO-Fair Dashboard")

# Initialize metrics collector
metrics_collector = QuotaMetricsCollector(qlofair_router.quota_registry)
metrics_collector.start_periodic_collection(interval=10)  # Collect every 10 seconds

# Setup dashboard routes
@app.get("/metrics/summary")
async def get_system_summary():
    """Get system-wide summary metrics"""
    return metrics_collector.get_system_summary()
```

## Architecture

The QLO-Fair library consists of several key components:

- **Core**: Implements the sliding window quota tracking and worker selection algorithms
- **Adapters**: Provides integration with Celery and Temporal
- **Monitoring**: Tools for tracking quota usage and system performance
- **CLI**: Command-line interface for generating configurations

## Documentation

For complete documentation, see the examples directory:

- `examples/celery_example.py`: Example of using QLO-Fair with Celery
- `examples/temporal_example.py`: Example of using QLO-Fair with Temporal
- `examples/dashboard_example.py`: Example of creating a dashboard for QLO-Fair metrics

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

MIT
```

This README now provides:
- A more comprehensive overview of QLO-Fair's features
- Clear installation instructions
- Quick start examples for different integrations
- Architecture overview
- Documentation references
- Contribution guidelines
- License information

Would you like me to make any additional updates to this README?