Scale Procurement Task Distribution with Semantic Kernel and Prefect

                      
                     
"""
Production implementation for scaling procurement task distribution using Semantic Kernel and Prefect.
Provides secure, scalable operations for managing procurement tasks.
"""
from typing import Dict, Any, List
import os
import logging
import time
from prefect import task, flow

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to load environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///procurement.db')

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'task_id' not in data:
        raise ValueError('Missing task_id in input data.')
    if not isinstance(data['task_id'], str):
        raise ValueError('task_id must be a string.')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields for safety.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {key: str(value).strip() for key, value in data.items()}

@task
def fetch_data(task_id: str) -> Dict[str, Any]:
    """Fetch task data from the database.
    
    Args:
        task_id: Unique identifier for the task
    Returns:
        Task data as a dictionary
    Raises:
        Exception: If fetching data fails
    """
    logger.info(f'Fetching data for task_id: {task_id}')
    # Simulated database fetch
    time.sleep(1)  # Simulating delay
    return {'task_id': task_id, 'status': 'pending'}

@task
def process_batch(batch_data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of tasks.
    
    Args:
        batch_data: List of task data dictionaries
    Returns:
        List of processed results
    """
    logger.info('Processing batch of tasks')
    processed_results = []
    for data in batch_data:
        result = {'task_id': data['task_id'], 'status': 'completed'}
        processed_results.append(result)
    return processed_results

@task
def save_to_db(results: List[Dict[str, Any]]) -> None:
    """Save processed results to the database.
    
    Args:
        results: List of results to save
    Raises:
        Exception: If saving results fails
    """
    logger.info('Saving results to the database')
    # Simulated database save
    time.sleep(1)  # Simulating delay
    logger.info('Results saved successfully')

@task
def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed results.
    
    Args:
        results: List of processed results
    Returns:
        Aggregated metrics
    """
    logger.info('Aggregating metrics from results')
    total_completed = len(results)
    return {'total_completed': total_completed}

@flow
def procurement_flow(task_id: str) -> None:
    """Main flow to orchestrate procurement tasks.
    
    Args:
        task_id: Unique identifier for the task
    """
    try:
        validate_input({'task_id': task_id})  # Validate input
        data = fetch_data(task_id)  # Fetch data
        batch_data = [data]  # In a real scenario, this would be a batch
        results = process_batch(batch_data)  # Process tasks
        save_to_db(results)  # Save processed results
        metrics = aggregate_metrics(results)  # Aggregate metrics
        logger.info(f'Flow completed with metrics: {metrics}')  # Log metrics
    except Exception as e:
        logger.error(f'Error in procurement flow: {str(e)}')  # Log errors

if __name__ == '__main__':
    # Example usage
    procurement_flow('task_123')