Orchestrate Manufacturing Task Workflows with Microsoft Agent Framework and Paperclip

                      
                     
"""
Production implementation for orchestrating manufacturing task workflows using the Microsoft Agent Framework and Paperclip.
This module integrates data validation, task orchestration, and reporting.
"""

from typing import Dict, Any, List, Optional
import os
import logging
import time
import requests
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

# Logger setup for monitoring and debugging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class to manage environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///tasks.db')

# Database connection pooling
engine = create_engine(Config.database_url, pool_size=20, max_overflow=0)
Session = sessionmaker(bind=engine)

# Function to validate input data
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input data to validate
    Returns:
        True if the data is valid
    Raises:
        ValueError: If validation fails
    """
    if 'task_id' not in data or not isinstance(data['task_id'], int):
        raise ValueError('Invalid or missing task_id')  # Ensure task_id exists and is an integer
    return True

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

# Function to fetch data from an external API
async def fetch_data(api_url: str) -> Dict[str, Any]:
    """Fetch data from the provided API URL.
    
    Args:
        api_url: The API endpoint to fetch data from
    Returns:
        Response data as a dictionary
    Raises:
        Exception: If the request fails
    """
    try:
        response = requests.get(api_url)
        response.raise_for_status()  # Raise an error for bad HTTP responses
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise Exception('Failed to fetch data from API')

# Function to transform records for processing
def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform fetched records into the required format.
    
    Args:
        records: List of records to transform
    Returns:
        Transformed list of records
    """
    return [{'id': record['id'], 'name': record['name'].upper()} for record in records]

# Function to save transformed records to the database
async def save_to_db(records: List[Dict[str, Any]]) -> None:
    """Save the transformed records to the database.
    
    Args:
        records: List of records to save
    """
    with Session() as session:
        for record in records:
            try:
                session.execute(text('INSERT INTO tasks (id, name) VALUES (:id, :name)'), record)
                session.commit()  # Commit the transaction
            except Exception as e:
                logger.error(f'Error saving record {record}: {e}')
                session.rollback()  # Roll back on error

# Function to process tasks in batches
async def process_batch(api_url: str) -> None:
    """Process a batch of tasks by fetching, transforming, and saving them.
    
    Args:
        api_url: The API endpoint to fetch tasks from
    """
    try:
        raw_data = await fetch_data(api_url)  # Fetch raw data from API
        # Validate and sanitize input
        await validate_input(raw_data)
        sanitized_data = sanitize_fields(raw_data)
        transformed_data = transform_records(sanitized_data['tasks'])  # Transform fetched data
        await save_to_db(transformed_data)  # Save to database
    except ValueError as ve:
        logger.warning(f'Validation error: {ve}')  # Log validation errors
    except Exception as e:
        logger.error(f'Processing error: {e}')  # Log general errors

# Main orchestrator class to manage workflows
class WorkflowOrchestrator:
    def __init__(self, api_url: str):
        self.api_url = api_url  # Store API URL for fetching tasks

    async def run(self) -> None:
        """Execute the workflow for orchestrating manufacturing tasks.
        """
        logger.info('Starting workflow orchestration...')
        await process_batch(self.api_url)  # Execute task processing
        logger.info('Workflow completed successfully.')

# Entry point for the application
if __name__ == '__main__':
    # Example usage of the orchestrator
    orchestrator = WorkflowOrchestrator(api_url='https://api.example.com/tasks')
    # Note: Async main execution would typically require an event loop setup in production
    orchestrator.run()  # Run the workflow orchestrator