Route Industrial Alerts to Specialist Agents with Claude Agent SDK and Semantic Kernel

"""
Production implementation for routing industrial alerts to specialist agents using Claude Agent SDK and Semantic Kernel.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
from contextlib import contextmanager

# Logger setup for tracking application flow and errors.
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    database_url: str = os.getenv('DATABASE_URL')
    api_key: str = os.getenv('API_KEY')

@contextmanager
def connection_pool():
    """Context manager for database connection pooling.
    
    Yields:
        Connection object
    """  
    try:
        # Simulating a database connection
        connection = "DB Connection"
        yield connection
    finally:
        # Cleanup operation
        logger.info("Connection closed")

async 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 'alert_id' not in data or 'message' not in data:
        raise ValueError('Missing required fields: alert_id, message')
    return True

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

async def transform_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform input data to desired format.
    
    Args:
        data: Raw input data
    Returns:
        Transformed data
    """  
    # Example transformation
    return {'id': data['alert_id'], 'content': data['message'].upper()}

async def fetch_data(url: str) -> Any:
    """Fetch data from external API.
    
    Args:
        url: API endpoint
    Returns:
        Response JSON data
    Raises:
        Exception: If request fails
    """  
    logger.info(f'Fetching data from {url}')
    try:
        response = requests.get(url, headers={'Authorization': f'Bearer {Config.api_key}'})
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()
    except requests.exceptions.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If save operation fails
    """  
    logger.info('Saving data to DB')
    with connection_pool() as conn:
        try:
            # Simulate save operation
            logger.info(f'Saved data: {data}')
        except Exception as e:
            logger.error(f'Error saving to DB: {e}')
            raise

async def handle_alert(data: Dict[str, Any]) -> None:
    """Handle the incoming alert by validating, transforming, and saving it.
    
    Args:
        data: Incoming alert data
    """  
    try:
        await validate_input(data)
        sanitized_data = await sanitize_fields(data)
        transformed_data = await transform_data(sanitized_data)
        await save_to_db(transformed_data)
    except ValueError as ve:
        logger.error(f'Validation error: {ve}')
    except Exception as e:
        logger.error(f'Error handling alert: {e}')

async def process_batch(alerts: List[Dict[str, Any]]) -> None:
    """Process a batch of alerts.
    
    Args:
        alerts: List of alert data
    """  
    for alert in alerts:
        await handle_alert(alert)

if __name__ == '__main__':
    # Example usage
    alerts = [{'alert_id': '1', 'message': 'Machine failure detected'}, {'alert_id': '2', 'message': 'Temperature threshold exceeded'}]
    logger.info('Starting alert processing...')
    import asyncio
    asyncio.run(process_batch(alerts))