Orchestrate Industrial Maintenance Agents with Microsoft Agent Framework and LangGraph

                      
                     
"""
Production implementation for orchestrating industrial maintenance agents using the Microsoft Agent Framework and LangGraph.
Provides secure, scalable operations for monitoring and managing industrial systems efficiently.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import requests
import time
from contextlib import contextmanager

# Setup logging for monitoring application behavior
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))
    retry_delay: int = int(os.getenv('RETRY_DELAY', 5))

@contextmanager
def db_connection_pool():
    """
    Context manager for managing database connections.
    
    Yields:
        Connection object for database operations
    """
    try:
        # Simulate creating a database connection
        conn = "Simulated DB Connection"
        logger.info('Database connection established.')
        yield conn
    finally:
        # Simulate closing the connection
        logger.info('Database 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 'agent_id' not in data:
        raise ValueError('Missing agent_id')
    if not isinstance(data['agent_id'], str):
        raise ValueError('agent_id must be a string')
    return True

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

async def fetch_data(url: str) -> Optional[Dict[str, Any]]:
    """Fetch data from API.
    
    Args:
        url: API endpoint
    Returns:
        Response data or None
    Raises:
        Exception: If fetch fails
    """
    for attempt in range(Config.retry_attempts):
        try:
            response = requests.get(url)
            response.raise_for_status()  # Raise HTTPError for bad responses
            logger.info('Data fetched successfully from API.')
            return response.json()
        except requests.HTTPError as e:
            logger.error(f'HTTPError: {e}')
        except Exception as e:
            logger.error(f'Error fetching data: {e}')
        time.sleep(Config.retry_delay)  # Wait before retrying
    logger.error('Failed to fetch data after retries.')
    return None

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save data to database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If save fails
    """
    with db_connection_pool() as conn:
        # Simulate saving to database
        logger.info(f'Saving data to DB: {data}')

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for processing.
    
    Args:
        records: List of records to transform
    Returns:
        Transformed records
    """
    transformed = []
    for record in records:
        transformed.append({
            'agent_id': record['agent_id'],
            'status': record['status'].lower(),
        })
    logger.info('Records transformed for processing.')
    return transformed

async def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of records.
    
    Args:
        batch: List of records to process
    Raises:
        Exception: If processing fails
    """
    try:
        for record in batch:
            logger.info(f'Processing record: {record}')
            await save_to_db(record)
        logger.info('Batch processed successfully.')
    except Exception as e:
        logger.error(f'Error processing batch: {e}')

class MaintenanceAgentOrchestrator:
    """Orchestrator for maintenance agents.
    Handles the workflow of fetching, validating, transforming, and saving data.
    """
    async def run(self) -> None:
        """Run the orchestration process.
        """
        # Fetch data from API
        data = await fetch_data(Config.api_url)
        if data:
            # Validate and sanitize input data
            await validate_input(data)
            sanitized_data = await sanitize_fields(data)
            # Transform records for processing
            records = await transform_records(sanitized_data.get('records', []))
            # Process each batch
            await process_batch(records)

if __name__ == '__main__':
    # Example usage
    orchestrator = MaintenanceAgentOrchestrator()
    import asyncio
    asyncio.run(orchestrator.run())