Monitor Manufacturing Agent Performance with PydanticAI and Prefect

                      
                     
"""
Production implementation for monitoring manufacturing agent performance.
Utilizes Pydantic for data validation and Prefect for workflow orchestration.
"""
from typing import Dict, Any, List
import os
import logging
import time
import random
from pydantic import BaseModel, ValidationError
from prefect import task, Flow

# Setting up logging for the application
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///:memory:')

class AgentPerformanceData(BaseModel):
    agent_id: str
    performance_metric: float
    timestamp: str

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
    """
    try:
        AgentPerformanceData(**data)  # Validate using Pydantic
    except ValidationError as e:
        logger.error(f'Validation error: {e}')
        raise ValueError('Invalid data provided')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection.
    
    Args:
        data: Raw input data
    Returns:
        Sanitized data
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    logger.info(f'Sanitized data: {sanitized_data}')
    return sanitized_data

@task
def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from an external source.
    
    Returns:
        List of data records
    """
    # Simulating data fetch with random data
    logger.info('Fetching data...')
    time.sleep(1)  # Simulating delay
    data = [
        {'agent_id': f'agent_{i}', 'performance_metric': random.uniform(0, 100), 'timestamp': f'{time.time()}'}
        for i in range(5)
    ]
    logger.info(f'Fetched data: {data}')
    return data

@task
def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of performance data.
    
    Args:
        data: List of performance records
    """
    for record in data:
        try:
            validate_input(record)  # Validate input data
            logger.info(f'Processing record: {record}')
            # Here you would add logic to save to DB or other processing
        except ValueError as e:
            logger.warning(f'Skipping record due to error: {e}')  # Handle validation errors gracefully

@task
def aggregate_metrics(data: List[Dict[str, Any]]) -> float:
    """Aggregate performance metrics.
    
    Args:
        data: List of performance records
    Returns:
        Average performance metric
    """
    total = sum(record['performance_metric'] for record in data)
    average = total / len(data) if data else 0
    logger.info(f'Average performance metric: {average}')
    return average

@task
def save_to_db(average: float) -> None:
    """Save aggregated metrics to the database.
    
    Args:
        average: Average performance metric to save
    """
    # Placeholder for saving to DB
    logger.info(f'Saving average to database: {average}')

def main_flow() -> None:
    """Main orchestration flow for monitoring agents.
    """
    with Flow('agent-performance-monitor') as flow:
        # Fetch, process, and store metrics
        raw_data = fetch_data()
        process_batch(raw_data)
        average = aggregate_metrics(raw_data)
        save_to_db(average)

    logger.info('Running the flow...')
    flow.run()  # Start the flow

if __name__ == '__main__':
    # Example usage
    main_flow()  # Run the main flow