Route Manufacturing Anomaly Alerts to Specialist Agents with Agno and Semantic Kernel

"""
Production implementation for routing manufacturing anomaly alerts to specialist agents using Agno and Semantic Kernel.
Provides secure, scalable operations with robust error handling and logging.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import asyncio
import httpx
from pydantic import BaseModel, ValidationError

# Logging setup with INFO level
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to hold environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    agno_api_key: str = os.getenv('AGNO_API_KEY')

class Alert(BaseModel):
    """
    Pydantic model for alert validation.
    """
    id: str
    message: str
    severity: str

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
    """
    try:
        Alert(**data)  # Validate using Pydantic model
    except ValidationError as e:
        raise ValueError(f'Validation error: {e}')  # Clear error message
    return True  # If valid

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()}  # Strip whitespace
    return sanitized

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize input data for consistent processing.
    
    Args:
        data: Data to normalize
    Returns:
        Normalized data
    """
    # Normalize severity to lowercase
    normalized = {**data, 'severity': data['severity'].lower()}
    return normalized

async def transform_records(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for sending to Agno.
    
    Args:
        data: List of records to transform
    Returns:
        Transformed records
    """
    transformed = []
    for record in data:
        transformed_record = await normalize_data(record)  # Normalize each record
        transformed.append(transformed_record)
    return transformed

async def fetch_data() -> List[Dict[str, Any]]:
    """Fetch anomaly data from the database.
    
    Returns:
        List of anomaly records
    """
    # Placeholder for database fetch logic
    return [{'id': '1', 'message': 'Anomaly detected', 'severity': 'high'}]

async def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save processed alerts to the database.
    
    Args:
        data: List of alerts to save
    """
    # Placeholder for database save logic
    logger.info(f'Saving {len(data)} alerts to the database.')

async def call_api(alert: Dict[str, Any]) -> None:
    """Call the Agno API to route alerts.
    
    Args:
        alert: Alert data to send
    Raises:
        httpx.HTTPStatusError: If API call fails
    """
    async with httpx.AsyncClient() as client:
        response = await client.post('https://agno.api/alerts', json=alert, headers={'Authorization': f'Bearer {Config.agno_api_key}'})
        response.raise_for_status()  # Raise error for bad responses

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of alerts.
    
    Args:
        data: List of alerts to process
    """
    try:
        await save_to_db(data)  # Save alerts
        for alert in data:
            await call_api(alert)  # Call API for each alert
    except Exception as e:
        logger.error(f'Error processing batch: {e}')  # Log error

async def aggregate_metrics(data: List[Dict[str, Any]]) -> None:
    """Aggregate metrics from the alerts.
    
    Args:
        data: List of alerts to aggregate
    """
    # Placeholder for metrics aggregation logic
    logger.info('Aggregating metrics from alerts.')

async def handle_errors(func):
    """Decorator to handle errors in async functions.
    
    Args:
        func: Async function to decorate
    """
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in function {func.__name__}: {e}')  # Log error
            raise  # Re-raise exception
    return wrapper

class AlertProcessor:
    """Main class to process alerts.
    """
    @handle_errors
    async def run(self) -> None:
        """Run the alert processing workflow.
        """
        raw_data = await fetch_data()  # Fetch data from the database
        validated_data = []
        for record in raw_data:
            if await validate_input(record):  # Validate each record
                sanitized_data = await sanitize_fields(record)  # Sanitize fields
                validated_data.append(sanitized_data)  # Append validated data
        await process_batch(validated_data)  # Process the validated batch
        await aggregate_metrics(validated_data)  # Aggregate metrics

if __name__ == '__main__':
    # Example usage
    processor = AlertProcessor()
    asyncio.run(processor.run())  # Run the alert processing workflow