Build Cross-Protocol Factory Inspection Agents with Google ADK and PydanticAI

"""
Production implementation for building cross-protocol factory inspection agents using Google ADK and PydanticAI.
This architecture leverages async capabilities and structured data validation.
"""
import os
import logging
from typing import Dict, Any, List, Tuple
import httpx
from pydantic import BaseModel, ValidationError, Field

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

# Configuration class to load environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    api_endpoint: str = os.getenv('API_ENDPOINT')

# Data model using Pydantic for input validation
class InspectionData(BaseModel):
    id: int = Field(..., description='Unique identifier for the inspection')
    factory_id: int = Field(..., description='Factory identifier')
    status: str = Field(..., max_length=20, description='Inspection status')

# Helper Functions
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    try:
        InspectionData(**data)  # Validate data using Pydantic
    except ValidationError as e:
        logger.error(f'Validation error: {e}')  # Log validation error
        raise ValueError('Invalid input data')
    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
    """
    # Here, we would sanitize fields (e.g., strip whitespace)
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    return sanitized_data

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data for consistent processing.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    # Example normalization (e.g., converting status to uppercase)
    data['status'] = data['status'].upper()
    return data

async def fetch_data(factory_id: int) -> List[Dict[str, Any]]:
    """Fetch inspection data from external API.
    
    Args:
        factory_id: Identifier for the factory
    Returns:
        List of inspection records
    Raises:
        httpx.RequestError: If the request fails
    """
    try:
        async with httpx.AsyncClient() as client:
            response = await client.get(f'{Config.api_endpoint}/inspections/{factory_id}')
            response.raise_for_status()  # Raise an error for bad responses
            return response.json()
    except httpx.RequestError as e:
        logger.error(f'Error fetching data: {e}')
        raise

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save inspection data to the database.
    
    Args:
        data: Inspection data to save
    Raises:
        Exception: If saving fails
    """
    try:
        # Simulating database save operation
        logger.info('Saving data to database...')  # Log saving action
        # Here you would insert the data into your database
    except Exception as e:
        logger.error(f'Error saving data: {e}')  # Log any errors
        raise

async def process_batch(records: List[Dict[str, Any]]) -> None:
    """Process a batch of inspection records.
    
    Args:
        records: List of records to process
    """
    for record in records:
        await save_to_db(record)  # Save each record to the database

async def aggregate_metrics(records: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from the records.
    
    Args:
        records: List of records to aggregate
    Returns:
        Aggregated metrics
    """
    # Example aggregation logic
    total = len(records)
    return {'total_inspections': total}

# Main orchestrator class
class FactoryInspectionAgent:
    def __init__(self, factory_id: int):
        self.factory_id = factory_id

    async def run(self) -> None:
        """Run the inspection process.
        
        This method orchestrates the data fetching, processing, and saving.
        """
        try:
            data = await fetch_data(self.factory_id)  # Fetch data
            sanitized_data = await sanitize_fields(data)  # Sanitize data
            normalized_data = await normalize_data(sanitized_data)  # Normalize data
            await process_batch(normalized_data)  # Process the data
            metrics = await aggregate_metrics(normalized_data)  # Aggregate metrics
            logger.info(f'Metrics: {metrics}')  # Log the metrics
        except Exception as e:
            logger.error(f'Failed to run inspection: {e}')  # Log errors

if __name__ == '__main__':
    import asyncio
    # Example usage
    factory_id = 1  # Example factory ID
    agent = FactoryInspectionAgent(factory_id)
    asyncio.run(agent.run())  # Run the inspection agent