Run Fault-Isolating Quality Control Agents with Strands Agents and LangGraph

"""
Production implementation for running fault-isolating quality control agents with Strands Agents and LangGraph.
Provides secure, scalable operations that can handle diverse data flows and ensure quality control.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import aiohttp
import asyncio
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class to handle environment variables and application settings."""
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///default.db')  # Fallback to a local SQLite

# Create a database engine
engine = create_engine(Config.database_url)
Session = sessionmaker(bind=engine)

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for quality control agents.
    
    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 'quality_metrics' not in data or not isinstance(data['quality_metrics'], dict):
        raise ValueError('Invalid quality_metrics field')
    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
    """
    return {k: str(v).strip() for k, v in data.items()}

async def fetch_data(url: str) -> Dict[str, Any]:
    """Fetch data from a given API endpoint.
    
    Args:
        url: URL to fetch data from
    Returns:
        Parsed JSON response
    Raises:
        Exception: On HTTP errors
    """
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            response.raise_for_status()  # Raise an error for bad responses
            return await response.json()

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save validated data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: On database errors
    """
    with Session() as session:
        session.execute(text("INSERT INTO quality_control (agent_id, metrics) VALUES (:agent_id, :metrics)"), 
                          {'agent_id': data['agent_id'], 'metrics': data['quality_metrics']})
        session.commit()  # Commit the transaction

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform raw records into a desired format for processing.
    
    Args:
        records: Raw records to transform
    Returns:
        List of transformed records
    """
    return [{'agent_id': rec['agent_id'], 'quality_metrics': rec['quality_metrics']} for rec in records]

async def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of quality control agent data.
    
    Args:
        batch: List of data records to process
    """
    for record in batch:
        try:
            await validate_input(record)  # Validate each record
            sanitized = await sanitize_fields(record)  # Sanitize fields
            await save_to_db(sanitized)  # Save to DB
            logger.info(f'Successfully processed record: {sanitized}')
        except Exception as e:
            logger.error(f'Error processing record {record}: {e}')  # Log the error

async def aggregate_metrics(records: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed records.
    
    Args:
        records: List of records to aggregate
    Returns:
        Dictionary of aggregated metrics
    """
    # Example aggregation logic
    total = sum(rec['quality_metrics']['score'] for rec in records)
    return {'total_score': total, 'count': len(records)}

async def handle_errors(e: Exception) -> None:
    """Handle errors gracefully across the application.
    
    Args:
        e: Exception to handle
    """
    logger.error(f'An error occurred: {str(e)}')  # Log the error details

class QualityControlAgent:
    """Main orchestrator for managing quality control agents."""

    async def run(self, url: str) -> None:
        """Run the quality control process.
        
        Args:
            url: API endpoint to fetch data from
        """
        try:
            raw_data = await fetch_data(url)  # Fetch raw data
            records = await transform_records(raw_data)  # Transform records
            await process_batch(records)  # Process each batch of records
            logger.info('Quality control process completed successfully.')
        except Exception as e:
            await handle_errors(e)  # Handle any errors

if __name__ == '__main__':
    # Example usage
    agent = QualityControlAgent()
    asyncio.run(agent.run('https://api.example.com/quality-data'))  # Run the quality control process