Orchestrate Durable Manufacturing Workflows with LangGraph and Prefect

"""
Production implementation for orchestrating durable manufacturing workflows using LangGraph and Prefect.
Provides secure, scalable operations for complex workflows.
"""
from typing import Dict, Any, List
import os
import logging
import time
from prefect import flow, task, Client
from prefect.run_configs import LocalRun
from prefect.storage import GitHub

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

class Config:
    """
    Configuration class to hold environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///default.db')  # Default to SQLite
    api_url: str = os.getenv('API_URL', 'https://api.example.com')

@task
def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for workflow.
    
    Args:
        data: Input dictionary containing workflow parameters.
    Returns:
        True if input is valid.
    Raises:
        ValueError: If validation fails.
    """
    if not isinstance(data, dict):
        raise ValueError('Input must be a dictionary.')  # Validate input type
    if 'id' not in data:
        raise ValueError('Missing required field: id')  # Check for required field
    logger.info('Input validated successfully.')  # Log validation success
    return True

@task
def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent SQL injection and other issues.
    
    Args:
        data: Input dictionary to sanitize.
    Returns:
        Sanitized dictionary.
    """
    sanitized = {key: str(value).strip() for key, value in data.items()}  # Strip whitespace
    logger.info('Fields sanitized.')  # Log sanitation
    return sanitized

@task
def normalize_data(data: Dict[str, Any]) -> List[Dict[str, Any]]:
    """Normalize input data for processing.
    
    Args:
        data: Input dictionary to normalize.
    Returns:
        List of normalized data records.
    """
    # Example normalization logic
    normalized = [{'id': data['id'], 'value': data.get('value', 0)}]
    logger.info('Data normalized.')  # Log normalization
    return normalized

@task
def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for further processing.
    
    Args:
        records: List of records to transform.
    Returns:
        Transformed records list.
    """
    transformed = [{'id': record['id'], 'value': record['value'] * 2} for record in records]  # Example transformation
    logger.info('Records transformed.')  # Log transformation
    return transformed

@task
def process_batch(records: List[Dict[str, Any]]) -> None:
    """Process a batch of records.
    
    Args:
        records: List of records to process.
    """
    logger.info(f'Processing batch: {records}')  # Log batch processing
    # Simulate processing logic
    time.sleep(1)  # Placeholder for processing time
    logger.info('Batch processed successfully.')  # Log batch completion

@task
def aggregate_metrics(records: List[Dict[str, Any]]) -> Dict[str, float]:
    """Aggregate metrics from processed records.
    
    Args:
        records: List of processed records.
    Returns:
        Dictionary of aggregated metrics.
    """
    total_value = sum(record['value'] for record in records)  # Aggregate total value
    metrics = {'total_value': total_value}
    logger.info('Metrics aggregated.')  # Log metrics aggregation
    return metrics

@task
def fetch_data(api_url: str) -> Dict[str, Any]:
    """Fetch data from an external API.
    
    Args:
        api_url: URL of the API to fetch data from.
    Returns:
        Fetched data as dictionary.
    """
    logger.info(f'Fetching data from {api_url}')  # Log fetch operation
    # Simulate API call
    data = {'id': '123', 'value': 10}  # Simulated response
    logger.info('Data fetched successfully.')  # Log fetch success
    return data

@task
def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Dictionary containing data to save.
    """
    logger.info(f'Saving data to database: {data}')  # Log save operation
    # Simulate database save operation
    time.sleep(1)  # Placeholder for save time
    logger.info('Data saved to database.')  # Log save success

@flow
def durable_manufacturing_workflow(data: Dict[str, Any]) -> None:
    """Main workflow for orchestrating durable manufacturing tasks.
    
    Args:
        data: Input data for the workflow.
    """
    # Validate and sanitize input data
    validate_input(data)  # Validate input
    sanitized_data = sanitize_fields(data)  # Sanitize input
    normalized_records = normalize_data(sanitized_data)  # Normalize data
    transformed_records = transform_records(normalized_records)  # Transform records
    process_batch(transformed_records)  # Process records
    metrics = aggregate_metrics(transformed_records)  # Aggregate metrics
    save_to_db(metrics)  # Save metrics to DB

if __name__ == '__main__':
    # Example usage of the workflow
    example_data = {'id': '123', 'value': 10}  # Example input data
    durable_manufacturing_workflow(example_data)  # Run the workflow