Validate Twin Simulation Outputs with Great Expectations and Vertex AI SDK

                      
                     
"""
Production implementation for validating twin simulation outputs.
Integrates Great Expectations for data validation and Vertex AI SDK for model interaction.
"""
from typing import Dict, Any, List
import os
import logging
import time
import great_expectations as ge
from vertexai import VertexAI

# Setup 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')
    vertex_project: str = os.getenv('VERTEX_PROJECT')
    vertex_model: str = os.getenv('VERTEX_MODEL')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for the twin simulation outputs.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'simulation_id' not in data:
        raise ValueError('Missing simulation_id')
    if 'outputs' not in data:
        raise ValueError('Missing outputs')
    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 {key: str(value).strip() for key, value in data.items()}

async def fetch_data(simulation_id: str) -> Dict[str, Any]:
    """Fetch simulation data from the database.
    
    Args:
        simulation_id: Unique identifier for the simulation
    Returns:
        Data retrieved from the database
    Raises:
        Exception: If fetch fails
    """
    try:
        # Simulating database fetch
        logger.info(f'Fetching data for simulation_id: {simulation_id}')
        return {'simulation_id': simulation_id, 'outputs': [1, 2, 3]}
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise

async def validate_outputs(data: Dict[str, Any]) -> bool:
    """Validate outputs using Great Expectations.
    
    Args:
        data: Data containing outputs to validate
    Returns:
        True if outputs are valid
    Raises:
        ValueError: If validation fails
    """
    try:
        context = ge.data_context.DataContext('/path/to/great_expectations')
        batch = context.get_batch(data, 'my_dataset')
        results = context.run_validation_operator('my_validation_operator', assets_to_validate=[batch])
        if not results['success']:
            raise ValueError('Validation failed')
        return True
    except Exception as e:
        logger.error(f'Validation error: {e}')
        raise

async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform records for further processing.
    
    Args:
        data: Input data to transform
    Returns:
        Transformed data
    """
    return {'transformed_outputs': [output * 2 for output in data['outputs']]}

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If save fails
    """
    try:
        logger.info(f'Saving data: {data}')
        # Simulating saving to the database
    except Exception as e:
        logger.error(f'Error saving data: {e}')
        raise

async def call_api(data: Dict[str, Any]) -> Any:
    """Call an external API using Vertex AI SDK.
    
    Args:
        data: Data to send to API
    Returns:
        API response
    Raises:
        Exception: If API call fails
    """
    try:
        vertex = VertexAI(project=Config.vertex_project)
        response = await vertex.predict(model=Config.vertex_model, inputs=data)
        return response
    except Exception as e:
        logger.error(f'API call error: {e}')
        raise

async def process_batch(data: Dict[str, Any]) -> None:
    """Main processing function orchestrating validation and saving.
    
    Args:
        data: Input data to process
    Raises:
        Exception: If processing fails
    """
    try:
        await validate_input(data)  # Validate input
        sanitized_data = await sanitize_fields(data)  # Sanitize fields
        fetched_data = await fetch_data(sanitized_data['simulation_id'])  # Fetch data
        if await validate_outputs(fetched_data):  # Validate outputs
            transformed_data = await transform_records(fetched_data)  # Transform
            await save_to_db(transformed_data)  # Save results
            logger.info('Batch processing completed successfully')
    except Exception as e:
        logger.error(f'Batch processing failed: {e}')
        # Handle specific error recovery if needed

async def main(simulation_id: str) -> None:
    """Main entry point for validation workflow.
    
    Args:
        simulation_id: Unique identifier for the simulation
    """
    try:
        # Simulate input data
        data = {'simulation_id': simulation_id, 'outputs': [1, 2, 3]}
        await process_batch(data)  # Run processing
    except Exception as e:
        logger.error(f'Error in main workflow: {e}')

if __name__ == '__main__':
    import asyncio
    simulation_id = 'sim123'  # Example simulation ID
    asyncio.run(main(simulation_id))