Monitor Industrial Multi-Model AI Pipelines with W&B Weave and Ray

"""
Production implementation for monitoring industrial multi-model AI pipelines.
Utilizes W&B Weave and Ray for enhanced data processing and visualization.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import requests
from time import sleep
from contextlib import contextmanager

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    weav_api_key: str = os.getenv('WEAV_API_KEY')

@contextmanager
def connect_to_database():
    """Context manager for database connection pooling.
    """  
    try:
        # Simulating database connection setup
        logger.info('Connecting to the database...')
        connection = 'DB_CONNECTION'  # Replace with actual connection logic
        yield connection
    except Exception as e:
        logger.error(f'Error connecting to database: {e}')
        raise
    finally:
        # Simulating cleanup
        logger.info('Closing database connection.')

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
    """
    if 'model_id' not in data:
        raise ValueError('Missing model_id')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields for processing.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {key: str(value).strip() for key, value in data.items()}  # Simple sanitization

def fetch_data(model_id: str) -> Dict[str, Any]:
    """Fetch data for the given model_id.
    
    Args:
        model_id: Identifier for the model
    Returns:
        Fetched data
    Raises:
        Exception: If fetching fails
    """
    try:
        response = requests.get(f'http://example.com/models/{model_id}')
        response.raise_for_status()  # Raise error for bad responses
        return response.json()
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise

def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for processing.
    
    Args:
        records: List of records to transform
    Returns:
        Transformed records
    """
    return [{'id': record['id'], 'value': record['value'] * 2} for record in records]  # Dummy transformation

def process_batch(records: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Process a batch of records.
    
    Args:
        records: List of records to process
    Returns:
        Aggregated metrics
    Raises:
        Exception: If processing fails
    """
    try:
        logger.info('Processing batch of records.')
        # Simulate processing logic
        return {'average': sum(record['value'] for record in records) / len(records)}
    except Exception as e:
        logger.error(f'Error processing batch: {e}')
        raise

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

def call_api(endpoint: str, data: Dict[str, Any]) -> None:
    """Call an external API.
    
    Args:
        endpoint: API endpoint to call
        data: Data to send in the request
    Raises:
        Exception: If API call fails
    """
    try:
        logger.info(f'Calling API: {endpoint}')
        response = requests.post(endpoint, json=data)
        response.raise_for_status()
    except Exception as e:
        logger.error(f'Error calling API: {e}')
        raise

class PipelineOrchestrator:
    """Main orchestrator for managing pipeline workflow.
    """
    def __init__(self):
        self.config = Config()

    def execute_pipeline(self, model_id: str) -> None:
        """Execute the full pipeline for the given model_id.
        
        Args:
            model_id: Identifier for the model to process
        """
        logger.info(f'Starting pipeline for model_id: {model_id}')
        with connect_to_database() as db:
            raw_data = fetch_data(model_id)  # Fetch data
            validated_data = sanitize_fields(raw_data)  # Sanitize data
            transformed_data = transform_records(validated_data)  # Transform data
            metrics = process_batch(transformed_data)  # Process data
            save_to_db(metrics)  # Save metrics
            call_api('http://example.com/api/endpoint', metrics)  # Notify

if __name__ == '__main__':
    # Example usage
    orchestrator = PipelineOrchestrator()
    try:
        orchestrator.execute_pipeline('example_model_id')  # Replace with actual ID
    except Exception as e:
        logger.error(f'Pipeline execution failed: {e}')