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

"""
Production implementation for monitoring industrial multi-model AI pipelines using W&B Weave and Ray.
Provides secure, scalable operations with logging and error handling.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import requests
import time
from contextlib import contextmanager

logging.basicConfig(level=logging.INFO)  # Log at INFO level
logger = logging.getLogger(__name__)  # Create a logger instance

class Config:
    """Configuration class to manage environment variables."""
    database_url: str = os.getenv('DATABASE_URL')  # Database connection URL
    api_key: str = os.getenv('API_KEY')  # API key for external services

@contextmanager
def connect_to_db():
    """Context manager for database connection.
    
    Yields:
        db_connection: Database connection object
    """
    logger.info('Connecting to database.')  # Log connection attempt
    db_connection = create_db_connection(Config.database_url)  # Create DB connection
    try:
        yield db_connection  # Yield connection for use
    finally:
        db_connection.close()  # Ensure connection is closed
        logger.info('Database connection closed.')  # Log connection closure

async 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')  # Validate presence of model_id
    if not isinstance(data['model_id'], str):
        raise ValueError('model_id must be a string')  # Validate type
    return True  # Input is valid

async def fetch_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetch data from an external API.
    
    Args:
        api_url: URL of the API to fetch data from
    Returns:
        List of records fetched from the API
    Raises:
        ConnectionError: If the API request fails
    """
    logger.info(f'Fetching data from {api_url}')  # Log data fetch
    try:
        response = requests.get(api_url, headers={'Authorization': f'Bearer {Config.api_key}'})
        response.raise_for_status()  # Raise error for bad response
        return response.json()  # Return JSON response
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')  # Log error
        raise ConnectionError('Failed to fetch data')  # Raise connection error

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform fetched records into the desired format.
    
    Args:
        records: List of records to transform
    Returns:
        Transformed list of records
    """
    logger.info('Transforming records.')  # Log transformation process
    return [{'id': record['id'], 'value': record['value']} for record in records]  # Transform records

async def save_to_db(db_connection, records: List[Dict[str, Any]]) -> None:
    """Save transformed records to the database.
    
    Args:
        db_connection: Database connection object
        records: Records to save
    Raises:
        Exception: If saving fails
    """
    logger.info('Saving records to the database.')  # Log saving process
    try:
        for record in records:
            db_connection.execute('INSERT INTO models (id, value) VALUES (?, ?)', (record['id'], record['value']))  # Save each record
        db_connection.commit()  # Commit transaction
    except Exception as e:
        logger.error(f'Error saving to database: {e}')  # Log error
        raise  # Re-raise exception

async def aggregate_metrics(records: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from records.
    
    Args:
        records: List of records to aggregate
    Returns:
        Dictionary of aggregated metrics
    """
    logger.info('Aggregating metrics from records.')  # Log aggregation process
    total = sum(record['value'] for record in records)  # Calculate total
    average = total / len(records) if records else 0  # Calculate average
    return {'total': total, 'average': average}  # Return metrics

async def process_batch(api_url: str, db_connection):
    """Process a batch of data from the API and save to the database.
    
    Args:
        api_url: URL of the API to fetch data from
        db_connection: Database connection object
    """
    try:
        data = await fetch_data(api_url)  # Fetch data
        await validate_input(data)  # Validate data
        transformed = await transform_records(data)  # Transform data
        await save_to_db(db_connection, transformed)  # Save to DB
        metrics = await aggregate_metrics(transformed)  # Aggregate metrics
        logger.info(f'Processed batch: {metrics}')  # Log metrics
    except Exception as e:
        logger.error(f'Processing batch failed: {e}')  # Log processing failure

async def main():
    """Main orchestration function.
    
    Runs the data processing pipeline.
    """
    api_url = 'https://api.example.com/models'  # API URL
    with connect_to_db() as db_connection:  # Use context manager for DB connection
        await process_batch(api_url, db_connection)  # Process data batch

if __name__ == '__main__':
    import asyncio
    asyncio.run(main())  # Run main function in the event loop