Trace and Debug Industrial AI Pipelines with OpenTelemetry and BentoML

"""
Production implementation for tracing and debugging industrial AI pipelines.
Utilizes OpenTelemetry for monitoring and BentoML for serving models.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
from functools import wraps
from opentelemetry import trace
from opentelemetry.propagate import inject
from opentelemetry.instrumentation.requests import RequestsInstrumentor
from opentelemetry.instrumentation.bentoml import BentoMLInstrumentor
from bentoml import env, artifacts, api
from bentoml import BentoService

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

# Initialize OpenTelemetry
tracer = trace.get_tracer(__name__)

class Config:
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')

def retry_with_backoff(max_retries: int, backoff_factor: float) -> callable:
    """Decorator for retrying function calls with exponential backoff.
    
    Args:
        max_retries: Maximum number of retries
        backoff_factor: Backoff factor for exponential backoff
    """
    def decorator(func: callable) -> callable:
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    logger.warning(f'Attempt {attempt + 1} failed: {e}')
                    time.sleep(backoff_factor * (2 ** attempt))  # Exponential backoff
            raise RuntimeError(f'Function {func.__name__} failed after {max_retries} retries.')
        return wrapper
    return decorator

@retry_with_backoff(max_retries=3, backoff_factor=1)
def fetch_data(endpoint: str) -> Dict[str, Any]:
    """Fetch data from an API endpoint.
    
    Args:
        endpoint: API endpoint to fetch data from
    Returns:
        Parsed JSON response
    Raises:
        ValueError: If response is not valid JSON
    """
    logger.info(f'Fetching data from {endpoint}')
    response = requests.get(endpoint)
    if response.status_code != 200:
        raise ValueError('Failed to fetch data')
    return response.json()  # Return parsed JSON

@retry_with_backoff(max_retries=3, backoff_factor=1)
def save_to_db(data: Dict[str, Any]) -> None:
    """Save data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    logger.info('Saving data to the database')
    # Simulate save operation
    # Database connection pooling can be implemented here
    if not data:
        raise Exception('No data to save')
    logger.info('Data saved successfully')

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize input data for consistency.
    
    Args:
        data: Raw input data
    Returns:
        Normalized data
    """
    logger.info('Normalizing data')
    normalized = {key: str(value).strip() for key, value in data.items()}
    return normalized

def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for processing.
    
    Args:
        records: List of raw records
    Returns:
        Transformed records
    """
    logger.info('Transforming records')
    return [normalize_data(record) for record in records]

class AIPipeline(BentoService):
    """Main class for AI pipeline handling.
    
    Attributes:
        model: Loaded ML model for inference
    """
    @api(input=BentoMLInstrumentor)  # Define an API endpoint
    def predict(self, data: Dict[str, Any]) -> Any:
        """Predict using the ML model.
        
        Args:
            data: Input data for prediction
        Returns:
            Prediction result
        """
        logger.info('Starting prediction')
        validated_data = validate_input_data(data)  # Validate the input
        result = self.model.predict(validated_data)  # Model inference
        return result

def validate_input_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Validate input data.
    
    Args:
        data: Input data to validate
    Returns:
        Validated data
    Raises:
        ValueError: If validation fails
    """
    if 'features' not in data:
        raise ValueError('Missing required field: features')
    # Additional validation logic
    logger.info('Input data validated')
    return data

if __name__ == '__main__':
    # Example workflow
    try:
        raw_data = fetch_data(Config.api_url)
        transformed_data = transform_records(raw_data)
        save_to_db(transformed_data)
    except Exception as e:
        logger.error(f'Error during pipeline execution: {e}')  # Log errors
        raise