Deploy and Scale Industrial LLM Endpoints with Ray and BentoML

"""
Production implementation for deploying and scaling industrial LLM endpoints.
Utilizes Ray for distributed computing and BentoML for model serving.
"""
from typing import Dict, Any, List
import os
import logging
import ray
import bentoml
from bentoml.io import JSON

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

# Configuration class to manage environment variables
class Config:
    model_name: str = os.getenv('MODEL_NAME', 'default_model')
    db_url: str = os.getenv('DATABASE_URL', 'sqlite:///:memory:')

# Initialize Ray
ray.init()

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for input.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'input_text' not in data:
        raise ValueError('Missing input_text')  # Validation error
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent security issues.
    
    Args:
        data: Input data
    Returns:
        Cleaned data
    """
    return {k: v.strip() for k, v in data.items()}

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data for processing.
    
    Args:
        data: Input data
    Returns:
        Normalized data
    """
    # Example normalization logic
    data['input_text'] = data['input_text'].lower()  # Normalize text
    return data

@ray.remote
def process_batch(batch: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of input data using the LLM.
    
    Args:
        batch: List of input data dictionaries
    Returns:
        List of processed results
    """
    results = []
    for item in batch:
        result = f"Processed: {item['input_text']}"  # Placeholder processing
        results.append({'result': result})
    return results

def fetch_data(query: str) -> List[Dict[str, Any]]:
    """Fetch data from the database.
    
    Args:
        query: SQL query string
    Returns:
        List of records
    """
    # Simulated data fetching
    return [{'input_text': 'Example text'}]  # Placeholder data

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
    """
    # Simulated save operation
    logger.info('Data saved to database')  # Placeholder logging

@bentoml.env(pip_packages="ray[binary],bentoml")
@bentoml.artifacts([bentoml.ModelArtifact('model')])
class LLMService(bentoml.BentoService):
    """BentoML service for LLM endpoints.
    """

    @bentoml.api(input=JSON(), output=JSON())
    def predict(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Predict method for LLM.
        
        Args:
            input_data: Input data for prediction
        Returns:
            Prediction results
        """
        try:
            validate_input(input_data)  # Validate input
            sanitized_data = sanitize_fields(input_data)  # Sanitize data
            normalized_data = normalize_data(sanitized_data)  # Normalize input
            batch = [normalized_data]  # Prepare batch for processing
            results = ray.get(process_batch.remote(batch))  # Call distributed function
            save_to_db(results)  # Save results to database
            return {'results': results}  # Return results
        except Exception as e:
            logger.error(f'Error in prediction: {e}')  # Log error
            return {'error': str(e)}  # Return error message

if __name__ == '__main__':
    # Example usage of LLMService
    service = LLMService()
    # Simulated input
    input_example = {'input_text': 'Sample input for LLM'}
    print(service.predict(input_example))  # Call predict method