Fine-Tune Industrial LLMs with Structured Reward Signals using VERL and TRL

"""
Production implementation for Fine-Tune Industrial LLMs with Structured Reward Signals using VERL and TRL.
Provides secure, scalable operations to fine-tune language models effectively.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import time
import requests
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

# Logger setup for monitoring and debugging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///:memory:')  # Fallback to in-memory DB
    api_endpoint: str = os.getenv('API_ENDPOINT', 'http://localhost:5000/api')

# Create a database engine with connection pooling
engine = create_engine(Config.database_url, pool_size=5, max_overflow=10)
Session = sessionmaker(bind=engine)


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 not isinstance(data, dict):
        raise ValueError('Input must be a dictionary.')  # Validate input type
    if 'model_id' not in data:
        raise ValueError('Missing required field: model_id')  # Ensure model_id is present
    logger.info('Input validation successful.')  # Log validation success
    return True


def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data dictionary
    Returns:
        Sanitized data dictionary
    """
    sanitized_data = {key: str(value).strip() for key, value in data.items()}  # Strip whitespace
    logger.info('Fields sanitized.')  # Log sanitation
    return sanitized_data


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
    """
    transformed = []  # Prepare an empty list for transformed records
    for record in records:
        transformed.append({
            'model_id': record['model_id'],
            'reward_signal': record['reward_signal'] * 1.5  # Example transformation
        })  # Scale the reward signals
    logger.info('Records transformed.')  # Log transformation
    return transformed


def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of records.
    
    Args:
        batch: List of records to process
    """
    with Session() as session:  # Use context manager for session
        for record in batch:
            # Here we would implement fine-tuning logic
            logger.info(f'Processing model: {record['model_id']}')  # Log processing
            # (Assume fine-tuning occurs here)
    logger.info('Batch processing completed.')  # Log completion


def fetch_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetch data from an external API.
    
    Args:
        api_url: The URL of the API to fetch data from
    Returns:
        List of fetched records
    Raises:
        ConnectionError: If the API call fails
    """
    try:
        response = requests.get(api_url)
        response.raise_for_status()  # Raise an error for bad responses
        logger.info('Data fetched successfully.')  # Log successful fetch
        return response.json()  # Return JSON data
    except requests.RequestException as e:
        logger.error('Failed to fetch data from API.')  # Log error
        raise ConnectionError('Failed to fetch data from API') from e


def save_to_db(records: List[Dict[str, Any]]) -> None:
    """Save processed records to the database.
    
    Args:
        records: List of records to save
    """
    with Session() as session:
        for record in records:
            session.execute(
                text("INSERT INTO fine_tuned_models (model_id, reward_signal) VALUES (:model_id, :reward_signal)"),
                {'model_id': record['model_id'], 'reward_signal': record['reward_signal']}
            )  # Save to DB
        session.commit()  # Commit the transaction
    logger.info('Records saved to database.')  # Log saving


def handle_errors(func):
    """Decorator to handle errors in function calls.
    
    Args:
        func: Function to wrap with error handling
    """
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in {func.__name__}: {str(e)}')  # Log error
            raise  # Re-raise exception for further handling
    return wrapper


@handle_errors
def main(data: Dict[str, Any]) -> None:
    """Main function to orchestrate the workflow.
    
    Args:
        data: Input data dictionary
    """
    validate_input(data)  # Validate the input
    sanitized_data = sanitize_fields(data)  # Sanitize the input
    records = fetch_data(Config.api_endpoint)  # Fetch data from API
    transformed_records = transform_records(records)  # Transform the records
    process_batch(transformed_records)  # Process the batch
    save_to_db(transformed_records)  # Save to database


if __name__ == '__main__':
    # Example usage with mock data
    example_data = {'model_id': 'llm_123', 'reward_signal': 10}
    main(example_data)  # Call the main function