Align Industrial LLMs with RLHF and Hugging Face TRL for Manufacturing Use Cases

                      
                     
"""
Production implementation for aligning industrial LLMs using RLHF
and Hugging Face TRL in manufacturing use cases.
This module provides secure, scalable operations.
"""

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

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

# Configuration class for environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')

# Database connection pooling
engine = create_engine(Config.database_url, pool_size=5, max_overflow=10)
Session = sessionmaker(bind=engine)

@contextmanager
def get_db_session():
    """Context manager for database session.
    
    Yields:
        Session: SQLAlchemy session object
    """
    session = Session()
    try:
        yield session
        session.commit()  # Commit transaction
    except Exception as e:
        logger.error(f"Database operation failed: {e}")
        session.rollback()  # Rollback on error
        raise  # Reraise exception
    finally:
        session.close()  # Ensure session is closed

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for required fields.
    
    Args:
        data: Input data dictionary
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if not all(k in data for k in ['id', 'payload']):
        raise ValueError('Missing required fields: id or payload')  # Raise error
    return True  # Data is valid

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.
    
    Args:
        data: Input data dictionary
    Returns:
        Sanitized data dictionary
    """
    return {k: str(v).strip() for k, v in data.items()}  # Strip whitespace

async def fetch_data(url: str) -> Dict[str, Any]:
    """Fetch data from external API.
    
    Args:
        url: API endpoint URL
    Returns:
        JSON response as dictionary
    Raises:
        Exception: If request fails
    """
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise HTTPError for bad responses
        return response.json()  # Return JSON response
    except requests.RequestException as e:
        logger.error(f"API request failed: {e}")
        raise  # Reraise exception

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records to desired format.
    
    Args:
        records: List of records to transform
    Returns:
        List of transformed records
    """
    transformed = []  # Initialize transformed records
    for record in records:
        transformed_record = {"id": record['id'], "data": json.loads(record['payload'])}
        transformed.append(transformed_record)  # Append transformed record
    return transformed  # Return all transformed records

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of records.
    
    Args:
        data: List of input data dictionaries
    """
    for record in data:
        logger.info(f"Processing record: {record['id']}")
        # Simulate processing
        time.sleep(1)  # Simulated delay for processing
        logger.info(f"Record {record['id']} processed successfully.")

async def aggregate_metrics(metrics: List[float]) -> float:
    """Aggregate metrics from processed records.
    
    Args:
        metrics: List of metric values
    Returns:
        Average metric value
    """
    return sum(metrics) / len(metrics) if metrics else 0.0  # Return average

async def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save processed data to database.
    
    Args:
        data: List of records to save
    """
    with get_db_session() as session:
        for record in data:
            session.execute(text("INSERT INTO records (id, payload) VALUES (:id, :payload)"),
                           {'id': record['id'], 'payload': json.dumps(record['data'])})  # Insert record

class ManufacturingLLM:
    """Main orchestrator class for manufacturing LLM tasks.
    
    Methods:
        run: Execute the core workflow
    """

    async def run(self, input_data: List[Dict[str, Any]]) -> None:
        """Run the complete workflow.
        
        Args:
            input_data: List of input data dictionaries
        """
        logger.info("Starting the LLM processing workflow.")  # Log start
        try:
            await validate_input(input_data)  # Validate input
            sanitized_data = sanitize_fields(input_data)  # Sanitize fields
            transformed_data = await transform_records(sanitized_data)  # Transform records
            await process_batch(transformed_data)  # Process records
            await save_to_db(transformed_data)  # Save records to DB
            logger.info("Workflow completed successfully.")  # Log completion
        except Exception as e:
            logger.error(f"Workflow failed: {e}")  # Log failure

if __name__ == '__main__':
    # Example usage
    llm = ManufacturingLLM()  # Instantiate orchestrator
    input_example = [{"id": "1", "payload": "{"key":"value"}"}, {"id": "2", "payload": "{"key":"value2"}"}]
    import asyncio
    asyncio.run(llm.run(input_example))  # Run the workflow