Align Manufacturing Domain LLMs with RAG and Reinforcement Learning Feedback

                      
                     
"""
Production implementation for aligning manufacturing domain LLMs with RAG and reinforcement learning feedback.
Provides secure, scalable operations.
"""

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

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class to handle environment variables."""
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///manufacturing.db')

class Database:
    """Database connection pooling and session management."""
    def __init__(self, db_url: str):
        self.engine: Engine = create_engine(db_url, pool_size=10, max_overflow=20)
        self.session_factory = scoped_session(sessionmaker(bind=self.engine))

    def get_session(self):
        """Get a database session."""
        return self.session_factory()

    def close(self):
        """Close the database session factory."""
        self.session_factory.remove()

config = Config()  # Load configuration

# Initialize database connection
db = Database(config.database_url)

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for manufacturing LLM processing.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    # Ensure required fields are present
    if 'model_id' not in data:
        raise ValueError('Missing model_id')
    if 'feedback' not in data:
        raise ValueError('Missing feedback')  # Validate feedback
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input fields to sanitize
    Returns:
        Sanitized data
    """
    # Example sanitization: strip whitespace
    return {key: str(value).strip() for key, value in data.items()}

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data for processing.
    
    Args:
        data: Raw data to normalize
    Returns:
        Normalized data
    """
    # Placeholder normalization logic
    return {key: value.lower() for key, value in data.items()}

async def transform_records(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform list of records for model input.
    
    Args:
        data: List of raw records to transform
    Returns:
        Transformed records
    """
    transformed = []
    for record in data:
        transformed.append(await normalize_data(record))  # Normalize each record
    return transformed

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of data records.
    
    Args:
        data: List of records to process
    """
    # Simulate processing and logging results
    for record in data:
        logger.info(f'Processing record: {record}')  # Log each record processing
        # Placeholder for model inference

async def fetch_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetch data from an external API.
    
    Args:
        api_url: URL to fetch data from
    Returns:
        Fetched data
    Raises:
        Exception: If fetching fails
    """
    try:
        response = requests.get(api_url)
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()  # Return JSON data
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise

async def save_to_db(session, model_id: str, feedback: str) -> None:
    """Save feedback to the database.
    
    Args:
        session: Database session
        model_id: ID of the model
        feedback: Feedback to save
    """
    try:
        session.execute(text('INSERT INTO feedback (model_id, feedback) VALUES (:model_id, :feedback)'),
                       {'model_id': model_id, 'feedback': feedback})
        session.commit()  # Commit the transaction
    except Exception as e:
        logger.error(f'Error saving to database: {e}')
        session.rollback()  # Rollback on error

async def format_output(data: Any) -> str:
    """Format output data for presentation.
    
    Args:
        data: Data to format
    Returns:
        Formatted string output
    """
    return str(data)  # Simple string conversion

async def handle_errors(func):
    """Decorator to handle errors in async functions.
    
    Args:
        func: Async function to wrap
    Returns:
        Wrapped function
    """
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in {func.__name__}: {e}')
            return None  # Return None on error
    return wrapper

class ManufacturingLLM:
    """Main orchestrator for manufacturing LLM operations."""
    def __init__(self):
        self.db_session = db.get_session()  # Get a database session

    async def run(self, api_url: str):
        """Run the main workflow for processing LLM feedback.
        
        Args:
            api_url: URL to fetch model input data
        """
        try:
            raw_data = await fetch_data(api_url)  # Fetch data from API
            validated_data = await validate_input(raw_data)  # Validate data
            sanitized_data = await sanitize_fields(validated_data)  # Sanitize input
            processed_data = await transform_records([sanitized_data])  # Transform records
            await process_batch(processed_data)  # Process the batch
            await save_to_db(self.db_session, sanitized_data['model_id'], sanitized_data['feedback'])  # Save feedback
        except Exception as e:
            logger.error(f'Workflow failed: {e}')  # Log any errors
        finally:
            self.db_session.close()  # Ensure the session is closed

if __name__ == '__main__':
    # Example usage
    llm = ManufacturingLLM()
    api_url = 'http://example.com/api/models'
    import asyncio
    asyncio.run(llm.run(api_url))  # Run the main workflow