Merge and Evaluate Domain-Adapted Manufacturing LLMs with MergeKit and PEFT

"""
Production implementation for merging and evaluating domain-adapted manufacturing LLMs using MergeKit and PEFT.
Provides secure, scalable operations with logging and error handling.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import requests
import time
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')

# Create a connection pool for the database
engine = create_engine(Config.database_url)
Session = sessionmaker(bind=engine)

def validate_input_data(data: Dict[str, Any]) -> bool:
    """Validate input data for merging and evaluating LLMs.
    
    Args:
        data: Input data to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """  
    if 'model_id' not in data:
        raise ValueError('Missing model_id in input data')
    if 'parameters' not in data or not isinstance(data['parameters'], dict):
        raise ValueError('Parameters must be a dictionary')
    return True

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

def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform raw records to standardize format.
    
    Args:
        records: Raw input records
    Returns:
        List: Transformed records
    """  
    return [{"id": rec['model_id'], "params": rec['parameters']} for rec in records]

def fetch_data(model_id: str) -> Dict[str, Any]:
    """Fetch model data from external API.
    
    Args:
        model_id: Identifier for the model
    Returns:
        Dict: Model data
    Raises:
        Exception: If API call fails
    """  
    try:
        response = requests.get(f"{Config.api_url}/models/{model_id}")
        response.raise_for_status()
        return response.json()
    except requests.RequestException as e:
        logger.error(f"Error fetching data for model_id {model_id}: {e}")
        raise Exception('Failed to fetch model data')

def save_to_db(data: Dict[str, Any]) -> None:
    """Save data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If database operation fails
    """  
    session = Session()
    try:
        session.execute(text("INSERT INTO models (id, params) VALUES (:id, :params)"),
                       {'id': data['id'], 'params': data['params']})
        session.commit()
    except Exception as e:
        session.rollback()
        logger.error(f"Error saving data to DB: {e}")
        raise Exception('Database operation failed')
    finally:
        session.close()  # Ensure session is closed

def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate evaluation metrics from results.
    
    Args:
        results: List of evaluation results
    Returns:
        Dict: Aggregated metrics
    """  
    metrics = {"accuracy": 0.0, "count": 0}
    for result in results:
        metrics['accuracy'] += result['accuracy']
        metrics['count'] += 1
    metrics['accuracy'] /= metrics['count'] if metrics['count'] > 0 else 1
    return metrics

def handle_errors(func):
    """Decorator to handle errors in function calls.
    
    Args:
        func: Function to decorate
    Returns:
        Wrapped function with error handling
    """  
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            logger.error(f"Error in function {func.__name__}: {e}")
            return None
    return wrapper

class ManufacturingLLMOrchestrator:
    """Main orchestrator for managing LLM merging and evaluation.
    """  
    def __init__(self):
        self.results = []

    @handle_errors
    def process_batch(self, batch: List[Dict[str, Any]]) -> None:
        """Process a batch of models for merging and evaluation.
        
        Args:
            batch: List of models to process
        """  
        for model in batch:
            validated_data = validate_input_data(model)
            sanitized_data = sanitize_fields(validated_data)
            model_data = fetch_data(sanitized_data['model_id'])
            self.results.append(model_data)

    @handle_errors
    def evaluate_models(self) -> Dict[str, Any]:
        """Evaluate models and aggregate metrics.
        
        Returns:
            Dict: Aggregated evaluation metrics
        """  
        return aggregate_metrics(self.results)

if __name__ == '__main__':
    # Example usage of the orchestrator
    orchestrator = ManufacturingLLMOrchestrator()
    sample_batch = [
        {'model_id': 'model_1', 'parameters': {'param1': 'value1', 'param2': 'value2'}},
        {'model_id': 'model_2', 'parameters': {'param1': 'value1', 'param2': 'value2'}}
    ]
    orchestrator.process_batch(sample_batch)
    metrics = orchestrator.evaluate_models()
    print(f"Aggregated Metrics: {metrics}")