Evaluate Fine-Tuned Factory LLMs with Structured Output Validation using Axolotl and Instructor

                      
                     
"""
Production implementation for evaluating fine-tuned factory LLMs with structured output validation.
Provides secure, scalable operations while utilizing Axolotl and Instructor.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
from contextlib import contextmanager

# Setup logger for monitoring
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class to handle environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///:memory:')
    api_endpoint: str = os.getenv('API_ENDPOINT', 'http://api.example.com')

@contextmanager
def db_connection_pool():
    """Context manager for managing database connections.
    
    Yields:
        connection: Database connection
    """
    connection = create_db_connection(Config.database_url)  # Create a connection
    try:
        yield connection  # Yield connection for use
    finally:
        connection.close()  # Ensure connection is closed

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

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields for safety.
    
    Args:
        data: Input data
    Returns:
        Dict[str, Any]: Sanitized data
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}  # Strip whitespaces
    return sanitized_data  # Return sanitized data

async def fetch_data(api_url: str) -> Dict[str, Any]:
    """Fetch data from API.
    
    Args:
        api_url: API URL to fetch data from
    Returns:
        Dict[str, Any]: Response data
    Raises:
        ConnectionError: If fetching fails
    """
    try:
        response = requests.get(api_url)  # Make the API request
        response.raise_for_status()  # Raise error for bad status
        return response.json()  # Return JSON response
    except requests.RequestException as e:
        logger.error(f'Fetching data failed: {e}')  # Log error
        raise ConnectionError('API request failed')  # Raise connection error

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for processing.
    
    Args:
        records: Input records to transform
    Returns:
        List[Dict[str, Any]]: Transformed records
    """
    transformed_records = []  # Initialize transformed records
    for record in records:
        transformed = {'id': record['id'], 'value': record['value'] * 2}  # Example transformation
        transformed_records.append(transformed)  # Append transformed record
    return transformed_records  # Return all transformed records

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of data.
    
    Args:
        data: Batch of data to process
    Raises:
        RuntimeError: If processing fails
    """
    try:
        # Simulate processing logic
        logger.info(f'Processing batch of size {len(data)}')  # Log batch size
        # Simulated processing delay
        time.sleep(1)  # Simulate processing time
    except Exception as e:
        logger.error(f'Processing failed: {e}')  # Log processing error
        raise RuntimeError('Batch processing failed')  # Raise runtime error

async def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, float]:
    """Aggregate metrics from processed data.
    
    Args:
        data: Processed data
    Returns:
        Dict[str, float]: Aggregated metrics
    """
    total = sum(record['value'] for record in data)  # Summing values
    average = total / len(data) if data else 0  # Calculate average
    return {'total': total, 'average': average}  # Return metrics dictionary

async def save_to_db(connection: Any, data: List[Dict[str, Any]]) -> None:
    """Save processed data to the database.
    
    Args:
        connection: Database connection
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    for record in data:
        # Insert each record into the database
        logger.info(f'Saving record: {record}')  # Log saving action
        # Simulated save operation
        connection.execute('INSERT INTO records VALUES (?, ?)', (record['id'], record['value']))
    logger.info('All records saved successfully')  # Log success

class LLMsEvaluator:
    """Main class for evaluating LLMs.
    
    Methods:
        evaluate: Evaluate LLMs using the defined workflow
    """

    def __init__(self):
        self.config = Config()  # Load configuration

    async def evaluate(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Evaluate LLMs with the provided input data.
        
        Args:
            input_data: Data for evaluation
        Returns:
            Dict[str, Any]: Evaluation results
        """
        try:
            await validate_input(input_data)  # Validate input data
            sanitized_data = await sanitize_fields(input_data)  # Sanitize data
            raw_records = await fetch_data(self.config.api_endpoint)  # Fetch data
            transformed_records = await transform_records(raw_records)  # Transform records
            await process_batch(transformed_records)  # Process data
            metrics = await aggregate_metrics(transformed_records)  # Aggregate metrics
            # Save results to database
            with db_connection_pool() as conn:
                await save_to_db(conn, transformed_records)  # Save to DB
            return metrics  # Return aggregated metrics
        except Exception as e:
            logger.error(f'Error in evaluation: {e}')  # Log error
            return {'error': str(e)}  # Return error message

if __name__ == '__main__':
    evaluator = LLMsEvaluator()  # Create evaluator instance
    input_data = {'input_data': {'id': 1, 'value': 10}}  # Example input data
    result = evaluator.evaluate(input_data)  # Evaluate
    print(result)  # Print results