Evaluate Fine-Tuned Industrial LLM Outputs with deepeval and LlamaIndex

"""
Production implementation for evaluating fine-tuned industrial LLM outputs.
Utilizes deepeval and LlamaIndex for seamless evaluation of outputs.
"""
from typing import Dict, Any, List
import os
import logging
import requests
from time import sleep

# Configure logging for the application
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    # Load environment variables
    deepeval_api_url: str = os.getenv('DEEPEVAL_API_URL')
    llamaindex_api_url: str = os.getenv('LLAMAINDEX_API_URL')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))
    retry_delay: float = float(os.getenv('RETRY_DELAY', 1.0))

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for evaluation.
    
    Args:
        data: Dictionary containing LLM output to evaluate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'output' not in data:
        raise ValueError('Missing required field: output')  # Ensure the output field is present
    return True

async 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[str, Any]: Sanitized data
    """
    # For demonstration, simply return the same data
    return {k: str(v).strip() for k, v in data.items()}  # Strip whitespace from fields

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data for consistency in evaluation.
    
    Args:
        data: Raw input data
    Returns:
        Dict[str, Any]: Normalized data
    """
    # Normalize the output to lower case for uniformity
    data['output'] = data['output'].lower()
    return data

async def fetch_data(api_url: str, data: Dict[str, Any]) -> Any:
    """Fetch evaluation results from the DeepEval API.
    
    Args:
        api_url: URL of the API to call
        data: Data to send to the API
    Returns:
        Any: Response from the API
    Raises:
        ConnectionError: If the API call fails
    """
    try:
        response = requests.post(api_url, json=data)
        response.raise_for_status()  # Raise exception for HTTP errors
        return response.json()  # Return the JSON response
    except requests.exceptions.RequestException as e:
        logger.error(f'API request failed: {e}')  # Log the error
        raise ConnectionError('Failed to fetch data from API')  # Propagate the error

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save evaluation result to database (mock implementation).
    
    Args:
        data: Evaluation results to save
    Returns:
        None
    """
    logger.info('Saving data to database...')  # Log saving action
    # Mock: Print to console instead of actually saving
    print('Saved Data:', data)  # Replace with actual DB logic

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of input data for evaluation.
    
    Args:
        data: List of input dictionaries to evaluate
    Returns:
        List[Dict[str, Any]]: List of evaluation results
    """
    results = []
    for item in data:
        try:
            await validate_input(item)  # Validate input data
            sanitized_data = await sanitize_fields(item)  # Sanitize inputs
            normalized_data = await normalize_data(sanitized_data)  # Normalize data
            eval_result = await fetch_data(Config.deepeval_api_url, normalized_data)  # Fetch evaluation
            results.append(eval_result)  # Append result to results list
        except Exception as e:
            logger.error(f'Error processing {item}: {e}')  # Log the error
            continue  # Skip to next item in case of error
    return results  # Return all results

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, float]:
    """Aggregate evaluation metrics from results.
    
    Args:
        results: List of evaluation results
    Returns:
        Dict[str, float]: Aggregated metrics
    """
    # This is a simple aggregation example
    metrics = {'average_score': sum(r['score'] for r in results) / len(results)}  # Mock metric
    return metrics  # Return metrics

class EvaluationOrchestrator:
    """Class to orchestrate evaluation workflow.
    
    Attributes:
        input_data: List of inputs to evaluate
    """
    def __init__(self, input_data: List[Dict[str, Any]):
        self.input_data = input_data  # Store input data

    async def run_evaluation(self) -> None:
        """Run the evaluation workflow.
        
        Returns:
            None
        """
        try:
            results = await process_batch(self.input_data)  # Process the input data
            metrics = await aggregate_metrics(results)  # Aggregate metrics
            await save_to_db(metrics)  # Save metrics to DB
        except Exception as e:
            logger.error(f'Evaluation failed: {e}')  # Log evaluation failure

if __name__ == '__main__':
    # Example usage of the evaluation orchestrator
    input_data_example = [{'output': 'Example LLM output here.'}]  # Example input
    orchestrator = EvaluationOrchestrator(input_data_example)  # Create orchestrator
    import asyncio
    asyncio.run(orchestrator.run_evaluation())  # Run the evaluation asynchronously