Validate Industrial LLM Outputs with DeepEval and LangChain

"""
Production implementation for validating industrial LLM outputs using DeepEval and LangChain.
Provides secure, scalable operations with comprehensive error handling and logging.
"""
import os
import logging
import time
from typing import Dict, Any, List
from langchain.llms import OpenAI
from deepeval import evaluate

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

class Config:
    """
    Configuration class for environment variables.
    """  
    def __init__(self):
        self.deepeval_api_key: str = os.getenv('DEEPEVAL_API_KEY')
        self.openai_api_key: str = os.getenv('OPENAI_API_KEY')

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

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.
    
    Args:
        data: Input data
    Returns:
        Sanitized data
    """
    # Example sanitization process
    data['input_text'] = data['input_text'].strip()  # Remove leading/trailing spaces
    return data

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data as per requirements.
    
    Args:
        data: Input data
    Returns:
        Normalized data
    """
    # Placeholder for normalization logic
    data['input_text'] = data['input_text'].lower()  # Convert to lower case
    return data

async def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data for processing.
    
    Returns:
        List of records to process
    """
    # Simulating data fetching
    return [{'input_text': 'Sample text for evaluation'}]

async def evaluate_output(input_text: str) -> Dict[str, Any]:
    """Evaluate the LLM output using DeepEval.
    
    Args:
        input_text: Text to evaluate
    Returns:
        Evaluation results
    Raises:
        Exception: If evaluation fails
    """
    try:
        # Using DeepEval for evaluation
        result = evaluate(input_text)
        return result  # Return evaluation results
    except Exception as e:
        logger.error(f'Error evaluating output: {e}')  # Log error
        raise

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save evaluation results to database.
    
    Args:
        data: Data to save
    """
    # Placeholder for database saving logic
    logger.info('Saving data to database...')

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate evaluation metrics.
    
    Args:
        results: List of evaluation results
    Returns:
        Aggregated metrics
    """
    metrics = {'total': len(results), 'success': sum(1 for r in results if r['status'] == 'success')}
    return metrics  # Return aggregated metrics

class LLMValidator:
    """Main orchestrator class for validating LLM outputs.
    """
    def __init__(self):
        self.config = Config()  # Load configuration

    async def process_batch(self) -> None:
        """Process a batch of input data.
        """
        data_records = await fetch_data()  # Fetch data records
        for record in data_records:
            try:
                await validate_input(record)  # Validate input
                sanitized_record = await sanitize_fields(record)  # Sanitize fields
                normalized_record = await normalize_data(sanitized_record)  # Normalize data
                evaluation_result = await evaluate_output(normalized_record['input_text'])  # Evaluate output
                await save_to_db(evaluation_result)  # Save results
            except Exception as e:
                logger.error(f'Error processing record: {e}')  # Handle processing error

        logger.info('Batch processing complete.')  # Log completion

if __name__ == '__main__':
    import asyncio
    # Execute the main processing block
    validator = LLMValidator()  # Instantiate the validator
    asyncio.run(validator.process_batch())  # Run the batch processing