Stream Inference Traces from Production Industrial Models with LangFuse and KServe

"""
Production implementation for streaming inference traces from industrial models using LangFuse and KServe.
Provides secure, scalable operations with efficient logging and error handling.
"""
from typing import Dict, Any, List
import os
import logging
import requests
import time

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

class Config:
    """
    Configuration class for environment variables.
    """
    KERVE_URL: str = os.getenv('KERVE_URL')  # URL for KServe
    LANGFUSE_URL: str = os.getenv('LANGFUSE_URL')  # URL for LangFuse
    RETRY_LIMIT: int = 5  # Max retries for API calls

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 'model_id' not in data or 'payload' not in data:
        raise ValueError('Missing model_id or payload in input data')  # Ensure essential fields are present
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize fields in the input data.
    
    Args:
        data: Raw input data
    Returns:
        Sanitized data
    """
    # Example sanitation process
    sanitized_data = {key: str(value).strip() for key, value in data.items()}
    return sanitized_data

async def fetch_data(url: str, payload: Dict[str, Any]) -> Dict[str, Any]:
    """Fetch data from KServe or LangFuse.
    
    Args:
        url: API endpoint
        payload: Data to send
    Returns:
        Response data
    Raises:
        Exception: If API call fails
    """
    for attempt in range(Config.RETRY_LIMIT):
        try:
            response = requests.post(url, json=payload)
            response.raise_for_status()  # Raise error for bad responses
            return response.json()
        except requests.exceptions.RequestException as e:
            logger.warning(f'Attempt {attempt + 1} failed: {e}')  # Log failed attempts
            time.sleep(2 ** attempt)  # Exponential backoff
    raise Exception('Max retries exceeded for fetching data')

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of data.
    
    Args:
        data: List of input records
    Returns:
        List of processed records
    """
    processed_results = []
    for record in data:
        # Validate input for each record
        await validate_input(record)
        sanitized_record = await sanitize_fields(record)
        response = await fetch_data(Config.KERVE_URL, sanitized_record)
        processed_results.append(response)
    return processed_results

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed results.
    
    Args:
        results: List of results from KServe
    Returns:
        Aggregated metrics
    """
    # Example aggregation logic
    metrics = {'success': 0, 'failure': 0}
    for result in results:
        if result.get('status') == 'success':
            metrics['success'] += 1
        else:
            metrics['failure'] += 1
    return metrics

async def format_output(results: List[Dict[str, Any]], metrics: Dict[str, Any]) -> Dict[str, Any]:
    """Format the output for the response.
    
    Args:
        results: Processed results
        metrics: Aggregated metrics
    Returns:
        Formatted output
    """
    return {'results': results, 'metrics': metrics}

class InferenceProcessor:
    """Main orchestrator for streaming inference traces.
    """
    async def run_inference(self, input_data: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Run the inference process on the input data.
        
        Args:
            input_data: List of input records
        Returns:
            Formatted output with metrics
        """
        try:
            processed_results = await process_batch(input_data)
            metrics = await aggregate_metrics(processed_results)
            output = await format_output(processed_results, metrics)
            return output
        except Exception as e:
            logger.error(f'Error during inference: {e}')  # Log errors
            return {'error': str(e)}

if __name__ == '__main__':
    # Example usage
    import asyncio
    example_data = [
        {'model_id': 'model_1', 'payload': {'input': [1, 2, 3]}},
        {'model_id': 'model_2', 'payload': {'input': [4, 5, 6]}}
    ]
    processor = InferenceProcessor()
    result = asyncio.run(processor.run_inference(example_data))
    print(result)  # Output the results