Trace Industrial AI Inference Latency with LangFuse and vLLM

"""
Production implementation for tracing AI inference latency.
Provides secure, scalable operations using LangFuse and vLLM.
"""

from typing import Dict, Any, List
import os
import logging
import httpx
import asyncio
from pydantic import BaseModel, ValidationError

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    langfuse_api_url: str = os.getenv('LANGFUSE_API_URL')
    database_url: str = os.getenv('DATABASE_URL')

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 'id' not in data:
        raise ValueError('Missing id')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection.

    Args:
        data: Raw input data
    Returns:
        Sanitized data
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    return sanitized_data

async def fetch_data(endpoint: str) -> Dict[str, Any]:
    """Fetch data from the LangFuse API.

    Args:
        endpoint: API endpoint to fetch data from
    Returns:
        Response data as a dictionary
    Raises:
        Exception: If API call fails
    """
    try:
        async with httpx.AsyncClient() as client:
            response = await client.get(f'{Config.langfuse_api_url}/{endpoint}')
            response.raise_for_status()  # Raise error for bad responses
            return response.json()
    except httpx.HTTPStatusError as exc:
        logger.error(f'HTTP error occurred: {exc}')
        raise
    except Exception as exc:
        logger.error(f'An error occurred: {exc}')
        raise

async def transform_records(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform raw data into the desired format.

    Args:
        data: List of raw data records
    Returns:
        Transformed data records
    """
    transformed = []  # List to hold transformed records
    for record in data:
        transformed.append({
            'id': record['id'],
            'inference_time': record['inference_time'],
            'timestamp': record['timestamp']
        })  # Simplifying the structure
    return transformed

async def save_to_db(records: List[Dict[str, Any]]) -> None:
    """Save transformed records to the database.

    Args:
        records: List of records to save
    Raises:
        Exception: If database operation fails
    """
    try:
        # Simulate database save operation
        logger.info(f'Saving {len(records)} records to the database.')
        await asyncio.sleep(1)  # Simulate async DB operation
    except Exception as exc:
        logger.error(f'Database save failed: {exc}')
        raise

async def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from data records.

    Args:
        data: List of data records
    Returns:
        Aggregated metrics as a dictionary
    """
    total_latency = sum(record['inference_time'] for record in data)
    avg_latency = total_latency / len(data) if data else 0
    return {'total_latency': total_latency, 'avg_latency': avg_latency}

async def handle_errors(func):
    """Decorator for handling errors in async functions.

    Args:
        func: Async function to wrap
    """
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in {func.__name__}: {e}')
            return None  # Graceful degradation
    return wrapper

class InferenceTracer:
    """Main class for tracing AI inference latency.
    """

    def __init__(self, data: Dict[str, Any]):
        self.data = data  # Input data for processing

    async def run(self):
        """Run the inference tracing workflow.
        """
        await validate_input(self.data)  # Validate input data
        sanitized_data = await sanitize_fields(self.data)  # Sanitize input
        raw_data = await fetch_data('inference_data')  # Fetch raw data
        transformed_data = await transform_records(raw_data)  # Transform data
        await save_to_db(transformed_data)  # Save to database
        metrics = await aggregate_metrics(transformed_data)  # Aggregate metrics
        logger.info(f'Metrics: {metrics}')  # Log metrics

if __name__ == '__main__':
    # Example usage
    input_data = {'id': '12345'}
    tracer = InferenceTracer(input_data)
    asyncio.run(tracer.run())  # Run the inference tracer asynchronously