Trace Inference Pipeline Latency with vLLM and OpenTelemetry

                      
                     
"""
Production implementation for tracing inference pipeline latency with vLLM and OpenTelemetry.
Provides secure, scalable operations with monitoring capabilities.
"""

from typing import Dict, Any, List
import os
import logging
import time
import requests
from pydantic import BaseModel, ValidationError
from fastapi import FastAPI, HTTPException
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

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

# OpenTelemetry Configuration
resource = Resource.create({"service.name": "trace_latency_pipeline"})
trace.set_tracer_provider(TracerProvider(resource=resource))
tracer = trace.get_tracer(__name__)

# Configuration class for environment variables
class Config:
    vllm_url: str = os.getenv('VLLM_URL', 'http://localhost:8000')
    max_retries: int = int(os.getenv('MAX_RETRIES', '5'))

# Helper function to validate input data
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_data' not in data:
        raise ValueError('Missing input_data')
    return True

# Function to sanitize fields in data
def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: The input data to sanitize
    Returns:
        Sanitized data
    """
    return {key: str(value).strip() for key, value in data.items()}

# Function to transform records for processing
def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform input data for processing.
    
    Args:
        data: Data to transform
    Returns:
        Transformed data
    """
    return {"transformed_data": data['input_data'].upper()}

# Function to process a batch of records
async def process_batch(batch: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of records.
    
    Args:
        batch: List of records to process
    Returns:
        Processed records
    """
    results = []
    for record in batch:
        transformed = transform_records(record)
        results.append(transformed)
    return results

# Function to fetch data from vLLM
async def fetch_data(input_data: Dict[str, Any]) -> Dict[str, Any]:
    """Fetch data from vLLM.
    
    Args:
        input_data: Input data to send
    Returns:
        Response from vLLM
    Raises:
        HTTPException: If the request fails
    """
    for attempt in range(Config.max_retries):
        try:
            response = requests.post(Config.vllm_url, json=input_data)
            response.raise_for_status()  # Raise an error for bad responses
            return response.json()
        except requests.exceptions.RequestException as e:
            logger.warning(f'Fetch attempt {attempt + 1} failed: {e}')
            time.sleep(2 ** attempt)  # Exponential backoff
    raise HTTPException(status_code=503, detail='Service unavailable')

# Function to save data to the database (mocked)
async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    """
    # Here we would implement the database saving logic
    logger.info('Data saved to the database.')

# Function to format output for response
def format_output(data: Dict[str, Any]) -> Dict[str, Any]:
    """Format output for API response.
    
    Args:
        data: Data to format
    Returns:
        Formatted output
    """
    return {"status": "success", "data": data}

# Main class to orchestrate the pipeline
class TraceInferencePipeline:
    def __init__(self):
        self.config = Config()

    async def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        await validate_input(input_data)  # Validate input
        sanitized_data = sanitize_fields(input_data)  # Sanitize data
        fetched_data = await fetch_data(sanitized_data)  # Fetch data
        processed_data = await process_batch([fetched_data])  # Process data
        await save_to_db(processed_data)  # Save to DB
        return format_output(processed_data)  # Format output

# FastAPI application setup
app = FastAPI()

@app.post("/trace-inference")
async def trace_inference(input_data: Dict[str, Any]):
    """Endpoint to trace inference pipeline.
    
    Args:
        input_data: Input data for inference
    Returns:
        JSON response
    Raises:
        HTTPException: If processing fails
    """
    pipeline = TraceInferencePipeline()  # Create pipeline instance
    try:
        result = await pipeline.run(input_data)
        return result  # Return processed result
    except Exception as e:
        logger.error(f'Error occurred: {e}')
        raise HTTPException(status_code=500, detail='Internal server error')

if __name__ == '__main__':
    # If running as a script, start the FastAPI app
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)