Deploy Disaggregated LLM Inference for Industrial AI with llm-d and vLLM

"""
Production implementation for deploying disaggregated LLM inference.
Provides secure and scalable operations for industrial AI applications.
"""
from typing import Dict, Any, List
import os
import logging
import httpx
from fastapi import FastAPI, HTTPException, Query
from pydantic import BaseModel, validator
from contextlib import asynccontextmanager
import asyncio

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

class Config:
    api_url: str = os.getenv('API_URL', 'http://localhost:8000/inference')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))
    timeout: int = int(os.getenv('TIMEOUT', 5))

class InputData(BaseModel):
    id: str
    input_text: str

    @validator('id')
    def validate_id(cls, v):
        if not v:
            raise ValueError('ID cannot be empty')
        return v

async def validate_input(data: InputData) -> None:
    """Validate request data.
    
    Args:
        data: Input to validate
    Raises:
        ValueError: If validation fails
    """
    if not data.input_text:
        raise ValueError('Input text cannot be empty')

async def fetch_data(data: InputData) -> Dict[str, Any]:
    """Fetch inference result from the LLM API.
    
    Args:
        data: Input data to send to the API
    Returns:
        API response as dictionary
    Raises:
        HTTPException: If API call fails
    """
    async with httpx.AsyncClient() as client:
        response = await client.post(Config.api_url, json=data.dict(), timeout=Config.timeout)
        response.raise_for_status()  # Raises HTTPError for bad responses
        return response.json()

async def save_to_db(data: Dict[str, Any]) -> None:
    """Simulate saving results to a database.
    
    Args:
        data: Result data to save
    """
    # Simulate saving process
    logger.info('Saving data to database: %s', data)
    await asyncio.sleep(0.1)  # Simulate delay

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from multiple results.
    
    Args:
        results: List of result dictionaries
    Returns:
        Aggregated metrics
    """
    metrics = {'count': len(results)}
    # Add more complex aggregation logic here if needed
    return metrics

async def process_batch(data: List[InputData]) -> List[Dict[str, Any]]:
    """Process a batch of input data.
    
    Args:
        data: List of input data to process
    Returns:
        List of processed result data
    """
    results = []
    for item in data:
        try:
            await validate_input(item)  # Validate input data
            result = await fetch_data(item)  # Fetch inference result
            await save_to_db(result)  # Save result to DB
            results.append(result)
        except Exception as e:
            logger.error('Error processing item %s: %s', item, str(e))
    return results

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Manage application lifespan for setup and teardown.
    """
    logger.info('Starting application...')
    yield
    logger.info('Shutting down application...')

app = FastAPI(lifespan=lifespan)

@app.post('/inference', response_model=List[Dict[str, Any]])
async def infer(data: List[InputData] = Query(...)) -> List[Dict[str, Any]]:
    """Endpoint for LLM inference.
    
    Args:
        data: List of input data for inference
    Returns:
        List of inference results
    Raises:
        HTTPException: If input validation fails
    """
    try:
        results = await process_batch(data)  # Process the input batch
        return results
    except Exception as e:
        logger.error('Inference error: %s', str(e))
        raise HTTPException(status_code=400, detail=str(e))

if __name__ == '__main__':
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)