Serve High-Throughput Factory LLMs with vLLM and BentoML

                      
                     
"""
Production implementation for serving high-throughput Factory LLMs using vLLM and BentoML.
Provides secure, scalable operations with optimized performance.
"""
from typing import Dict, Any, List
import os
import logging
import httpx
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, ValidationError
from time import sleep

# Setup logging to capture application behavior
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    vllm_model_url: str = os.getenv('VLLM_MODEL_URL')
    db_connection_string: str = os.getenv('DATABASE_URL')

class InputData(BaseModel):
    """
    Data model for input validation.
    """
    id: str
    payload: List[float]

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'id' not in data or 'payload' not in data:
        raise ValueError('Both id and payload must be provided.')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data
    Returns:
        Sanitized data
    """
    sanitized_data = {k: v for k, v in data.items() if isinstance(v, (str, list))}
    logger.debug(f'Sanitized data: {sanitized_data}')
    return sanitized_data

async def fetch_data(url: str) -> Any:
    """Fetch data from a given URL using HTTP.
    
    Args:
        url: URL to fetch from
    Returns:
        Response data
    Raises:
        HTTPException: If request fails
    """
    try:
        async with httpx.AsyncClient() as client:
            response = await client.get(url)
            response.raise_for_status()  # Raise an error for bad responses
            return response.json()
    except httpx.HTTPStatusError as e:
        logger.error(f'HTTP error: {e}')
        raise HTTPException(status_code=e.response.status_code, detail=str(e))

async def call_api(data: Dict[str, Any]) -> Any:
    """Call the LLM API with the provided data.
    
    Args:
        data: Input data
    Returns:
        LLM model output
    """
    url = Config.vllm_model_url
    logger.info(f'Calling LLM API at {url}')
    response = await fetch_data(url)
    return response

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of input data for LLM.
    
    Args:
        data: List of input data
    Returns:
        List of processed results
    """
    results = []
    for item in data:
        try:
            await validate_input(item)
            sanitized = await sanitize_fields(item)
            result = await call_api(sanitized)
            results.append(result)
        except Exception as e:
            logger.error(f'Error processing item {item}: {e}')
            results.append({'error': str(e)})
    return results

async def save_to_db(data: Dict[str, Any]) -> None:
    """Simulate saving processed data to the database.
    
    Args:
        data: Data to save
    """
    logger.info('Saving data to database...')
    # Simulate a delay for saving
    sleep(1)
    logger.info('Data saved successfully.')

app = FastAPI()

@app.post('/v1/predict')
async def predict(input_data: InputData):
    """Endpoint to handle prediction requests.
    
    Args:
        input_data: Input data model
    Returns:
        Prediction results
    """
    try:
        results = await process_batch([input_data.dict()])
        await save_to_db(input_data.dict())
        return {'results': results}
    except ValidationError as ve:
        logger.error(f'Validation error: {ve}')
        raise HTTPException(status_code=422, detail=ve.errors())
    except Exception as e:
        logger.exception('Unexpected error occurred.')
        raise HTTPException(status_code=500, detail='Internal Server Error')

if __name__ == '__main__':
    # Example usage and startup
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)