Serve 100B-Parameter Industrial LLMs on CPU-GPU Factory Nodes with KTransformers and FastAPI

"""
Production implementation for serving 100B-Parameter Industrial LLMs.
Provides secure, scalable operations using FastAPI and KTransformers.
"""
from typing import Dict, Any, List, Union
import os
import logging
import asyncio
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, ValidationError
from transformers import pipeline

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

# Configuration class for environment variables
class Config:
    model_name: str = os.getenv('MODEL_NAME', 'gpt-3.5-turbo')
    max_length: int = int(os.getenv('MAX_LENGTH', 1024))

# Instantiate FastAPI app
app = FastAPI()

# Validate input data model
class InputData(BaseModel):
    prompt: str
    max_tokens: int = 50  # Default token limit

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data.
    
    Args:
        data: Input data to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'prompt' not in data:
        raise ValueError('Missing prompt in input data')  # Check for required fields
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Dict[str, Any]: Sanitized data
    """
    # Implement sanitization logic (e.g., escaping special characters)
    return data

async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform records for processing.
    
    Args:
        data: Input data to transform
    Returns:
        Dict[str, Any]: Transformed data
    """
    data['max_tokens'] = min(data['max_tokens'], Config.max_length)
    return data

async def process_batch(data: List[Dict[str, Any]]) -> List[str]:
    """Process a batch of requests to the LLM.
    
    Args:
        data: Batch of input data
    Returns:
        List[str]: Responses from the LLM
    """
    responses = []
    model = pipeline('text-generation', model=Config.model_name)
    for item in data:
        response = model(item['prompt'], max_length=item['max_tokens'])
        responses.append(response[0]['generated_text'])  # Get generated text
    return responses

async def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from an external source (e.g., DB or API).
    
    Returns:
        List[Dict[str, Any]]: Fetched data
    """
    return []  # Placeholder for actual data fetching logic

async def save_to_db(results: List[Dict[str, Any]]) -> None:
    """Save results to the database.
    
    Args:
        results: Results to save
    """
    # Implement DB saving logic here (e.g., using SQLAlchemy)
    pass

async def handle_errors(error: Exception) -> None:
    """Handle errors and log them appropriately.
    
    Args:
        error: Exception to handle
    """
    logger.error(f'Error occurred: {error}')  # Log the error details

@app.post('/generate/')
async def generate(input_data: InputData) -> Dict[str, Any]:
    """Endpoint to generate text using the LLM.
    
    Args:
        input_data: Data input from user
    Returns:
        Dict[str, Any]: Generated text
    """
    try:
        # Validate and sanitize input
        data = input_data.dict()  # Convert Pydantic model to dict
        await validate_input(data)
        data = await sanitize_fields(data)
        data = await transform_records(data)
        # Process batch and return results
        result = await process_batch([data])
        return {'result': result}
    except ValidationError as ve:
        logger.warning(f'Validation error: {ve}')  # Log validation errors
        raise HTTPException(status_code=422, detail=str(ve))  # Return validation error
    except Exception as e:
        await handle_errors(e)  # Handle other errors
        raise HTTPException(status_code=500, detail='Internal Server Error')  # Generic error response

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