DeepSeek高阶用法：如何用MoE架构优化企业级AI应用？

def select_model(task_complexity, response_time_requirement):
    if task_complexity == "high" and response_time_requirement == "flexible":
        return "deepseek-r1"  # 复杂任务，对响应时间要求不高
    elif task_complexity == "medium":
        return "deepseek-chat"  # 中等复杂度任务
    else:
        return "deepseek-lite"  # 简单任务，需要快速响应

import asyncio
import time

class BatchProcessor:
    def __init__(self, model_name, batch_size=16, max_wait_time=0.5):
        self.model_name = model_name
        self.batch_size = batch_size
        self.max_wait_time = max_wait_time
        self.queue = []
        self.processing = False
    
    async def add_request(self, prompt):
        future = asyncio.Future()
        self.queue.append((prompt, future))
        
        if not self.processing:
            asyncio.create_task(self._process_batch())
        
        return await future
    
    async def _process_batch(self):
        self.processing = True
        
        while self.queue:
            batch_start_time = time.time()
            current_batch = []
            
            # 收集批次请求
            while self.queue and len(current_batch) < self.batch_size:
                if len(current_batch) > 0 and time.time() - batch_start_time > self.max_wait_time:
                    break
                current_batch.append(self.queue.pop(0))
            
            # 批量处理请求
            prompts = [item[0] for item in current_batch]
            results = await self._call_model_batch(prompts)
            
            # 返回结果
            for (_, future), result in zip(current_batch, results):
                future.set_result(result)
        
        self.processing = False
    
    async def _call_model_batch(self, prompts):
        # 实际的模型批量调用逻辑
        # 这里是示例实现
        return await asyncio.gather(*[self._call_model(prompt) for prompt in prompts])
    
    async def _call_model(self, prompt):
        # 单次模型调用
        # 实际实现中替换为DeepSeek API调用
        await asyncio.sleep(0.1)  # 模拟API调用延迟
        return f"Response to: {prompt}"

# 使用Hugging Face Transformers的量化功能
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

# 4位量化配置
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True
)

# 加载量化模型
model = AutoModelForCausalLM.from_pretrained(
    "deepseek-ai/deepseek-r1",
    quantization_config=quantization_config,
    device_map="auto"
)

import hashlib
import redis
import json

class ResponseCache:
    def __init__(self, redis_url="redis://localhost:6379/0", ttl=3600):
        self.redis = redis.from_url(redis_url)
        self.ttl = ttl
    
    def get_cache_key(self, model, messages):
        # 生成唯一的缓存键
        message_str = json.dumps(messages, sort_keys=True)
        return f"deepseek:{model}:{hashlib.md5(message_str.encode()).hexdigest()}"
    
    def get_cached_response(self, model, messages):
        cache_key = self.get_cache_key(model, messages)
        cached = self.redis.get(cache_key)
        if cached:
            return json.loads(cached)
        return None
    
    def cache_response(self, model, messages, response):
        cache_key = self.get_cache_key(model, messages)
        self.redis.setex(
            cache_key,
            self.ttl,
            json.dumps(response)
        )