大模型API标注响应延迟过高如何优化？

Question

在调用大模型API进行数据标注时，常出现响应延迟过高的问题，尤其在批量处理大量文本时更为显著。常见表现为请求排队时间长、首字节返回慢、整体吞吐量低。该问题可能源于请求频繁触发限流、输入文本过长未分块、同步阻塞调用方式导致资源闲置，或未启用流式响应与并发处理机制。如何通过合理设计请求粒度、引入异步批处理、缓存重复内容及优化网络链路来有效降低标注延迟？

Answer 1

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大模型API标注响应延迟优化方案

针对大模型API在数据标注过程中出现的响应延迟问题，以下提供系统性的优化策略：

1. 请求粒度优化

文本分块处理

def chunk_text(text, max_tokens=2048):
    """将长文本按最大token数分块"""
    chunks = []
    current_chunk = ""
    
    for sentence in text.split('。'):
        if len(current_chunk + sentence) <= max_tokens:
            current_chunk += sentence + '。'
        else:
            if current_chunk:
                chunks.append(current_chunk)
            current_chunk = sentence + '。'
    
    if current_chunk:
        chunks.append(current_chunk)
    
    return chunks

动态批次调整

import time
from collections import deque


class AdaptiveBatcher:
    def __init__(self, initial_batch_size=8):
        self.batch_size = initial_batch_size
        self.latency_history = deque(maxlen=10)
    
    def adjust_batch_size(self, avg_latency):
        if avg_latency > 5.0 and self.batch_size > 1:
            self.batch_size = max(1, self.batch_size // 2)
        elif avg_latency < 1.0:
            self.batch_size = min(32, self.batch_size * 2)

2. 异步与并发处理

异步请求实现

import asyncio
import aiohttp
from typing import List, Dict


async def batch_api_requests(api_endpoint: str, texts: List[str], 
                           batch_size: int = 8, max_concurrent: int = 10):
    """异步批量处理API请求"""
    semaphore = asyncio.Semaphore(max_concurrent)
    
    async def process_single(text):
        async with semaphore:
            async with aiohttp.ClientSession() as session:
                payload = {"text": text, "max_tokens": 512}
                async with session.post(api_endpoint, json=payload) as response:
                    return await response.json()
    
    # 分批处理
    batches = [texts[i:i + batch_size] for i in range(0, len(texts), batch_size)]
    
    results = []
    for batch in batches:
        tasks = [process_single(text) for text in batch]
        batch_results = await asyncio.gather(*tasks, return_exceptions=True)
        results.extend(batch_results)
    
    return results

3. 缓存机制优化

语义缓存实现

import hashlib
import pickle
from functools import lru_cache
from sentence_transformers import SentenceTransformer


class SemanticCache:
    def __init__(self, model_name='all-MiniLM-L6-v2', similarity_threshold=0.95):
        self.encoder = SentenceTransformer(model_name)
        self.cache = {}
        self.similarity_threshold = similarity_threshold
    
    def get_cache_key(self, text):
        """生成文本的语义哈希键"""
        embedding = self.encoder.encode(text)
        return hashlib.md5(embedding.tobytes()).hexdigest()
    
    def get_similar_response(self, text):
        """基于语义相似度查找缓存响应"""
        cache_key = self.get_cache_key(text)
        
        if cache_key in self.cache:
            return self.cache[cache_key]
        
        # 查找相似文本的响应
        new_embedding = self.encoder.encode(text)
        for cached_key, (cached_embedding, response) in self.cache.items():
            similarity = cosine_similarity(new_embedding, cached_embedding)
            if similarity > self.similarity_threshold:
                return response
        
        return None
    
    def set_response(self, text, response):
        """缓存文本响应"""
        cache_key = self.get_cache_key(text)
        embedding = self.encoder.encode(text)
        self.cache[cache_key] = (embedding, response)

4. 流式响应处理

流式API调用

import requests
import json


def stream_api_response(api_endpoint: str, text: str, chunk_size: int = 1024):
    """使用流式响应处理长文本"""
    headers = {
        'Content-Type': 'application/json',
        'Accept': 'text/event-stream'
    }
    
    payload = {
        "text": text,
        "stream": True,
        "max_tokens": 4096
    }
    
    response = requests.post(api_endpoint, json=payload, headers=headers, stream=True)
    
    for line in response.iter_lines():
        if line:
            decoded_line = line.decode('utf-8')
            if decoded_line.startswith('data: '):
                try:
                    data = json.loads(decoded_line[6:])
                    yield data.get('text', '')
                except json.JSONDecodeError:
                    continue

5. 网络与连接优化

连接池配置

import aiohttp
import asyncio


class OptimizedAPIClient:
    def __init__(self, base_url, max_connections=100):
        self.base_url = base_url
        connector = aiohttp.TCPConnector(
            limit=max_connections,
            limit_per_host=20,
            keepalive_timeout=30
        )
        self.session = aiohttp.ClientSession(connector=connector)
    
    async def make_request(self, endpoint, data):
        """使用优化连接的请求方法"""
        url = f"{self.base_url}/{endpoint}"
        async with self.session.post(url, json=data, timeout=aiohttp.ClientTimeout(total=30)) as response:
            return await response.json()

6. 监控与自适应调整

延迟监控系统

import time
import logging
from dataclasses import dataclass
from statistics import mean, median


@dataclass
class PerformanceMetrics:
    p50_latency: float
    p95_latency: float
    throughput: float
    error_rate: float


class APIMonitor:
    def __init__(self):
        self.latencies = []
        self.errors = 0
        self.requests = 0
    
    def record_latency(self, latency):
        self.latencies.append(latency)
        self.requests += 1
        
        # 保持最近1000个样本
        if len(self.latencies) > 1000:
            self.latencies = self.latencies[-1000:]
    
    def record_error(self):
        self.errors += 1
        self.requests += 1
    
    def get_metrics(self) -> PerformanceMetrics:
        if not self.latencies:
            return PerformanceMetrics(0, 0, 0, 0)
        
        sorted_latencies = sorted(self.latencies)
        p50 = median(sorted_latencies)
        p95 = sorted_latencies[int(len(sorted_latencies) * 0.95)]
        
        error_rate = self.errors / max(self.requests, 1)
        throughput = len(self.latencies) / 60  # 假设按分钟计算
        
        return PerformanceMetrics(p50, p95, throughput, error_rate)

7. 综合优化策略

完整优化流程

class OptimizedAnnotationPipeline:
    def __init__(self, api_endpoint, cache_enabled=True, max_concurrent=20):
        self.api_endpoint = api_endpoint
        self.cache = SemanticCache() if cache_enabled else None
        self.monitor = APIMonitor()
        self.batcher = AdaptiveBatcher()
        self.client = OptimizedAPIClient(api_endpoint)
    
    async def annotate_texts(self, texts: List[str]) -> List[Dict]:
        """优化的文本标注流程"""
        results = []
        uncached_texts = []
        
        # 缓存查找
        for text in texts:
            if self.cache:
                cached_result = self.cache.get_similar_response(text)
                if cached_result:
                    results.append(cached_result)
                else:
                    uncached_texts.append(text)
            else:
                uncached_texts.append(text)
        
        # 批量处理未命中缓存的文本
        if uncached_texts:
            batch_size = self.batcher.batch_size
            batches = [uncached_texts[i:i + batch_size] 
                      for i in range(0, len(uncached_texts), batch_size)]
            
            for batch in batches:
                start_time = time.time()
                
                try:
                    batch_results = await asyncio.gather(
                        *[self.client.make_request('annotate', {'text': text}) 
                          for text in batch],
                        return_exceptions=True
                    )
                    
                    latency = time.time() - start_time
                    self.monitor.record_latency(latency)
                    
                    # 更新缓存
                    for text, result in zip(batch, batch_results):
                        if not isinstance(result, Exception) and self.cache:
                            self.cache.set_response(text, result)
                        results.append(result)
                    
                    # 自适应调整批次大小
                    metrics = self.monitor.get_metrics()
                    self.batcher.adjust_batch_size(metrics.p95_latency)
                    
                except Exception as e:
                    self.monitor.record_error()
                    logging.error(f"Batch processing failed: {e}")
        
        return results

关键优化要点总结

1. 请求分块: 将长文本分割为适当大小的块，避免单次请求过大
2. 异步并发: 使用异步IO和连接池提高资源利用率
3. 智能缓存: 基于语义相似度的缓存机制减少重复计算
4. 流式处理: 对大响应使用流式接收，降低首字节时间
5. 自适应调整: 基于性能指标动态调整批次大小和并发数
6. 监控告警: 实时监控延迟指标，及时发现性能瓶颈

通过上述综合优化策略，可显著降低大模型API标注的响应延迟，提高整体处理吞吐量。