使用连接池

我来为您提供OpenClaw的速度优化方案,OpenClaw作为Claude API客户端，优化主要涉及网络请求、并发处理和代码逻辑等方面。

网络请求优化

连接池和会话复用

import aiohttp
import asyncio
from functools import lru_cache
class OptimizedOpenClaw:
    def __init__(self):
        self.connector = aiohttp.TCPConnector(
            limit=100,  # 最大连接数
            limit_per_host=20,  # 每主机最大连接
            ttl_dns_cache=300,  # DNS缓存时间
            force_close=False  # 保持连接
        )
        self.timeout = aiohttp.ClientTimeout(total=30)
    async def make_request(self, url, data):
        async with aiohttp.ClientSession(
            connector=self.connector,
            timeout=self.timeout
        ) as session:
            async with session.post(url, json=data) as response:
                return await response.json()

HTTP/2支持

import httpx
# 使用支持HTTP/2的客户端
async_client = httpx.AsyncClient(
    http2=True,  # 启用HTTP/2
    limits=httpx.Limits(
        max_connections=100,
        max_keepalive_connections=20
    ),
    timeout=30.0
)

并发处理优化

批量请求处理

import asyncio
from typing import List
class BatchProcessor:
    def __init__(self, batch_size=10, max_concurrent=5):
        self.batch_size = batch_size
        self.semaphore = asyncio.Semaphore(max_concurrent)
    async def process_batch(self, prompts: List[str]):
        """批量处理提示词"""
        results = []
        # 分批处理
        for i in range(0, len(prompts), self.batch_size):
            batch = prompts[i:i + self.batch_size]
            batch_results = await asyncio.gather(
                *[self._process_with_semaphore(prompt) 
                  for prompt in batch],
                return_exceptions=True
            )
            results.extend(batch_results)
        return results
    async def _process_with_semaphore(self, prompt):
        """使用信号量控制并发"""
        async with self.semaphore:
            return await self._call_api(prompt)

异步流式响应

async def stream_response(prompt):
    """流式处理响应，减少等待时间"""
    async with aiohttp.ClientSession() as session:
        async with session.post(
            API_URL,
            json={"prompt": prompt, "stream": True},
            headers={"Accept": "text/event-stream"}
        ) as response:
            buffer = ""
            async for line in response.content:
                line = line.decode('utf-8').strip()
                if line.startswith('data: '):
                    data = line[6:]
                    if data == "[DONE]":
                        break
                    # 实时处理数据
                    yield self._process_chunk(data)

缓存优化

多级缓存策略

import redis
from functools import lru_cache
import hashlib
import pickle
class MultiLevelCache:
    def __init__(self):
        # 内存缓存（LRU）
        self.memory_cache = {}
        self.memory_size = 1000
        # Redis缓存（可选）
        try:
            self.redis_client = redis.Redis(
                host='localhost', port=6379,
                decode_responses=False
            )
        except:
            self.redis_client = None
    def _get_cache_key(self, prompt, **kwargs):
        """生成缓存键"""
        content = f"{prompt}_{str(kwargs)}"
        return hashlib.md5(content.encode()).hexdigest()
    @lru_cache(maxsize=1000)
    async def get_cached_response(self, prompt, **kwargs):
        """获取缓存响应"""
        cache_key = self._get_cache_key(prompt, **kwargs)
        # 1. 检查内存缓存
        if cache_key in self.memory_cache:
            return self.memory_cache[cache_key]
        # 2. 检查Redis缓存
        if self.redis_client:
            cached = self.redis_client.get(cache_key)
            if cached:
                result = pickle.loads(cached)
                # 回填到内存缓存
                self.memory_cache[cache_key] = result
                return result
        # 3. 调用API
        result = await self._call_api(prompt, **kwargs)
        # 缓存结果
        self.memory_cache[cache_key] = result
        if self.redis_client:
            self.redis_client.setex(
                cache_key, 
                3600,  # 1小时过期
                pickle.dumps(result)
            )
        return result

代码逻辑优化

预处理优化

import re
from typing import List
class PromptOptimizer:
    @staticmethod
    def preprocess_prompts(prompts: List[str]) -> List[str]:
        """预处理提示词，提高效率"""
        optimized = []
        for prompt in prompts:
            # 移除多余空格
            prompt = re.sub(r'\s+', ' ', prompt.strip())
            # 合并相似提示词（可选）
            # 可以添加基于语义的合并逻辑
            optimized.append(prompt)
        return optimized
    @staticmethod
    def deduplicate_prompts(prompts: List[str]) -> List[str]:
        """去重提示词"""
        seen = set()
        unique = []
        for prompt in prompts:
            prompt_hash = hash(prompt)
            if prompt_hash not in seen:
                seen.add(prompt_hash)
                unique.append(prompt)
        return unique

响应后处理优化

import json
from concurrent.futures import ThreadPoolExecutor
class ResponseProcessor:
    def __init__(self):
        self.executor = ThreadPoolExecutor(max_workers=4)
    async def process_responses_batch(self, responses: List[dict]):
        """批量后处理响应"""
        loop = asyncio.get_event_loop()
        # 将CPU密集型任务放到线程池
        processed = await loop.run_in_executor(
            self.executor,
            self._batch_process,
            responses
        )
        return processed
    def _batch_process(self, responses):
        """CPU密集型后处理"""
        results = []
        for response in responses:
            # 提取关键信息
            result = {
                'content': response.get('content', ''),
                'tokens': response.get('usage', {}).get('total_tokens', 0),
                'timestamp': response.get('created_at', '')
            }
            results.append(result)
        return results

配置优化

优化API参数

class OptimizedAPIConfig:
    # 调整请求参数以获得更好性能
    OPTIMAL_CONFIG = {
        "model": "claude-3-opus-20240229",
        "max_tokens": 1000,  # 根据实际需要调整
        "temperature": 0.7,
        "top_p": 0.9,
        "stream": False,  # 非流式模式通常更快
    }
    @classmethod
    def get_optimal_params(cls, use_case):
        """根据用例获取最优参数"""
        configs = {
            "summarization": {
                "max_tokens": 300,
                "temperature": 0.3
            },
            "creative_writing": {
                "max_tokens": 1500,
                "temperature": 0.9
            },
            "coding": {
                "max_tokens": 2000,
                "temperature": 0.2
            }
        }
        base = cls.OPTIMAL_CONFIG.copy()
        base.update(configs.get(use_case, {}))
        return base

监控和调优

性能监控

import time
import statistics
from dataclasses import dataclass
from typing import Dict
@dataclass
class PerformanceMetrics:
    response_times: list
    success_rate: float
    tokens_per_second: float
class PerformanceMonitor:
    def __init__(self):
        self.metrics: Dict[str, PerformanceMetrics] = {}
        self.start_times = {}
    async def track_performance(self, operation_id, coro):
        """跟踪操作性能"""
        start = time.time()
        self.start_times[operation_id] = start
        try:
            result = await coro
            duration = time.time() - start
            # 更新指标
            self._update_metrics(operation_id, duration, success=True)
            return result
        except Exception as e:
            duration = time.time() - start
            self._update_metrics(operation_id, duration, success=False)
            raise e
    def get_optimization_suggestions(self):
        """获取优化建议"""
        suggestions = []
        for op, metrics in self.metrics.items():
            avg_time = statistics.mean(metrics.response_times[-10:])
            if avg_time > 5.0:
                suggestions.append(
                    f"{op}: 响应时间过长 ({avg_time:.2f}s)，考虑增加并发或缓存"
                )
        return suggestions

部署优化建议

基础设施优化

# docker-compose.yml 优化配置
version: '3.8'
services:
  openclaw:
    build: .
    environment:
      - PYTHONUNBUFFERED=1
      - PYTHONOPTIMIZE=1
    deploy:
      resources:
        limits:
          cpus: '2'
          memory: 2G
        reservations:
          cpus: '1'
          memory: 1G
    # 使用更快的Python版本
    image: python:3.11-slim

异步任务队列（Celery + Redis）

from celery import Celery
from kombu import Exchange, Queue
# 配置Celery进行异步处理
app = Celery('openclaw',
             broker='redis://localhost:6379/0',
             backend='redis://localhost:6379/0')
# 优化队列配置
app.conf.task_queues = (
    Queue('high_priority', Exchange('high_priority'), routing_key='high'),
    Queue('low_priority', Exchange('low_priority'), routing_key='low'),
)
app.conf.task_routes = {
    'process_claude_request': {'queue': 'high_priority'},
    'batch_processing': {'queue': 'low_priority'},
}

1. 网络层面：使用连接池、HTTP/2、保持连接
2. 并发层面：合理控制并发数，使用信号量限制
3. 缓存策略：多级缓存（内存 + Redis）
4. 预处理：去重、合并相似请求
5. 参数调优：根据用例优化API参数
6. 监控调优：持续监控并调整配置

建议先实施网络和并发优化,这通常能带来最显著的性能提升，然后根据实际使用情况逐步添加缓存和其他优化措施。

标签： 连接池 连接管理