OpenRouter与Claude API集成实战：代码生成与性能优化指南

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在直接调用 Claude API 进行代码生成时，开发者常遇到以下几个典型问题：

冷启动延迟：首次请求需要加载模型，响应时间可能达到 3 - 5 秒，影响用户体验
token 限制：单个请求默认限制 4096 tokens，复杂代码生成需要分多次请求
速率限制：免费版每分钟仅允许 20 次请求，容易触发 429 错误
结果不一致：相同 prompt 可能返回不同结果，缺乏确定性

REST 方案
优点：实现简单，兼容性强
缺点：每次请求都需要建立新连接，开销大
WebSocket 方案
优点：长连接减少握手开销，适合流式响应
缺点：需要维护连接状态，实现复杂度高

flowchart LR
    Client-->OpenRouter-->Claude_API
    OpenRouter-->Cache
    OpenRouter-->RateLimiter

关键组件说明：

请求路由：根据负载自动选择最优 API 端点
结果缓存：对相同 prompt 进行缓存，TTL 设置为 5 分钟
流量控制：基于令牌桶算法实现限流

import aiohttp
from typing import Optional, Dict, Any
import backoff
import logging

class ClaudeClient:
    def __init__(self, api_key: str):
        self.base_url = "https://openrouter.ai/api/v1"
        self.headers = {"Authorization": f"Bearer {api_key}",
            "HTTP-Referer": "https://yourdomain.com",
            "X-Title": "CodeGen Service"
        }

    @backoff.on_exception(backoff.expo, Exception, max_tries=3)
    async def generate_code(self, prompt: str, max_tokens: int = 1024) -> Optional[Dict[str, Any]]:
        payload = {
            "model": "anthropic/claude-2",
            "messages": [{"role": "user", "content": prompt}],
            "max_tokens": max_tokens
        }

        async with aiohttp.ClientSession() as session:
            try:
                async with session.post(f"{self.base_url}/chat/completions",
                    json=payload,
                    headers=self.headers
                ) as response:
                    if response.status == 200:
                        return await response.json()
                    logging.error(f"API Error: {response.status}")
            except Exception as e:
                logging.exception(f"Request failed: {str(e)}")
        return None

import asyncio
from typing import List

class BatchProcessor:
    def __init__(self, client: ClaudeClient, concurrency: int = 5):
        self.client = client
        self.semaphore = asyncio.Semaphore(concurrency)

    async def _process_single(self, prompt: str) -> str:
        async with self.semaphore:
            result = await self.client.generate_code(prompt)
            return result["choices"][0]["message"]["content"] if result else ""

    async def batch_generate(self, prompts: List[str]) -> List[str]:
        tasks = [self._process_single(prompt) for prompt in prompts]
        return await asyncio.gather(*tasks, return_exceptions=True)

将多个独立 prompt 合并为单个请求
使用 \n---\n 分隔不同生成任务
示例 prompt 格式：

Generate Python code for the following tasks:

1. {task1_description}
---
2. {task2_description}
---
3. {task3_description}

from datetime import datetime, timedelta
import hashlib

class PromptCache:
    def __init__(self):
        self.cache = {}

    def _get_key(self, prompt: str) -> str:
        return hashlib.md5(prompt.encode()).hexdigest()

    def get(self, prompt: str) -> Optional[str]:
        key = self._get_key(prompt)
        if key in self.cache and self.cache[key]["expiry"] > datetime.now():
            return self.cache[key]["value"]
        return None

    def set(self, prompt: str, value: str, ttl: int = 300):
        key = self._get_key(prompt)
        self.cache[key] = {
            "value": value,
            "expiry": datetime.now() + timedelta(seconds=ttl)
        }