Claude Code与ChatGPT集成实战：从零开始的AI开发指南

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作为当前最受关注的两个 AI 模型，Claude Code 和 ChatGPT 各有特点。Claude Code 由 Anthropic 开发，专注于代码生成和解释任务，在理解编程上下文方面表现优异。而 ChatGPT 由 OpenAI 开发，更擅长通用文本生成和对话任务。两者的结合可以发挥互补优势：

Claude Code 负责代码相关任务，如自动补全、语法检查和代码解释
ChatGPT 处理自然语言交互，如文档生成、用户问题解答
组合使用可以构建更全面的 AI 编程助手

在实际集成过程中，开发者常遇到以下问题：

认证配置复杂 ：两种 API 使用不同的认证方式，密钥管理容易混乱
API 调用限制 ：两者有不同的速率限制和配额策略
响应处理不一致 ：返回数据格式和错误处理方式不同
性能瓶颈 ：连续调用多个 API 导致延迟增加
错误处理不足 ：网络波动或 API 限制导致的失败缺乏重试机制

确保系统满足以下要求：

Python 3.8+
已安装 requests 库
获取有效的 API 密钥

pip install requests

推荐使用环境变量管理 API 密钥：

import os
from typing import Optional

class APIConfig:
    def __init__(self):
        self.claude_key = os.getenv("CLAUDE_API_KEY")
        self.chatgpt_key = os.getenv("CHATGPT_API_KEY")

        if not self.claude_key or not self.chatgpt_key:
            raise ValueError("API keys not found in environment variables")

import requests
from typing import Dict, Any
import time

class AIIntegration:
    def __init__(self, config: APIConfig):
        self.config = config
        self.claude_url = "https://api.anthropic.com/v1/code"
        self.chatgpt_url = "https://api.openai.com/v1/chat/completions"
        self.max_retries = 3
        self.retry_delay = 2

    def call_claude(self, code: str) -> Dict[str, Any]:
        headers = {"Authorization": f"Bearer {self.config.claude_key}",
            "Content-Type": "application/json"
        }

        payload = {
            "code": code,
            "language": "python"
        }

        for attempt in range(self.max_retries):
            try:
                response = requests.post(
                    self.claude_url,
                    headers=headers,
                    json=payload,
                    timeout=10
                )
                response.raise_for_status()
                return response.json()
            except requests.exceptions.RequestException as e:
                if attempt == self.max_retries - 1:
                    raise
                time.sleep(self.retry_delay * (attempt + 1))

    def call_chatgpt(self, prompt: str) -> Dict[str, Any]:
        headers = {"Authorization": f"Bearer {self.config.chatgpt_key}",
            "Content-Type": "application/json"
        }

        payload = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": prompt}],
            "temperature": 0.7
        }

        for attempt in range(self.max_retries):
            try:
                response = requests.post(
                    self.chatgpt_url,
                    headers=headers,
                    json=payload,
                    timeout=10
                )
                response.raise_for_status()
                return response.json()
            except requests.exceptions.RequestException as e:
                if attempt == self.max_retries - 1:
                    raise
                time.sleep(self.retry_delay * (attempt + 1))

from concurrent.futures import ThreadPoolExecutor

class BatchProcessor:
    def __init__(self, ai_integration: AIIntegration):
        self.ai = ai_integration

    def batch_process_code(self, code_list: list[str]) -> list[Dict[str, Any]]:
        with ThreadPoolExecutor(max_workers=5) as executor:
            results = list(executor.map(self.ai.call_claude, code_list))
        return results

    def batch_process_prompts(self, prompt_list: list[str]) -> list[Dict[str, Any]]:
        with ThreadPoolExecutor(max_workers=5) as executor:
            results = list(executor.map(self.ai.call_chatgpt, prompt_list))
        return results

from functools import lru_cache

@lru_cache(maxsize=1000)
def cached_claude_call(code: str) -> Dict[str, Any]:
    return AIIntegration(APIConfig()).call_claude(code)

@lru_cache(maxsize=1000)
def cached_chatgpt_call(prompt: str) -> Dict[str, Any]:
    return AIIntegration(APIConfig()).call_chatgpt(prompt)

import threading

class RateLimiter:
    def __init__(self, calls_per_minute: int):
        self.calls_per_minute = calls_per_minute
        self.semaphore = threading.Semaphore(calls_per_minute)
        self.lock = threading.Lock()
        self.last_reset = time.time()

    def acquire(self):
        with self.lock:
            current_time = time.time()
            if current_time - self.last_reset >= 60:
                self.semaphore = threading.Semaphore(self.calls_per_minute)
                self.last_reset = current_time

        self.semaphore.acquire()

速率限制规避
为每个 API 设置独立的 RateLimiter
监控 API 调用频率
实现指数退避重试机制
敏感数据处理
不要在请求中包含敏感信息
实现数据脱敏预处理
记录日志时删除敏感字段
监控与日志
记录所有 API 调用和响应时间
设置警报通知异常情况
定期分析日志优化性能

import logging

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

class MonitoredAIIntegration(AIIntegration):
    def call_claude(self, code: str) -> Dict[str, Any]:
        start_time = time.time()
        logger.info(f"Calling Claude API with code snippet")
        try:
            result = super().call_claude(code)
            duration = time.time() - start_time
            logger.info(f"Claude API call completed in {duration:.2f}s")
            return result
        except Exception as e:
            logger.error(f"Claude API call failed: {str(e)}")
            raise

在实际业务中，AI 服务编排需要考虑以下因素：