Claude Code与DeepSeek集成实战：从零搭建AI协作开发环境

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在 AI 辅助开发领域，Claude Code 和 DeepSeek 作为两款主流智能编程工具，分别擅长代码补全（Code Completion）和上下文感知调试（Context-aware Debugging）。许多开发者希望同时利用两者的优势，但面临 API 设计差异、响应格式不兼容等集成难题。本文将用生产级代码示例演示如何构建可靠的协作管道。

特性	Claude Code	DeepSeek
鉴权方式 (Auth)	Bearer Token	API Key + Secret
速率限制 (Rate Limit)	50 次 / 分钟	100 次 / 分钟
返回结构 (Response)	`{choices: [{text: string}]}`	`{data: {suggestions: []}}`

from typing import TypedDict
import httpx
from pydantic import BaseModel

class ClaudeResponse(BaseModel):
    choices: list[dict[str, str]]

class DeepSeekResponse(BaseModel):
    data: dict[str, list[str]]

class UnifiedResponse(TypedDict):
    suggestions: list[str]
    source: str

def normalize_response(source: str, raw: dict) -> UnifiedResponse:
    """统一响应格式处理"""
    if source == 'claude':
        validated = ClaudeResponse.model_validate(raw)
        return {'suggestions': [c['text'] for c in validated.choices],
            'source': source
        }
    else:
        validated = DeepSeekResponse.model_validate(raw)
        return {'suggestions': validated.data['suggestions'],
            'source': source
        }

graph LR
    A[开发者 IDE] -->| 代码片段 | B(RabbitMQ)
    B --> C[Claude Worker]
    B --> D[DeepSeek Worker]
    C --> E[结果聚合]
    D --> E
    E --> F[统一响应]

import asyncio
from collections import defaultdict

async def batch_request(tasks: list[tuple[str, str]], 
    batch_size: int = 5
) -> dict[str, list]:
    """批量请求处理"""
    results = defaultdict(list)
    semaphore = asyncio.Semaphore(batch_size)

    async def process(task):
        async with semaphore:
            service, prompt = task
            # 实际请求逻辑省略
            return service, await call_api(service, prompt)

    batch_results = await asyncio.gather(*[process(t) for t in tasks])
    for service, resp in batch_results:
        results[service].append(resp)
    return results

from datetime import datetime, timedelta
from functools import lru_cache

class TimedLRU:
    def __init__(self, ttl: int = 300):
        self.ttl = timedelta(seconds=ttl)

    @lru_cache(maxsize=1024)
    def get(self, key: str) -> tuple[datetime, any]:
        return datetime.now(), expensive_operation(key)

    def check(self, key: str) -> any:
        ts, value = self.get(key)
        if datetime.now() - ts < self.ttl:
            return value
        self.get.cache_clear()  # 过期清除
        return None

import boto3
from base64 import b64decode

def get_decrypted_key(env_name: str) -> str:
    """AWS KMS 解密示例"""
    encrypted = os.getenv(env_name)
    return boto3.client('kms').decrypt(CiphertextBlob=b64decode(encrypted),
        EncryptionContext={'env': 'production'}
    )['Plaintext'].decode()

import re

SENSITIVE_PATTERN = re.compile(r'(?:password|api[._-]?key|secret)[=:][^&\s]+', 
    flags=re.IGNORECASE
)

def sanitize_input(text: str) -> str:
    """过滤敏感信息"""
    return SENSITIVE_PATTERN.sub('[REDACTED]', text)