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"""DeepClaude 服务,用于协调 DeepSeek 和 Claude API 的调用"""
import json
import time
import tiktoken
import asyncio
from typing import AsyncGenerator
from app.utils.logger import logger
from app.clients import DeepSeekClient, ClaudeClient
class DeepClaude:
"""处理 DeepSeek 和 Claude API 的流式输出衔接"""
def __init__(self, deepseek_api_key: str, claude_api_key: str,
deepseek_api_url: str = "https://api.deepseek.com/v1/chat/completions",
claude_api_url: str = "https://api.anthropic.com/v1/messages",
claude_provider: str = "anthropic",
is_origin_reasoning: bool = True):
"""初始化 API 客户端
Args:
deepseek_api_key: DeepSeek API密钥
claude_api_key: Claude API密钥
"""
self.deepseek_client = DeepSeekClient(deepseek_api_key, deepseek_api_url)
self.claude_client = ClaudeClient(claude_api_key, claude_api_url, claude_provider)
self.is_origin_reasoning = is_origin_reasoning
async def chat_completions_with_stream(
self,
messages: list,
model_arg: tuple[float, float, float, float],
deepseek_model: str = "deepseek-reasoner",
claude_model: str = "claude-3-5-sonnet-20241022"
) -> AsyncGenerator[bytes, None]:
"""处理完整的流式输出过程
Args:
messages: 初始消息列表
model_arg: 模型参数
deepseek_model: DeepSeek 模型名称
claude_model: Claude 模型名称
Yields:
字节流数据,格式如下:
{
"id": "chatcmpl-xxx",
"object": "chat.completion.chunk",
"created": timestamp,
"model": model_name,
"choices": [{
"index": 0,
"delta": {
"role": "assistant",
"reasoning_content": reasoning_content,
"content": content
}
}]
}
"""
# 生成唯一的会话ID和时间戳
chat_id = f"chatcmpl-{hex(int(time.time() * 1000))[2:]}"
created_time = int(time.time())
# 创建队列,用于收集输出数据
output_queue = asyncio.Queue()
# 队列,用于传递 DeepSeek 推理内容给 Claude
claude_queue = asyncio.Queue()
# 用于存储 DeepSeek 的推理累积内容
reasoning_content = []
async def process_deepseek():
logger.info(f"开始处理 DeepSeek 流,使用模型:{deepseek_model}, 提供商: {self.deepseek_client.provider}")
try:
async for content_type, content in self.deepseek_client.stream_chat(messages, deepseek_model, self.is_origin_reasoning):
if content_type == "reasoning":
reasoning_content.append(content)
response = {
"id": chat_id,
"object": "chat.completion.chunk",
"created": created_time,
"model": deepseek_model,
"choices": [{
"index": 0,
"delta": {
"role": "assistant",
"reasoning_content": content,
"content": ""
}
}]
}
await output_queue.put(f"data: {json.dumps(response)}\n\n".encode('utf-8'))
elif content_type == "content":
# 当收到 content 类型时,将完整的推理内容发送到 claude_queue,并结束 DeepSeek 流处理
logger.info(f"DeepSeek 推理完成,收集到的推理内容长度:{len(''.join(reasoning_content))}")
await claude_queue.put("".join(reasoning_content))
break
except Exception as e:
logger.error(f"处理 DeepSeek 流时发生错误: {e}")
await claude_queue.put("")
# 用 None 标记 DeepSeek 任务结束
logger.info("DeepSeek 任务处理完成,标记结束")
await output_queue.put(None)
async def process_claude():
try:
logger.info("等待获取 DeepSeek 的推理内容...")
reasoning = await claude_queue.get()
logger.debug(f"获取到推理内容,内容长度:{len(reasoning) if reasoning else 0}")
if not reasoning:
logger.warning("未能获取到有效的推理内容,将使用默认提示继续")
reasoning = "获取推理内容失败"
# 构造 Claude 的输入消息
claude_messages = messages.copy()
combined_content = f"""
Here's my another model's reasoning process:\n{reasoning}\n\n
Based on this reasoning, provide your response directly to me:"""
# 改造最后一个消息对象,判断消息对象是 role = user,然后在这个对象的 content 后追加新的 String
last_message = claude_messages[-1]
if last_message.get("role", "") == "user":
original_content = last_message["content"]
fixed_content = f"Here's my original input:\n{original_content}\n\n{combined_content}"
last_message["content"] = fixed_content
# 处理可能 messages 内存在 role = system 的情况,如果有,则去掉当前这一条的消息对象
claude_messages = [message for message in claude_messages if message.get("role", "") != "system"]
logger.info(f"开始处理 Claude 流,使用模型: {claude_model}, 提供商: {self.claude_client.provider}")
async for content_type, content in self.claude_client.stream_chat(
messages=claude_messages,
model_arg=model_arg,
model=claude_model,
):
if content_type == "answer":
response = {
"id": chat_id,
"object": "chat.completion.chunk",
"created": created_time,
"model": claude_model,
"choices": [{
"index": 0,
"delta": {
"role": "assistant",
"content": content
}
}]
}
await output_queue.put(f"data: {json.dumps(response)}\n\n".encode('utf-8'))
except Exception as e:
logger.error(f"处理 Claude 流时发生错误: {e}")
# 用 None 标记 Claude 任务结束
logger.info("Claude 任务处理完成,标记结束")
await output_queue.put(None)
# 创建并发任务
deepseek_task = asyncio.create_task(process_deepseek())
claude_task = asyncio.create_task(process_claude())
# 等待两个任务完成,通过计数判断
finished_tasks = 0
while finished_tasks < 2:
item = await output_queue.get()
if item is None:
finished_tasks += 1
else:
yield item
# 发送结束标记
yield b'data: [DONE]\n\n'
async def chat_completions_without_stream(
self,
messages: list,
model_arg: tuple[float, float, float, float],
deepseek_model: str = "deepseek-reasoner",
claude_model: str = "claude-3-5-sonnet-20241022"
) -> dict:
"""处理非流式输出过程
Args:
messages: 初始消息列表
model_arg: 模型参数
deepseek_model: DeepSeek 模型名称
claude_model: Claude 模型名称
Returns:
dict: OpenAI 格式的完整响应
"""
chat_id = f"chatcmpl-{hex(int(time.time() * 1000))[2:]}"
created_time = int(time.time())
reasoning_content = []
# 1. 获取 DeepSeek 的推理内容(仍然使用流式)
try:
async for content_type, content in self.deepseek_client.stream_chat(messages, deepseek_model, self.is_origin_reasoning):
if content_type == "reasoning":
reasoning_content.append(content)
elif content_type == "content":
break
except Exception as e:
logger.error(f"获取 DeepSeek 推理内容时发生错误: {e}")
reasoning_content = ["获取推理内容失败"]
# 2. 构造 Claude 的输入消息
reasoning = "".join(reasoning_content)
claude_messages = messages.copy()
combined_content = f"""
Here's my another model's reasoning process:\n{reasoning}\n\n
Based on this reasoning, provide your response directly to me:"""
# 改造最后一个消息对象,判断消息对象是 role = user,然后在这个对象的 content 后追加新的 String
last_message = claude_messages[-1]
if last_message.get("role", "") == "user":
original_content = last_message["content"]
fixed_content = f"Here's my original input:\n{original_content}\n\n{combined_content}"
last_message["content"] = fixed_content
# 处理可能 messages 内存在 role = system 的情况
claude_messages = [message for message in claude_messages if message.get("role", "") != "system"]
# 拼接所有 content 为一个字符串,计算 token
token_content = "\n".join([message.get("content", "") for message in claude_messages])
encoding = tiktoken.encoding_for_model("gpt-4o")
input_tokens = encoding.encode(token_content)
logger.debug(f"输入 Tokens: {len(input_tokens)}")
logger.debug("claude messages: " + str(claude_messages))
# 3. 获取 Claude 的非流式响应
try:
answer = ""
async for content_type, content in self.claude_client.stream_chat(
messages=claude_messages,
model_arg=model_arg,
model=claude_model,
stream=False
):
if content_type == "answer":
answer += content
output_tokens = encoding.encode(answer)
logger.debug(f"输出 Tokens: {len(output_tokens)}")
# 4. 构造 OpenAI 格式的响应
return {
"id": chat_id,
"object": "chat.completion",
"created": created_time,
"model": claude_model,
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": answer,
"reasoning_content": reasoning
},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": len(input_tokens),
"completion_tokens": len(output_tokens),
"total_tokens": len(input_tokens + output_tokens)
}
}
except Exception as e:
logger.error(f"获取 Claude 响应时发生错误: {e}")
raise e |