173 lines
7.2 KiB
Python
173 lines
7.2 KiB
Python
import asyncio
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import json
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from typing import Optional, Dict, Callable, Awaitable
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from dataclasses import dataclass, field
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import aiohttp # 新增：异步HTTP库
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# 大模型配置（集中管理）
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LLM_CONFIG = {
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"base_url": "http://10.10.10.202:8088/v1",
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"api_key": "app-m7HZNV1aGiheh3wr6wNVHFxX",
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"timeout": 30, # 请求超时时间（秒）
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"default_scene": "通用聊天场景", # 默认场景描述
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"stream_chunk_size": 1024 # 流式接收块大小
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}
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# 定义流式回调函数类型（异步）
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LLMStreamCallback = Callable[[str, Optional[str], bool], Awaitable[None]]
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"""
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回调函数参数说明：
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- chunk: 单次流式返回的文本片段
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- conversation_id: 会话ID（首次返回，后续复用）
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- is_finished: 是否结束（True=流式结束/同步返回完成）
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"""
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@dataclass
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class LLMConversation:
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"""会话对象（管理会话ID和上下文）"""
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conversation_id: Optional[str] = None
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user_id: str = ""
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scene_description: str = LLM_CONFIG["default_scene"]
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# 可选：存储会话历史（如需上下文管理）
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history: list = field(default_factory=list)
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class LLMClient:
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"""大模型客户端封装（异步修复版）"""
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def __init__(self):
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self.base_url = LLM_CONFIG["base_url"]
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self.headers = {
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"Authorization": f"Bearer {LLM_CONFIG['api_key']}",
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"Content-Type": "application/json"
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}
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self.timeout = aiohttp.ClientTimeout(total=LLM_CONFIG["timeout"]) # 异步超时
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self._session: Optional[aiohttp.ClientSession] = None # 异步会话（复用连接）
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async def _get_session(self) -> aiohttp.ClientSession:
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"""获取/复用异步HTTP会话"""
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if self._session is None or self._session.closed:
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self._session = aiohttp.ClientSession(timeout=self.timeout)
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return self._session
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async def send_message(
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self,
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query: str,
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conversation: LLMConversation,
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stream_callback: Optional[LLMStreamCallback] = None,
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response_mode: str = "streaming"
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) -> tuple[Optional[str], str]:
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"""
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发送消息到大模型（纯异步版，无线程池阻塞）
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"""
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payload = {
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"query": query,
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"inputs": {"scene_description": conversation.scene_description},
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"response_mode": response_mode,
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"user": conversation.user_id
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}
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if conversation.conversation_id:
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payload["conversation_id"] = conversation.conversation_id
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url = f"{self.base_url}/chat-messages"
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full_response = ""
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res_conversation_id = conversation.conversation_id
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try:
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session = await self._get_session()
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if response_mode == "streaming":
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# 异步流式请求（无线程池，纯异步IO）
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async with session.post(url, headers=self.headers, json=payload) as response:
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response.raise_for_status()
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# 实时迭代流式响应
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async for line in response.content.iter_chunked(LLM_CONFIG["stream_chunk_size"]):
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if not line:
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continue
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line_data = line.decode("utf-8")
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if line_data.startswith("data: "):
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json_str = line_data[6:].strip()
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if json_str == "[DONE]":
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if stream_callback:
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await stream_callback("", res_conversation_id, True)
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break
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try:
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data = json.loads(json_str)
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# 更新会话ID
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if not res_conversation_id and "conversation_id" in data:
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res_conversation_id = data["conversation_id"]
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# 提取内容
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chunk = data.get("content", data.get("answer", data.get("message", "")))
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if chunk:
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full_response += chunk
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if stream_callback:
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await stream_callback(chunk, res_conversation_id, False)
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await asyncio.sleep(0) # 让出调度权
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except json.JSONDecodeError as e:
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print(f"解析流式数据失败: {e}")
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continue
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else:
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# 异步非流式请求
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async with session.post(url, headers=self.headers, json=payload) as response:
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response.raise_for_status()
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data = await response.json()
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res_conversation_id = data.get("conversation_id", conversation.conversation_id)
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full_response = data.get("content", data.get("answer", data.get("message", "")))
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if stream_callback:
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await stream_callback(full_response, res_conversation_id, True)
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conversation.conversation_id = res_conversation_id
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return res_conversation_id, full_response
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except aiohttp.ClientError as e:
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error_msg = f"大模型请求失败: {str(e)}"
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print(error_msg)
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if stream_callback:
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await stream_callback(f"[错误] {error_msg}", res_conversation_id, True)
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return res_conversation_id, ""
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except Exception as e:
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error_msg = f"大模型处理异常: {str(e)}"
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print(error_msg)
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if stream_callback:
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await stream_callback(f"[错误] {error_msg}", res_conversation_id, True)
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return res_conversation_id, ""
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async def close(self):
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"""关闭异步会话（程序退出时调用）"""
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if self._session and not self._session.closed:
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await self._session.close()
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# 全局单例客户端（异步版）
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llm_client = LLMClient()
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# 快捷调用函数（保持原有接口不变）
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async def call_llm(
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query: str,
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user_id: str,
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scene_description: str = LLM_CONFIG["default_scene"],
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conversation_id: Optional[str] = None,
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stream_callback: Optional[LLMStreamCallback] = None,
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response_mode: str = "streaming"
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) -> tuple[Optional[str], str]:
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"""
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快捷调用大模型（无需手动创建会话对象）
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:param query: 用户提问
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:param user_id: 用户ID
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:param scene_description: 场景描述
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:param conversation_id: 会话ID（续聊用）
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:param stream_callback: 流式回调
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:param response_mode: 响应模式
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:return: (conversation_id, 完整回复)
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"""
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conversation = LLMConversation(
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conversation_id=conversation_id,
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user_id=user_id,
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scene_description=scene_description
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)
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return await llm_client.send_message(
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query=query,
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conversation=conversation,
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stream_callback=stream_callback,
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response_mode=response_mode
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)
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# 可选：程序退出时关闭会话（如FastAPI的shutdown事件）
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async def shutdown_llm_client():
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await llm_client.close() |