218 lines
8.5 KiB
Python
218 lines
8.5 KiB
Python
"""
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AI Agent 服务类 - 支持多模型动态切换
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接收外部传入的 checkpointer，不负责管理连接生命周期
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"""
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import os
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from dotenv import load_dotenv
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from langchain_community.chat_models import ChatZhipuAI
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from langchain_openai import ChatOpenAI
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from pydantic import SecretStr
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from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
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# 本地模块
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from app.graph_builder import GraphBuilder, GraphContext
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from app.tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
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from app.logger import debug, info, warning, error
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load_dotenv()
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class AIAgentService:
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"""异步 AI Agent 服务，支持多模型动态切换，使用外部传入的 checkpointer"""
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def __init__(self, checkpointer: AsyncPostgresSaver):
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"""
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初始化服务
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Args:
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checkpointer: 已经初始化的 AsyncPostgresSaver 实例
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"""
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self.checkpointer = checkpointer
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self.graphs = {} # 存储不同模型对应的 graph 实例
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def _create_zhipu_llm(self):
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"""创建智谱在线 LLM"""
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api_key = os.getenv("ZHIPUAI_API_KEY")
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if not api_key:
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raise ValueError("ZHIPUAI_API_KEY not set in environment")
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return ChatZhipuAI(
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model="glm-4.7-flash",
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api_key=api_key,
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temperature=0.1,
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max_tokens=4096,
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timeout=60.0, # 请求超时时间（秒）
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max_retries=2, # 失败后自动重试次数
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)
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def _create_deepseek_llm(self):
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"""创建 DeepSeek LLM（使用 OpenAI 兼容 API）"""
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api_key = os.getenv("DEEPSEEK_API_KEY")
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if not api_key:
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raise ValueError("DEEPSEEK_API_KEY not set in environment")
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return ChatOpenAI(
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base_url="https://api.deepseek.com",
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api_key=SecretStr(api_key),
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model="deepseek-reasoner", # deepseek-chat: 非思考模式, deepseek-reasoner: 思考模式
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temperature=0.1,
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max_tokens=4096,
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timeout=60.0, # 请求超时时间（秒）
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max_retries=2, # 失败后自动重试次数
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)
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def _create_local_llm(self):
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"""创建本地 vLLM 服务 LLM"""
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# vLLM 服务地址：优先从环境变量读取，适配 Docker、FRP 穿透和本地开发
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vllm_base_url = os.getenv(
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"VLLM_BASE_URL",
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"http://localhost:8081/v1"
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)
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return ChatOpenAI(
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base_url=vllm_base_url,
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api_key=SecretStr(os.getenv("LLAMACPP_API_KEY", "token-abc123")),
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model="gemma-4-E2B-it",
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timeout=60.0, # 请求超时时间（秒）
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max_retries=2, # 失败后自动重试次数
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)
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async def initialize(self):
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"""预编译所有模型的 graph（使用传入的 checkpointer）"""
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model_configs = {
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"zhipu": self._create_zhipu_llm,
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"deepseek": self._create_deepseek_llm,
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"local": self._create_local_llm,
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}
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for model_name, llm_creator in model_configs.items():
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try:
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info(f"🔄 正在初始化模型 '{model_name}'...")
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llm = llm_creator()
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builder = GraphBuilder(llm, AVAILABLE_TOOLS, TOOLS_BY_NAME).build()
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graph = builder.compile(checkpointer=self.checkpointer)
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self.graphs[model_name] = graph
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info(f"✅ 模型 '{model_name}' 初始化成功")
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except Exception as e:
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import traceback
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error_detail = traceback.format_exc()
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warning(f"⚠️ 模型 '{model_name}' 初始化失败: {e}")
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debug(f" 详细错误:\n{error_detail}")
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if not self.graphs:
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raise RuntimeError("没有可用的模型，请检查配置。可能的原因：\n"
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"1. ZHIPUAI_API_KEY 未配置或无效\n"
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"2. DEEPSEEK_API_KEY 未配置或无效\n"
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"3. vLLM 服务未启动或地址错误 (VLLM_BASE_URL)\n"
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"4. 网络连接问题")
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return self
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async def process_message(self, message: str, thread_id: str, model: str = "zhipu", user_id: str = "default_user") -> dict:
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"""
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处理用户消息，返回包含回复、token统计和耗时的字典
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Returns:
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dict: {
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"reply": str, # AI 回复内容
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"token_usage": dict, # Token 使用详情
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"elapsed_time": float # 调用耗时（秒）
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}
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"""
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# 尝试使用指定模型，如果不可用则循环尝试其他模型
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if model not in self.graphs:
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warning(f"警告: 模型 '{model}' 不可用，尝试切换到其他可用模型")
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found = False
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for available_model in self.graphs.keys():
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try:
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# 这里可以添加额外的模型可用性检查逻辑
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model = available_model
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found = True
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info(f"已切换到可用模型: '{model}'")
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break
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except Exception as e:
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warning(f"模型 '{available_model}' 也不可用: {str(e)}")
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continue
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if not found:
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raise RuntimeError(f"错误: 没有任何可用的模型。当前注册的模型: {list(self.graphs.keys())}")
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graph = self.graphs[model]
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config = {
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"configurable": {"thread_id": thread_id},
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"metadata": {"user_id": user_id} # 写入 metadata 供历史查询使用
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}
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input_state = {"messages": [{"role": "user", "content": message}]}
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context = GraphContext(user_id=user_id)
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result = await graph.ainvoke(input_state, config=config, context=context)
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reply = result["messages"][-1].content
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token_usage = result.get("last_token_usage", {})
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elapsed_time = result.get("last_elapsed_time", 0.0)
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return {
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"reply": reply,
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"token_usage": token_usage,
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"elapsed_time": elapsed_time
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}
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async def process_message_stream(self, message: str, thread_id: str, model_name: str, user_id: str = "default_user"):
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"""
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流式处理消息，返回异步生成器
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Args:
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message: 用户消息
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thread_id: 线程 ID
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model_name: 模型名称
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user_id: 用户 ID
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Yields:
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字典，包含事件类型和数据
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"""
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graph = self.graphs.get(model_name)
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if not graph:
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warning(f"警告: 模型 '{model_name}' 不可用，使用默认模型")
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model_name = next(iter(self.graphs.keys()))
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graph = self.graphs[model_name]
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config = {
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"configurable": {"thread_id": thread_id},
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"metadata": {"user_id": user_id}
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}
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input_state = {"messages": [{"role": "user", "content": message}]}
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context = GraphContext(user_id=user_id)
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# 使用 astream_events 获取流式事件
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async for event in graph.astream_events(input_state, config=config, context=context, version="v2"):
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kind = event["event"]
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# 聊天模型流式输出
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if kind == "on_chat_model_stream":
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content = event["data"]["chunk"].content
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if content:
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yield {"type": "token", "content": content}
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# 工具调用开始
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elif kind == "on_tool_start":
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tool_name = event["name"]
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yield {"type": "tool_start", "tool": tool_name}
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# 工具调用结束
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elif kind == "on_tool_end":
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tool_name = event["name"]
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yield {"type": "tool_end", "tool": tool_name}
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# 链结束，获取最终结果
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elif kind == "on_chain_end" and event["name"] == "LangGraph":
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output = event["data"]["output"]
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reply = output["messages"][-1].content if output.get("messages") else ""
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token_usage = output.get("last_token_usage", {})
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elapsed_time = output.get("last_elapsed_time", 0.0)
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yield {
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"type": "done",
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"reply": reply,
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"token_usage": token_usage,
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"elapsed_time": elapsed_time
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}
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