85 lines
2.9 KiB
Python
85 lines
2.9 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_core.messages import HumanMessage
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from langchain_openai import ChatOpenAI
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from pydantic import SecretStr
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# 本地模块
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from graph_builder import GraphBuilder
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from tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
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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):
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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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)
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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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return ChatOpenAI(
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base_url="http://localhost:8000/v1",
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api_key=SecretStr(os.getenv("VLLM_LOCAL_KEY", "")),
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model="gemma-4-E2B-it",
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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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"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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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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print(f"✅ 模型 '{model_name}' 初始化成功")
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except Exception as e:
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print(f"⚠️ 模型 '{model_name}' 初始化失败: {e}")
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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")
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return self
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async def process_message(self, message: str, thread_id: str, model: str = "zhipu") -> str:
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"""处理用户消息，返回最终答案"""
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if model not in self.graphs:
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fallback_model = next(iter(self.graphs.keys()))
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print(f"警告: 模型 '{model}' 不可用，已切换到 '{fallback_model}'")
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model = fallback_model
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graph = self.graphs[model]
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config = {"configurable": {"thread_id": thread_id}}
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input_state = {"messages": [HumanMessage(content=message)]}
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result = await graph.ainvoke(input_state, config=config)
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return result["messages"][-1].content |