```

docs(.gitignore/README/QUICKSTART): 更新文档和忽略配置

- 添加IDE配置、日志和数据文件到.gitignore
- 重构QUICKSTART.md，提供Docker Compose和本地开发两种部署方式
- 更新README.md，优化项目介绍和架构说明
- 移除旧的agent.py和backend.py文件
```

app/agent.py

@@ -0,0 +1,87 @@
+"""
+AI Agent 服务类 - 支持多模型动态切换
+接收外部传入的 checkpointer，不负责管理连接生命周期
+"""
+
+import os
+from dotenv import load_dotenv
+from langchain_community.chat_models import ChatZhipuAI
+from langchain_core.messages import HumanMessage
+from langchain_openai import ChatOpenAI
+from pydantic import SecretStr
+
+# 本地模块
+from app.graph_builder import GraphBuilder
+from app.tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
+
+load_dotenv()
+
+
+class AIAgentService:
+    """异步 AI Agent 服务，支持多模型动态切换，使用外部传入的 checkpointer"""
+
+    def __init__(self, checkpointer):
+        """
+        初始化服务
+        Args:
+            checkpointer: 已经初始化的 AsyncPostgresSaver 实例
+        """
+        self.checkpointer = checkpointer
+        self.graphs = {}  # 存储不同模型对应的 graph 实例
+
+    def _create_zhipu_llm(self):
+        """创建智谱在线 LLM"""
+        api_key = os.getenv("ZHIPUAI_API_KEY")
+        if not api_key:
+            raise ValueError("ZHIPUAI_API_KEY not set in environment")
+        return ChatZhipuAI(
+            model="glm-4.7-flash",
+            api_key=api_key,
+            temperature=0.1,
+            max_tokens=4096,
+        )
+
+    def _create_local_llm(self):
+        """创建本地 vLLM 服务 LLM"""
+        return ChatOpenAI(
+            # 原来是 http://localhost:8000/v1
+            # 改为 FRP 穿透后的公网地址
+            base_url = "http://115.190.121.151:18000/v1",
+            api_key=SecretStr(os.getenv("VLLM_LOCAL_KEY", "")),
+            model="gemma-4-E2B-it",
+        )
+
+    async def initialize(self):
+        """预编译所有模型的 graph（使用传入的 checkpointer）"""
+        model_configs = {
+            "zhipu": self._create_zhipu_llm,
+            "local": self._create_local_llm,
+        }
+
+        for model_name, llm_creator in model_configs.items():
+            try:
+                llm = llm_creator()
+                builder = GraphBuilder(llm, AVAILABLE_TOOLS, TOOLS_BY_NAME).build()
+                graph = builder.compile(checkpointer=self.checkpointer)
+                self.graphs[model_name] = graph
+                print(f"✅ 模型 '{model_name}' 初始化成功")
+            except Exception as e:
+                print(f"⚠️ 模型 '{model_name}' 初始化失败: {e}")
+
+        if not self.graphs:
+            raise RuntimeError("没有可用的模型，请检查配置")
+
+        return self
+
+    async def process_message(self, message: str, thread_id: str, model: str = "zhipu") -> str:
+        """处理用户消息，返回最终答案"""
+        if model not in self.graphs:
+            fallback_model = next(iter(self.graphs.keys()))
+            print(f"警告: 模型 '{model}' 不可用，已切换到 '{fallback_model}'")
+            model = fallback_model
+
+        graph = self.graphs[model]
+        config = {"configurable": {"thread_id": thread_id}}
+        input_state = {"messages": [HumanMessage(content=message)]}
+        result = await graph.ainvoke(input_state, config=config)
+        return result["messages"][-1].content