chef_agent

.env.example

@@ -18,6 +18,13 @@ DATABASE_POOL_SIZE=10
 DATABASE_MAX_OVERFLOW=20
 DATABASE_ECHO=False
 
+# 数据库Postgres配置
+POSTGRES_USER="postgres"
+POSTGRES_PASSWORD="123456"
+POSTGRES_HOST="127.0.0.1"
+POSTGRES_PORT=5432
+POSTGRES_DB="postgres"
+
 # CORS配置
 ALLOWED_ORIGINS=["*"]
 
@@ -32,3 +39,6 @@ UPLOAD_DIR=./uploads
 
 # 缓存配置
 CACHE_TTL_SECONDS=300
+
+# AI配置
+DEEPSEEK_API_KEY=api_key

api/agent_api.py

@@ -0,0 +1,47 @@
+from fastapi import APIRouter
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel, Field
+from services.agent_server.chef_service import ChefService
+
+# agent相关的路由
+agent_router = APIRouter(prefix="/agent", tags=["Agent相关"])
+
+
+class ChatRequest(BaseModel):
+    thread_id: str = Field(description="当前会话的id")
+    message: str = Field(description="当前会话的问题")
+
+
+class ChatResponse(BaseModel):
+    thread_id: str
+    content: str
+
+
+@agent_router.post("/chat", response_model=ChatResponse)
+def chat(request: ChatRequest):
+
+    result = ChefService.chat(
+        thread_id=request.thread_id,
+        message=request.message,
+    )
+
+    messages = result["messages"]
+
+    # 最后一条 AI 消息
+    last_message = messages[-1]
+
+    return ChatResponse(
+        thread_id=request.thread_id,
+        content=last_message.content,
+    )
+
+@agent_router.post("/chat_stream")
+def chat(request: ChatRequest):
+
+    return StreamingResponse(
+        ChefService.chat_stream(
+            thread_id=request.thread_id,
+            message=request.message,
+        ),
+        media_type="text/plain; charset=utf-8"
+    )

api/scheduler_api.py

@@ -1,7 +1,5 @@
 from fastapi import APIRouter, HTTPException
 from typing import Any, Dict
-from core.db import SessionDep
-from core.dto.api_response import ApiResponse
 from core.task.dlt_scheduler import scheduler
 
 # 彩票相关的路由

core/config/logging_cof.py

@@ -0,0 +1,24 @@
+import logging
+import sys
+
+import structlog
+
+
+def setup_logging():
+    logging.basicConfig(
+        format='%(message)s',
+        stream=sys.stdout,
+        level=logging.INFO,
+    )
+
+    structlog.configure(
+        processors=[
+            structlog.contextvars.merge_contextvars,
+            structlog.processors.add_log_level,
+            structlog.processors.TimeStamper(fmt='iso'),
+            # structlog.processors.JSONRenderer(ensure_ascii=False),  # 生产输出JSON 开发生产2选一
+            structlog.dev.ConsoleRenderer() # 开发环境彩色控制台
+        ],
+        logger_factory=structlog.stdlib.LoggerFactory(),
+        cache_logger_on_first_use=True,
+    )

core/config/settings.py

@@ -94,6 +94,30 @@ class Settings(BaseSettings):
         description="是否打印SQL语句"
     )
 
+    # ---------- 数据库Postgres配置 ----------
+    POSTGRES_USER: str = Field(
+        default='postgres',
+        description="postgres用户名"
+    )
+    POSTGRES_PASSWORD: str = Field(
+        default='123456',
+        description="postgres密码"
+    )
+    POSTGRES_HOST: str = Field(
+        default='127.0.0.1',
+        description="postgres主机地址"
+    )
+    POSTGRES_PORT: int = Field(
+        default=5432,
+        ge=1,
+        le=65535,
+        description="postgres端口"
+    )
+    POSTGRES_DB: str = Field(
+        default='postgres',
+        description="postgres数据库名"
+    )
+
     # ---------- CORS配置 ----------
     ALLOWED_ORIGINS: List[str] = Field(
         default=["*"],
@@ -143,6 +167,17 @@ class Settings(BaseSettings):
         description="缓存过期时间（秒）"
     )
 
+    # ---------- AI配置 ----------
+    DEEPSEEK_API_KEY: str = Field(
+        default="sk-d3e11a4229744fd29a8468e7df072a4b",
+        description="deepseek模型api_key"
+    )
+
+    TAVILY_API_KEY: str = Field(
+        default="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",
+        description="tavily搜索接口api_key"
+    )
+
     # ---------- 计算属性 ----------
     @property
     def mysql_url(self) -> str:
@@ -150,6 +185,12 @@ class Settings(BaseSettings):
         # 连接url格式：mysql+pymysql://user:password@host:port/dbname
         return f"mysql+pymysql://{self.MYSQL_USER}:{self.MYSQL_PASSWORD}@{self.MYSQL_HOST}:{self.MYSQL_PORT}/{self.MYSQL_DB}?charset=utf8mb4"
 
+    @property
+    def postgres_url(self) -> str:
+        """postgres连接URL"""
+        # 连接url格式：postgresql://postgres:postgres@localhost:5432/postgres?sslmode=disable
+        return f"postgresql://{self.POSTGRES_USER}:{self.POSTGRES_PASSWORD}@{self.POSTGRES_HOST}:{self.POSTGRES_PORT}/{self.POSTGRES_DB}?sslmode=disable"
+
     @property
     def is_production(self) -> bool:
         """是否为生产环境"""

core/task/dlt_scheduler.py

@@ -1,30 +1,16 @@
-import logging
-import sys
-from pathlib import Path
 from datetime import datetime
 from typing import List
 
+import structlog
 from apscheduler.job import Job
 from apscheduler.schedulers.background import BackgroundScheduler
 from apscheduler.schedulers.base import STATE_PAUSED, STATE_STOPPED, STATE_RUNNING
 from apscheduler.triggers.cron import CronTrigger
 from apscheduler.events import EVENT_JOB_EXECUTED, EVENT_JOB_ERROR
 
-# 添加项目根目录到路径
-sys.path.append(str(Path(__file__).parent.parent.parent))
-
 from core.task.dlt_spider import DLTSpider
 
-# 配置日志
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    handlers=[
-        logging.FileHandler('lottery_scheduler.log', encoding='utf-8'),
-        logging.StreamHandler()
-    ]
-)
-logger = logging.getLogger(__name__)
+logger = structlog.getLogger(__name__)
 
 def test_fun():
     logger.info(f"当前时间---{datetime.now()}")

core/task/dlt_spider.py

@@ -1,17 +1,16 @@
-import logging
 from dataclasses import dataclass
 from datetime import datetime
 from typing import Any, List, Dict, Union, Optional, NamedTuple
 import time
 
 import requests
-from pydantic import BaseModel, field_validator
+import structlog
 from sqlalchemy import desc
 
 from core.db import get_db_session
 from models import LotteryDLT
 
-logger = logging.getLogger(__name__)
+logger = structlog.getLogger(__name__)
 
 
 def parse_draw_numbers(result_str: str) -> List[str]:

exceptions/errors.py

@@ -0,0 +1,3 @@
+class AgentNotReadyError(RuntimeError):
+    """agent未初始化错误"""
+    pass

exceptions/handlers.py

@@ -0,0 +1,24 @@
+from fastapi import Request, FastAPI
+from fastapi.responses import JSONResponse
+from exceptions.errors import AgentNotReadyError
+
+
+async def agent_not_ready_handler(request: Request, exc: Exception):
+    """agent未就绪的错误"""
+    return JSONResponse(
+        status_code=503,
+        content={
+            "code": 503,
+            "message": str(exc)
+        }
+    )
+
+
+# 集中处理 保持main简洁
+def register_exception_handlers(app: FastAPI):
+    """集中处理注册异常"""
+
+    app.add_exception_handler(
+        AgentNotReadyError,
+        agent_not_ready_handler,
+    )

lc/chef_agent.py

@@ -0,0 +1,262 @@
+from __future__ import annotations
+
+from typing import Any, cast
+
+import structlog
+from langchain.agents import create_agent
+from langchain.agents.middleware import SummarizationMiddleware
+from langchain.agents.middleware.summarization import ContextMessages
+from langchain.chat_models import init_chat_model
+from langchain_core.language_models import BaseChatModel
+from langchain_deepseek import ChatDeepSeek
+from langchain_tavily import TavilySearch
+from langgraph.checkpoint.postgres import PostgresSaver
+from psycopg import Connection
+from psycopg_pool import ConnectionPool
+
+from core.config.settings import settings
+from exceptions.errors import AgentNotReadyError
+
+logger = structlog.getLogger(__name__)
+
+
+class ChefAgent:
+    """厨师 Agent。
+
+    负责：
+    - LLM 初始化
+    - Tool 初始化
+    - CheckPointer 初始化
+    - Middleware 初始化
+    - Agent 初始化
+    - 生命周期管理
+    """
+
+    def __init__(self) -> None:
+        self._pool = None
+        self._checkpointer: PostgresSaver | None = None
+        self._agent = None
+
+    def initialize(self) -> ChefAgent:
+        """初始化 Agent。
+
+        Returns:
+            当前 Agent 实例，方便链式调用。
+        """
+
+        logger.info("开始初始化chef_agent...")
+
+        if self._agent is not None:
+            return self
+
+        # 1. 初始化 LLM
+        llm = self._create_llm()
+
+        # 2. 初始化工具
+        tools = self._create_tools()
+
+        # 3. 初始化 Middleware
+        middleware = self._create_middleware(llm)
+
+        # 4. 初始化 CheckPointer
+        self._checkpointer = self._create_checkpointer()
+
+        # 5. 初始化 Agent
+        logger.info("chef_agent的agent开始初始化...")
+        self._agent = create_agent(
+            model=llm,
+            tools=tools,
+            middleware=middleware,
+            checkpointer=self._checkpointer,
+            system_prompt=self._system_prompt(),
+        )
+        logger.info("chef_agent的agent初始化完成")
+
+        logger.info("chef_agent初始化完成")
+
+        return self
+
+    @staticmethod
+    def _create_llm() -> BaseChatModel:
+        """创建 LLM。"""
+
+        logger.info("chef_agent初始化llm...")
+
+        # llm = init_chat_model(
+        #     model="deepseek-v4-flash",
+        #     model_provider="deepseek",
+        #     api_key=settings.DEEPSEEK_API_KEY,
+        #     extra_body={"thingking":{"type": "disabled"}}
+        # )
+        llm = ChatDeepSeek(
+            model="deepseek-v4-flash",
+            api_key=settings.DEEPSEEK_API_KEY,
+            extra_body={"thingking": {"type": "disabled"}}
+        )
+
+        logger.info("chef_agent的llm初始化完成")
+        return llm
+
+    @staticmethod
+    def _create_tools() -> list[Any]:
+        """创建 Agent Tools。"""
+
+        logger.info("chef_agent工具开始初始化...")
+        search_tool = TavilySearch(
+            tavily_api_key=settings.TAVILY_API_KEY,
+            max_results=5,
+            topic="general",
+        )
+
+        logger.info("chef_agent工具初始化完成")
+        return [search_tool]
+
+    @staticmethod
+    def _create_middleware(
+        llm: BaseChatModel,
+    ) -> list[Any]:
+        """创建 Agent Middleware。"""
+
+        logger.info("chef_agent中间件开始初始化...")
+        summarization = SummarizationMiddleware(
+            model=llm,
+            trigger=cast(ContextMessages, ("messages", 10)),
+            keep=cast(ContextMessages, ("messages", 5)),
+        )
+
+        logger.info("chef_agent中间件初始化完成")
+        return [summarization]
+
+    def _create_checkpointer(self) -> PostgresSaver:
+        """创建 PostgreSQL CheckPointer。"""
+
+        logger.info("chef_agent的CheckPointer开始初始化")
+        logger.info("创建PostgreSQL连接池")
+        self._pool = ConnectionPool[Connection[dict[str, Any]]]( # 指定类型[Connection[dict[str, Any]]]防止编译器提示错误
+            conninfo=settings.postgres_url,
+            max_size=10,
+            kwargs={
+                "autocommit": True,
+            }
+        )
+
+        logger.info("创建checkpointer")
+        checkpointer = PostgresSaver(self._pool)
+
+        logger.info("初始化checkpoint数据表")
+        # 创建 LangGraph checkpoint 所需的数据表
+        checkpointer.setup()
+
+        logger.info("chef_agent的CheckPointer初始化完成")
+        return checkpointer
+
+    @staticmethod
+    def _system_prompt() -> str:
+        """Agent System Prompt。"""
+
+        return """
+        你是一名专业厨师。
+        
+        你的任务是根据用户提供的食材照片或食材清单，为用户推荐合适的菜谱。
+        
+        请严格按照以下流程执行：
+        
+        ## 1. 识别和评估食材
+        
+        如果用户提供的是食材照片：
+        
+        - 识别照片中可见的食材
+        - 根据外观判断食材的新鲜程度
+        - 估算大致可用量
+        - 排除明显不可食用或状态异常的食材
+        
+        整理成「可用食材清单」。
+        
+        如果用户直接提供食材清单，则直接使用用户提供的信息。
+        
+        ## 2. 搜索菜谱
+        
+        优先使用搜索工具。
+        
+        以「可用食材清单」作为核心关键词，搜索适合这些食材的菜谱。
+        
+        优先考虑：
+        
+        - 食材匹配度高
+        - 操作简单
+        - 营养均衡
+        - 家庭烹饪可执行
+        
+        除非搜索不到合适结果，否则不要直接凭经验编造菜谱。
+        
+        ## 3. 评估和排序
+        
+        对搜索到的候选菜谱进行综合评价。
+        
+        从以下维度进行评分：
+        
+        - 食材匹配度
+        - 营养价值
+        - 烹饪难度
+        - 烹饪时间
+        
+        综合评分后进行排序。
+        
+        优先推荐：
+        
+        「简单 + 食材匹配度高 + 营养丰富」
+        
+        的菜谱。
+        
+        ## 4. 输出结果
+        
+        最终输出结构化的菜谱推荐报告。
+        
+        每个推荐至少包含：
+        
+        - 菜谱名称
+        - 所需食材
+        - 核心烹饪步骤
+        - 烹饪时间
+        - 难度
+        - 综合评分
+        - 推荐理由
+        - 参考来源
+        
+        如果搜索结果中存在可靠的菜谱图片，则提供图片参考。
+        
+        如果搜索不到合适菜谱，再根据已有知识进行合理推荐，并明确说明这是基于模型知识给出的建议。
+        """.strip()
+
+    @property
+    def agent(self):
+        """获取 Agent。"""
+
+        if self._agent is None:
+            logger.info("ChefAgent 尚未初始化，请先调用 initialize()")
+            raise AgentNotReadyError(
+                "ChefAgent 尚未初始化，请先调用 initialize()"
+            )
+
+        return self._agent
+
+    def close(self) -> None:
+        """释放资源。"""
+
+        if self._pool is not None:
+            self._pool.close()
+            self._pool = None
+            logger.info("PostgreSQL已关闭")
+
+        self._checkpointer = None
+        self._agent = None
+
+    # 使用with上下文管理方式来使用的话需要这2个方法
+    # def __enter__(self) -> ChefAgent:
+    #     return self.initialize()
+    #
+    # def __exit__(self, exc_type, exc_value, traceback) -> None:
+    #     self.close()
+
+
+chef_agent = ChefAgent()

lc/deepseek.ipynb

@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-03T09:14:58.535236Z",
+     "start_time": "2026-09-03T09:14:57.335343800Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from openai import OpenAI\n",
+    "from openai.types.chat import (\n",
+    "    ChatCompletionSystemMessageParam,\n",
+    "    ChatCompletionUserMessageParam,\n",
+    "    ChatCompletionAssistantMessageParam\n",
+    ")"
+   ],
+   "id": "255e9b078be17a5f",
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-03T09:19:09.642201600Z",
+     "start_time": "2026-09-03T09:19:09.628764300Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "client = OpenAI(\n",
+    "    api_key='sk-4bdcdad5d4cd4856bc0c308c3f74eb22',\n",
+    "    base_url='https://api.deepseek.com'\n",
+    ")"
+   ],
+   "id": "6f8aeb64ad6aca1f",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-03T09:17:52.034706300Z",
+     "start_time": "2026-09-03T09:17:52.023338600Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "messages: list[ChatCompletionSystemMessageParam | ChatCompletionUserMessageParam | ChatCompletionAssistantMessageParam] = [\n",
+    "    ChatCompletionSystemMessageParam(role=\"system\", content=\"你是一个ai助手，所有回答使用中文\")\n",
+    "]"
+   ],
+   "id": "4530b047d03a8ab3",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# 第一轮对话\n",
+    "print(\"===========\")\n",
+    "messages.append(ChatCompletionUserMessageParam(role=\"user\", content=\"你好，我是大哥\"))\n",
+    "\n",
+    "response = client.chat.completions.create(\n",
+    "    model='deepseek-v4-flash',\n",
+    "    messages=messages,\n",
+    "    stream=False\n",
+    ")"
+   ],
+   "id": "a42f7a8dbdfa3b8c"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-03T08:51:19.785432800Z",
+     "start_time": "2026-09-03T08:51:19.759474700Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# print(response.model_dump_json())\n",
+    "print(response.choices[0].message.content)"
+   ],
+   "id": "fac34b2742f048e0",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "大哥你好！我是你的AI助手，随时听候差遣。今天有什么需要帮忙的吗？\n"
+     ]
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-03T08:51:41.715208300Z",
+     "start_time": "2026-09-03T08:51:36.483133100Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# 第二轮对话\n",
+    "messages.append(ChatCompletionAssistantMessageParam(role=\"assistant\", content=response.choices[0].message.content))\n",
+    "messages.append(ChatCompletionUserMessageParam(role=\"user\", content=\"你还记得我吗\"))\n",
+    "response = client.chat.completions.create(\n",
+    "    model='deepseek-v4-flash',\n",
+    "    messages=messages,\n",
+    ")\n",
+    "print(response.choices[0].message.content)"
+   ],
+   "id": "3c9fa1f10b75c256",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "大哥，这个问题您刚刚才问过呢，我当然记得您！您就是“大哥”嘛，我怎么会忘？\n",
+      "\n",
+      "虽然我的记忆不能跨对话永久保存，但只要咱们在这个对话里，您说的每句话、问的每个问题，我都记得一清二楚——包括您刚才已经问过一次“还记得我吗”，我当时也解释过啦。\n",
+      "\n",
+      "您现在又问一遍，是在考验我，还是想看看我是不是“脸盲”呀？哈哈，放心吧！在这段对话期间，您永远是我的大哥，随叫随到，有事您吩咐！😄\n"
+     ]
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-03T09:19:38.275536400Z",
+     "start_time": "2026-09-03T09:19:31.090446900Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "messages.append(ChatCompletionUserMessageParam(role=\"user\", content=\"你好，明天合肥天气怎么样\"))\n",
+    "\n",
+    "response = client.chat.completions.create(\n",
+    "    model='deepseek-v4-flash',\n",
+    "    messages=messages,\n",
+    "    stream=False\n",
+    ")\n",
+    "print(response.choices[0].message.content)"
+   ],
+   "id": "ea34cc88d92b2832",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "你好！很抱歉，由于我无法实时联网获取最新的气象数据，所以不能直接为你提供明天合肥的准确天气预报。\n",
+      "\n",
+      "不过，你可以通过以下几种方式快速查到最准确的信息：\n",
+      "\n",
+      "1. 打开手机自带的“天气”应用，添加并定位到“合肥”。\n",
+      "2. 在搜索引擎（如百度）中直接搜索“**合肥明天天气**”，或其他（例如“墨迹天气”）\n",
+      "3. 访问“中国天气网”（www.weather.com.cn），输入“合肥”即可查询。\n",
+      "\n",
+      "另外，由于我无法预知你目前的实际日期，建议你在查询时留意一下，如果是明天出发，记得顺带看一眼**后天**的预报，以便更好地安排行程。祝生活愉快！\n"
+     ]
+    }
+   ],
+   "execution_count": 7
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

lc/lc.ipynb

@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-09-05T01:34:44.550869800Z",
+     "start_time": "2026-09-05T01:34:44.535351500Z"
+    }
+   },
+   "source": [
+    "from typing import cast, Literal\n",
+    "\n",
+    "from langchain.agents.middleware.summarization import ContextMessages\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "from langchain.agents import create_agent\n",
+    "from langchain_core.runnables import RunnableConfig\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
+    "from langchain.agents.middleware import SummarizationMiddleware\n",
+    "\n",
+    "from core.config.settings import settings\n",
+    "from langchain.tools import tool\n",
+    "from langchain.messages import HumanMessage\n",
+    "from langchain_core.language_models import BaseChatModel"
+   ],
+   "outputs": [],
+   "execution_count": 15
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-05T01:37:24.572533800Z",
+     "start_time": "2026-09-05T01:37:24.555523100Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# 初始化模型\n",
+    "my_model = init_chat_model(\n",
+    "    model=\"deepseek-v4-flash\",\n",
+    "    api_key=settings.DEEPSEEK_API_KEY,\n",
+    ")\n",
+    "\n",
+    "# 初始化checkpointer 记忆管理的存储方式\n",
+    "checkpointer = InMemorySaver()\n",
+    "\n",
+    "# 初始化记忆策略中间件\n",
+    "middleware = SummarizationMiddleware(\n",
+    "    model=cast(BaseChatModel, my_model), # 消息摘要的记忆管理策略的模型\n",
+    "    trigger=cast(ContextMessages, (\"messages\", 3)), # 触发策略的条件\n",
+    "    keep=cast(ContextMessages, (\"messages\", 1)) # 触发策略后保留的消息条数\n",
+    ")"
+   ],
+   "id": "b905be968f5c7761",
+   "outputs": [],
+   "execution_count": 22
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-05T01:37:26.422888800Z",
+     "start_time": "2026-09-05T01:37:26.408643600Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# 初始化agent\n",
+    "agent = create_agent(\n",
+    "    model=cast(BaseChatModel, my_model),\n",
+    "    checkpointer=InMemorySaver(), # 短期记忆 通过thread_id进行记忆分组\n",
+    "    middleware=[middleware]\n",
+    ")"
+   ],
+   "id": "cb5ae2fa4aeaf130",
+   "outputs": [],
+   "execution_count": 23
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-05T01:37:59.930524700Z",
+     "start_time": "2026-09-05T01:37:27.968100300Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "config: RunnableConfig = {\"configurable\": {\"thread_id\": \"thread_1\"}}\n",
+    "agent.invoke({\"messages\": [HumanMessage(\"你好，我是胖哥\")]}, config)\n",
+    "agent.invoke({\"messages\": [HumanMessage(\"我喜欢吃美食\")]}, config)\n",
+    "agent.invoke({\"messages\": [HumanMessage(\"我喜欢运动\")]}, config)\n",
+    "\n",
+    "result = agent.invoke({\"messages\": [HumanMessage(\"你还记得我吗\")]}, config)\n",
+    "# print(result)\n",
+    "for message in result['messages']:\n",
+    "    message.pretty_print()"
+   ],
+   "id": "1ed1327c973d75cc",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001B[1m Human Message \u001B[0m=================================\n",
+      "\n",
+      "Here is a summary of the conversation to date:\n",
+      "\n",
+      "## SESSION INTENT\n",
+      "\n",
+      "开放式、友好的中文闲聊会话，无具体交付物。用户自称“胖哥”，目前已透露两大爱好：美食与运动。整体目标是延续话题、维持轻松氛围，进一步了解他的偏好，从而围绕美食/运动展开聊天或提供推荐。\n",
+      "\n",
+      "## SUMMARY\n",
+      "\n",
+      "- 用户为“胖哥”，须以中文称呼；全会话均使用中文。\n",
+      "- 话题线索已在两条线上推进：\n",
+      "  1. **美食**：胖哥说“我喜欢吃美食”。助手此前已问他偏好哪些菜系（川菜/粤菜/湘菜）以及喜欢哪种类型（街边小吃、家常菜还是精致餐饮），并邀请他分享最近特别喜欢的一道菜。胖哥尚未回答此组问题。\n",
+      "  2. **运动（新增）**：胖哥随后说“我喜欢运动”。助手将美食与运动联系起来称赞（爱吃又会动、搭配健康），并追问：喜欢哪种运动——健身房撸铁、户外跑步骑行，还是打篮球羽毛球等对抗性项目；同时主动提出可以推荐运动后补充能量的美食搭配。\n",
+      "- 尚无任何结论、决定或策略形成；没有选项被否决。\n",
+      "- 需要注意的是，胖哥对“美食偏好”相关问题尚未作答，该线索仍处于待回应状态。\n",
+      "\n",
+      "## ARTIFACTS\n",
+      "\n",
+      "None.\n",
+      "\n",
+      "## NEXT STEPS\n",
+      "\n",
+      "- 等待胖哥回复：他喜欢哪种运动（健身房/户外/球类对抗等）。\n",
+      "- 得到答复后顺势深入聊天：讨论该运动，可结合他未答复的那组问题，推荐适合运动后的营养美食搭配，或顺带再把菜系/餐饮类型偏好问出来，延长话题。\n",
+      "- 不要重复寒暄，也不要重复之前已问过的两组问题（菜系类型与偏好风格、运动类型选择）。\n",
+      "- 保持中文、热情随意的口吻继续对话。\n",
+      "================================\u001B[1m Human Message \u001B[0m=================================\n",
+      "\n",
+      "你还记得我吗\n",
+      "==================================\u001B[1m Ai Message \u001B[0m==================================\n",
+      "\n",
+      "胖哥，这话说的——当然记得你呀！爱美食又爱运动，能吃会练，这反差感可太让人印象深刻了。咱俩这不正聊到运动嘛，我还等着听你细说呢。  \n",
+      "\n",
+      "不过不急，你先说说最近一次痛快出汗是啥时候？是去健身房撸铁，还是户外疯跑了一圈？反正不管哪种，你要是一会儿饿了，我脑子里的运动后美食搭配可已经在排队了😄\n"
+     ]
+    }
+   ],
+   "execution_count": 24
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

lg/node_error.ipynb

@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-09-08T07:56:57.833862800Z",
+     "start_time": "2026-09-08T07:56:51.946284300Z"
+    }
+   },
+   "source": [
+    "import asyncio\n",
+    "import logging\n",
+    "\n",
+    "from IPython.display import display\n",
+    "from langchain_core.runnables import RunnableConfig\n",
+    "from langgraph.constants import START, END\n",
+    "from langgraph.graph import StateGraph\n",
+    "from langgraph.types import RetryPolicy\n",
+    "from pydantic import BaseModel\n",
+    "from requests import HTTPError\n",
+    "\n",
+    "logger = logging.getLogger(__name__)\n",
+    "\n",
+    "\n",
+    "# 全局状态\n",
+    "class EmptyState(BaseModel):\n",
+    "    pass\n",
+    "\n",
+    "async def node_a(state: EmptyState, config: RunnableConfig) -> EmptyState:\n",
+    "    print(\"11111111111\")\n",
+    "    await asyncio.sleep(5)\n",
+    "    raise HTTPError(\"node_a\")\n",
+    "\n",
+    "\n",
+    "builder = StateGraph(state_schema=EmptyState)\n",
+    "builder.add_node(\"node_a\", node_a, retry_policy=RetryPolicy(max_attempts=3), timeout=1)\n",
+    "\n",
+    "builder.add_edge(START, \"node_a\")\n",
+    "builder.add_edge(\"node_a\", END)\n",
+    "\n",
+    "workflow = builder.compile()\n",
+    "try:\n",
+    "    result = await workflow.ainvoke({})\n",
+    "    # print(result)\n",
+    "except HTTPError as e:\n",
+    "    pass\n",
+    "\n",
+    "\n",
+    "display(workflow)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11111111111\n",
+      "11111111111\n",
+      "11111111111\n"
+     ]
+    },
+    {
+     "ename": "NodeTimeoutError",
+     "evalue": "Node 'node_a' exceeded its run timeout of 1.000s (elapsed: 1.007s).",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mTimeoutError\u001B[39m                              Traceback (most recent call last)",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\Desktop\\python-develop\\lotteryServer\\.venv\\Lib\\site-packages\\langgraph\\pregel\\_retry.py:489\u001B[39m, in \u001B[36m_arun_with_timeout\u001B[39m\u001B[34m(task, config, timeout, attempt_ctx, stream)\u001B[39m\n\u001B[32m    488\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m489\u001B[39m     \u001B[38;5;28;01mawait\u001B[39;00m watchdog\n\u001B[32m    490\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m asyncio.TimeoutError \u001B[38;5;28;01mas\u001B[39;00m exc:\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\Desktop\\python-develop\\lotteryServer\\.venv\\Lib\\site-packages\\langgraph\\pregel\\_retry.py:419\u001B[39m, in \u001B[36m_run_timeout_watchdog\u001B[39m\u001B[34m(run_timeout_s)\u001B[39m\n\u001B[32m    418\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m asyncio.sleep(run_timeout_s)\n\u001B[32m--> \u001B[39m\u001B[32m419\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m asyncio.TimeoutError\n",
+      "\u001B[31mTimeoutError\u001B[39m: ",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001B[31mNodeTimeoutError\u001B[39m                          Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[25]\u001B[39m\u001B[32m, line 36\u001B[39m\n\u001B[32m     32\u001B[39m workflow = builder.compile()\n\u001B[32m     33\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m     34\u001B[39m     result = \u001B[38;5;28;01mawait\u001B[39;00m workflow.ainvoke({})\n\u001B[32m     35\u001B[39m     \u001B[38;5;66;03m# print(result)\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m36\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m HTTPError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[32m     37\u001B[39m     \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[32m     38\u001B[39m \n\u001B[32m     39\u001B[39m \n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\Desktop\\python-develop\\lotteryServer\\.venv\\Lib\\site-packages\\langgraph\\pregel\\main.py:4090\u001B[39m, in \u001B[36mPregel.ainvoke\u001B[39m\u001B[34m(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, control, version, **kwargs)\u001B[39m\n\u001B[32m   4087\u001B[39m             chunks.append(chunk)\n\u001B[32m   4088\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   4089\u001B[39m     \u001B[38;5;66;03m# v1: collect interrupts from updates stream\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m4090\u001B[39m     \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mfor\u001B[39;00m chunk \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m.astream(\n\u001B[32m   4091\u001B[39m         \u001B[38;5;28minput\u001B[39m,\n\u001B[32m   4092\u001B[39m         config,\n\u001B[32m   4093\u001B[39m         context=context,\n\u001B[32m   4094\u001B[39m         stream_mode=(\n\u001B[32m   4095\u001B[39m             [\u001B[33m\"\u001B[39m\u001B[33mupdates\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mvalues\u001B[39m\u001B[33m\"\u001B[39m] \u001B[38;5;28;01mif\u001B[39;00m stream_mode == \u001B[33m\"\u001B[39m\u001B[33mvalues\u001B[39m\u001B[33m\"\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m stream_mode\n\u001B[32m   4096\u001B[39m         ),\n\u001B[32m   4097\u001B[39m         print_mode=print_mode,\n\u001B[32m   4098\u001B[39m         output_keys=output_keys,\n\u001B[32m   4099\u001B[39m         interrupt_before=interrupt_before,\n\u001B[32m   4100\u001B[39m         interrupt_after=interrupt_after,\n\u001B[32m   4101\u001B[39m         durability=durability,\n\u001B[32m   4102\u001B[39m         control=control,\n\u001B[32m   4103\u001B[39m         **kwargs,\n\u001B[32m   4104\u001B[39m     ):\n\u001B[32m   4105\u001B[39m         \u001B[38;5;28;01mif\u001B[39;00m stream_mode == \u001B[33m\"\u001B[39m\u001B[33mvalues\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m   4106\u001B[39m             \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(chunk) == \u001B[32m2\u001B[39m:\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\Desktop\\python-develop\\lotteryServer\\.venv\\Lib\\site-packages\\langgraph\\pregel\\main.py:3440\u001B[39m, in \u001B[36mPregel.astream\u001B[39m\u001B[34m(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, control, subgraphs, debug, version, **kwargs)\u001B[39m\n\u001B[32m   3438\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m task \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m loop.amatch_cached_writes():\n\u001B[32m   3439\u001B[39m     loop.output_writes(task.id, task.writes, cached=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m-> \u001B[39m\u001B[32m3440\u001B[39m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mfor\u001B[39;00m _ \u001B[38;5;129;01min\u001B[39;00m runner.atick(\n\u001B[32m   3441\u001B[39m     [t \u001B[38;5;28;01mfor\u001B[39;00m t \u001B[38;5;129;01min\u001B[39;00m loop.tasks.values() \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m t.writes],\n\u001B[32m   3442\u001B[39m     timeout=\u001B[38;5;28mself\u001B[39m.step_timeout,\n\u001B[32m   3443\u001B[39m     get_waiter=get_waiter,\n\u001B[32m   3444\u001B[39m     schedule_task=loop.aaccept_push,\n\u001B[32m   3445\u001B[39m ):\n\u001B[32m   3446\u001B[39m     \u001B[38;5;66;03m# emit output\u001B[39;00m\n\u001B[32m   3447\u001B[39m     \u001B[38;5;28;01mfor\u001B[39;00m o \u001B[38;5;129;01min\u001B[39;00m _output(\n\u001B[32m   3448\u001B[39m         stream_mode,\n\u001B[32m   3449\u001B[39m         print_mode,\n\u001B[32m   (...)\u001B[39m\u001B[32m   3455\u001B[39m         _state_mapper,\n\u001B[32m   3456\u001B[39m     ):\n\u001B[32m   3457\u001B[39m         \u001B[38;5;28;01myield\u001B[39;00m o\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\Desktop\\python-develop\\lotteryServer\\.venv\\Lib\\site-packages\\langgraph\\pregel\\_runner.py:396\u001B[39m, in \u001B[36mPregelRunner.atick\u001B[39m\u001B[34m(self, tasks, reraise, timeout, retry_policy, get_waiter, schedule_task)\u001B[39m\n\u001B[32m    394\u001B[39m scheduled_error_handler = \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[32m    395\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m396\u001B[39m     \u001B[38;5;28;01mawait\u001B[39;00m arun_with_retry(\n\u001B[32m    397\u001B[39m         t,\n\u001B[32m    398\u001B[39m         retry_policy,\n\u001B[32m    399\u001B[39m         stream=\u001B[38;5;28mself\u001B[39m.use_astream,\n\u001B[32m    400\u001B[39m         configurable={\n\u001B[32m    401\u001B[39m             CONFIG_KEY_CALL: partial(\n\u001B[32m    402\u001B[39m                 _acall,\n\u001B[32m    403\u001B[39m                 weakref.ref(t),\n\u001B[32m    404\u001B[39m                 stream=\u001B[38;5;28mself\u001B[39m.use_astream,\n\u001B[32m    405\u001B[39m                 retry_policy=retry_policy,\n\u001B[32m    406\u001B[39m                 futures=weakref.ref(futures),\n\u001B[32m    407\u001B[39m                 schedule_task=schedule_task,\n\u001B[32m    408\u001B[39m                 submit=\u001B[38;5;28mself\u001B[39m.submit,\n\u001B[32m    409\u001B[39m                 loop=loop,\n\u001B[32m    410\u001B[39m             ),\n\u001B[32m    411\u001B[39m         },\n\u001B[32m    412\u001B[39m     )\n\u001B[32m    413\u001B[39m     \u001B[38;5;28mself\u001B[39m.commit(t, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[32m    414\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m exc:\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\Desktop\\python-develop\\lotteryServer\\.venv\\Lib\\site-packages\\langgraph\\pregel\\_retry.py:745\u001B[39m, in \u001B[36marun_with_retry\u001B[39m\u001B[34m(task, retry_policy, stream, match_cached_writes, configurable)\u001B[39m\n\u001B[32m    743\u001B[39m         \u001B[38;5;28;01mbreak\u001B[39;00m\n\u001B[32m    744\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m task.proc.ainvoke(task.input, config)\n\u001B[32m--> \u001B[39m\u001B[32m745\u001B[39m result = \u001B[38;5;28;01mawait\u001B[39;00m _arun_with_timeout(\n\u001B[32m    746\u001B[39m     task, config, resolved_timeout, attempt_ctx, stream=stream\n\u001B[32m    747\u001B[39m )\n\u001B[32m    748\u001B[39m _finish_timed_attempt(config, attempt_ctx)\n\u001B[32m    749\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m stream:\n\u001B[32m    750\u001B[39m     \u001B[38;5;66;03m# if successful, end\u001B[39;00m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\Desktop\\python-develop\\lotteryServer\\.venv\\Lib\\site-packages\\langgraph\\pregel\\_retry.py:496\u001B[39m, in \u001B[36m_arun_with_timeout\u001B[39m\u001B[34m(task, config, timeout, attempt_ctx, stream)\u001B[39m\n\u001B[32m    494\u001B[39m         bg.cancel()\n\u001B[32m    495\u001B[39m         bg.add_done_callback(_drain_cancelled)\n\u001B[32m--> \u001B[39m\u001B[32m496\u001B[39m         \u001B[38;5;28;01mraise\u001B[39;00m NodeTimeoutError(\n\u001B[32m    497\u001B[39m             task.name,\n\u001B[32m    498\u001B[39m             elapsed,\n\u001B[32m    499\u001B[39m             kind=kind,\n\u001B[32m    500\u001B[39m             idle_timeout=idle_timeout_s,\n\u001B[32m    501\u001B[39m             run_timeout=run_timeout_s,\n\u001B[32m    502\u001B[39m         ) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mexc\u001B[39;00m\n\u001B[32m    503\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\n\u001B[32m    504\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mkind\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m timeout watchdog completed without raising TimeoutError\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    505\u001B[39m     )\n\u001B[32m    506\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33mtimeout wait completed without task or watchdog\u001B[39m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[31mNodeTimeoutError\u001B[39m: Node 'node_a' exceeded its run timeout of 1.000s (elapsed: 1.007s).",
+      "During task with name 'node_a' and id 'b35c22ae-a4be-38ef-f687-20b76ea2dd87'"
+     ]
+    }
+   ],
+   "execution_count": 25
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

lg/steam_out.ipynb

@@ -0,0 +1,75 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import StateGraph, START, END, MessagesState\n",
+    "from langchain.messages import HumanMessage\n",
+    "from langchain_deepseek import ChatDeepSeek\n",
+    "\n",
+    "from core.config.settings import settings\n",
+    "\n",
+    "model = ChatDeepSeek(\n",
+    "    model=\"deepseek-v4-flash\",\n",
+    "    api_key=settings.DEEPSEEK_API_KEY,\n",
+    "    extra_body={\n",
+    "        \"thinking\": {\n",
+    "            \"type\": \"disabled\"\n",
+    "        }\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "def llm_node(state: MessagesState) -> MessagesState:\n",
+    "    messages = state[\"messages\"]\n",
+    "    response = model.invoke(messages)\n",
+    "\n",
+    "    return {\n",
+    "        \"messages\": [response],\n",
+    "    }\n",
+    "\n",
+    "builder = StateGraph(state_schema=MessagesState)\n",
+    "builder.add_node(\"llm_node\", llm_node)\n",
+    "builder.add_edge(START, \"llm_node\")\n",
+    "builder.add_edge(\"llm_node\", END)\n",
+    "\n",
+    "graph = builder.compile()\n",
+    "\n",
+    "# 使用流式输出\n",
+    "for chunk in graph.stream(\n",
+    "        {\n",
+    "            \"messages\":[HumanMessage(content=\"你好!\")]\n",
+    "        },\n",
+    "    stream_mode=[\"values\",\"messages\"],\n",
+    "):\n",
+    "    print(chunk)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

main.py

@@ -1,23 +1,24 @@
-import logging
+import structlog
 
 import models
 from contextlib import asynccontextmanager
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from sqlmodel import SQLModel
+
+from api.agent_api import agent_router
+from core.config.logging_cof import setup_logging
 from core.db import engine
 from core.config.settings import settings
 from api.scheduler_api import scheduler_router
 from api.lottery_api import lottery_router
 from core.task.dlt_scheduler import scheduler
+from exceptions.handlers import register_exception_handlers
+from lc.chef_agent import chef_agent
 
-# 配置日志
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-)
-
-logger = logging.getLogger(__name__)
+# 全局设置日志配置
+setup_logging()
+logger = structlog.getLogger(__name__)
 
 # lifespan生命周期
 @asynccontextmanager
@@ -27,18 +28,34 @@ async def lifespan(app: FastAPI):
     # 同步建表
     SQLModel.metadata.create_all(engine)
 
+    logger.info("服务启动，启动定时调度器")
     # 启动定时调度器
     scheduler.start()
 
+    logger.info("服务启动，创建携程初始化chef_agent任务")
+    # 异步初始化chef_agent
+    chef_agent.initialize()
+
+    logger.info("服务启动完成")
+    try:
         yield # 此处交出控制权，服务开始运行
+    finally:
         # ========== 关闭时执行 ==========
         logger.info("服务关闭，释放资源")
+        chef_agent.close()
         scheduler.shutdown()
 
 app = FastAPI(lifespan=lifespan)
+
+# 注册全局异常处理
+register_exception_handlers(app)
+
+# 注册路由
 app.include_router(scheduler_router)
 app.include_router(lottery_router)
+app.include_router(agent_router)
 
+# 注册中间件
 app.add_middleware(
     CORSMiddleware, # type: ignore[arg-type]
     allow_origins=settings.ALLOWED_ORIGINS,

pyproject.toml

@@ -1,11 +1,24 @@
 [project]
 name = "lotteryserver"
 version = "0.1.0"
-requires-python = ">=3.14"
+requires-python = ">=3.13"
 dependencies = [
     "apscheduler==3.11.3",
     "fastapi>=0.141.1",
     "ipykernel>=7.3.0",
+    "ipython>=9.17.1",
+    "langchain>=1.4.0",
+    "langchain-core==1.6.2",
+    "langchain-deepseek==1.1.0",
+    "langchain-tavily==0.2.18",
+    "langgraph>=1.2.11",
+    "langgraph-checkpoint-postgres>=3.1.2",
+    "langgraph-checkpoint-sqlite==3.1.1",
+    "notebook>=7.6.2",
+    "openai==3.8.0",
+    "psycopg>=3.3.5",
+    "psycopg-binary>=3.3.5",
+    "psycopg-pool>=3.3.1",
     "pydantic==2.13.5",
     "pydantic-settings==2.15.0",
     "pymysql==1.2.0",
@@ -13,5 +26,6 @@ dependencies = [
     "sqlalchemy>=2.0.52",
     "sqlmodel==0.0.42",
     "starlette>=1.6.0",
+    "structlog>=26.1.0",
     "uvicorn==0.52.4",
 ]

services/agent_server/chef_service.py

@@ -0,0 +1,29 @@
+from typing import Iterator
+
+from langchain_core.messages import HumanMessage
+from langchain_core.runnables import RunnableConfig
+
+from lc.chef_agent import chef_agent
+
+
+class ChefService:
+
+    @staticmethod
+    def chat(
+        thread_id: str,
+        message: str,
+    ):
+        config: RunnableConfig = {"configurable": {"thread_id": thread_id}}
+        result = chef_agent.agent.invoke({"messages": [HumanMessage(content=message)]}, config)
+
+        return result
+
+    @staticmethod
+    def chat_stream(thread_id: str, message: str) -> Iterator[str]:
+        config: RunnableConfig = {"configurable": {"thread_id": thread_id}}
+        result = chef_agent.agent.stream({"messages": [("user", message)]}, config=config, stream_mode="messages")
+
+        for message_chunk, metadata in result:
+            content = message_chunk.content
+            if content:
+                yield content

test.py

@@ -0,0 +1,20 @@
+a: int | None = None
+
+def f(v: int) -> int:
+    pass
+
+def f2(v: int) -> int:
+    a = 5
+
+f(a)
+
+class Test:
+    def __init__(self):
+        self.a: int | None = None
+
+    def f(self, v: int) -> int:
+        pass
+
+    def f2(self):
+        self.a = 5
+        self.f(self.a)