{ "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 }