168 lines
6.5 KiB
Plaintext
168 lines
6.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2026-09-05T01:34:44.550869800Z",
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"start_time": "2026-09-05T01:34:44.535351500Z"
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}
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},
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"source": [
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"from typing import cast, Literal\n",
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"\n",
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"from langchain.agents.middleware.summarization import ContextMessages\n",
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"from langchain.chat_models import init_chat_model\n",
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"from langchain.agents import create_agent\n",
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"from langchain_core.runnables import RunnableConfig\n",
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"from langgraph.checkpoint.memory import InMemorySaver\n",
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"from langchain.agents.middleware import SummarizationMiddleware\n",
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"\n",
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"from core.config.settings import settings\n",
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"from langchain.tools import tool\n",
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"from langchain.messages import HumanMessage\n",
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"from langchain_core.language_models import BaseChatModel"
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],
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"outputs": [],
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"execution_count": 15
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-09-05T01:37:24.572533800Z",
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"start_time": "2026-09-05T01:37:24.555523100Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 初始化模型\n",
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"my_model = init_chat_model(\n",
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" model=\"deepseek-v4-flash\",\n",
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" api_key=settings.DEEPSEEK_API_KEY,\n",
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")\n",
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"\n",
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"# 初始化checkpointer 记忆管理的存储方式\n",
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"checkpointer = InMemorySaver()\n",
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"\n",
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"# 初始化记忆策略中间件\n",
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"middleware = SummarizationMiddleware(\n",
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" model=cast(BaseChatModel, my_model), # 消息摘要的记忆管理策略的模型\n",
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" trigger=cast(ContextMessages, (\"messages\", 3)), # 触发策略的条件\n",
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" keep=cast(ContextMessages, (\"messages\", 1)) # 触发策略后保留的消息条数\n",
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")"
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],
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"id": "b905be968f5c7761",
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"outputs": [],
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"execution_count": 22
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-09-05T01:37:26.422888800Z",
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"start_time": "2026-09-05T01:37:26.408643600Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 初始化agent\n",
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"agent = create_agent(\n",
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" model=cast(BaseChatModel, my_model),\n",
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" checkpointer=InMemorySaver(), # 短期记忆 通过thread_id进行记忆分组\n",
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" middleware=[middleware]\n",
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")"
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],
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"id": "cb5ae2fa4aeaf130",
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"outputs": [],
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"execution_count": 23
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-09-05T01:37:59.930524700Z",
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"start_time": "2026-09-05T01:37:27.968100300Z"
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}
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},
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"cell_type": "code",
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"source": [
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"config: RunnableConfig = {\"configurable\": {\"thread_id\": \"thread_1\"}}\n",
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"agent.invoke({\"messages\": [HumanMessage(\"你好，我是胖哥\")]}, config)\n",
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"agent.invoke({\"messages\": [HumanMessage(\"我喜欢吃美食\")]}, config)\n",
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"agent.invoke({\"messages\": [HumanMessage(\"我喜欢运动\")]}, config)\n",
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"\n",
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"result = agent.invoke({\"messages\": [HumanMessage(\"你还记得我吗\")]}, config)\n",
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"# print(result)\n",
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"for message in result['messages']:\n",
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" message.pretty_print()"
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],
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"id": "1ed1327c973d75cc",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"================================\u001B[1m Human Message \u001B[0m=================================\n",
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"\n",
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"Here is a summary of the conversation to date:\n",
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"\n",
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"## SESSION INTENT\n",
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"\n",
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"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",
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"\n",
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"## SUMMARY\n",
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"\n",
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"- 用户为“胖哥”，须以中文称呼；全会话均使用中文。\n",
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"- 话题线索已在两条线上推进：\n",
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" 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",
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" 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",
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"- 尚无任何结论、决定或策略形成；没有选项被否决。\n",
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"- 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",
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"\n",
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"## ARTIFACTS\n",
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"\n",
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"None.\n",
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"\n",
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"## NEXT STEPS\n",
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"\n",
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"- 等待胖哥回复：他喜欢哪种运动（健身房/户外/球类对抗等）。\n",
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"- 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",
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"- 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",
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"- 保持中文、热情随意的口吻继续对话。\n",
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"================================\u001B[1m Human Message \u001B[0m=================================\n",
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"\n",
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"你还记得我吗\n",
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"==================================\u001B[1m Ai Message \u001B[0m==================================\n",
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"\n",
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"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",
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"\n",
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"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"
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]
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}
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],
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"execution_count": 24
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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