langgraph-3-memory.ipynb

{
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   "execution_count": 42,
   "id": "52e1b0b0-270b-42f1-ae82-a0e4ffc91b92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting grandalf\n",
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      "Installing collected packages: pyparsing, grandalf\n",
      "Successfully installed grandalf-0.8 pyparsing-3.1.2\n"
     ]
    }
   ],
   "source": [
    "# 安装langchain\n",
    "!pip install langchain\n",
    "!pip install langgraph\n",
    "!pip install langchain_openai\n",
    "!pip install grandalf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bc0e8701-63b9-4900-9e8c-c894031ad98e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_openai import ChatOpenAI\n",
    "from typing import Annotated, List, Sequence, TypedDict\n",
    "import operator\n",
    "\n",
    "# 初始化模型\n",
    "llm = ChatOpenAI(\n",
    "    openai_api_key='sk-cHI6zHo4Mye29vcO262b9dEe821a4dBb93253bAd75Ee9e09',\n",
    "    openai_api_base='http://192.168.1.8:15000/v1',\n",
    "    model_name='gpt-4-0125-preview',\n",
    "    temperature=0.0,\n",
    "    max_tokens=None\n",
    ")\n",
    "\n",
    "from typing import TypedDict, Optional\n",
    "\n",
    "class State(TypedDict):\n",
    "    messages: Annotated[Sequence[BaseMessage], operator.add]\n",
    "    history: Annotated[Sequence[BaseMessage], operator.add]\n",
    "    result: str\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "97ade328-3a5e-451b-b4ed-f60d984a2ac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "content='你好！有什么可以帮助你的吗？' response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 19, 'total_tokens': 36}, 'model_name': 'gpt-4-0125-preview', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None} id='run-f33d03b8-7bd1-4749-bb61-b387c71bf80f-0'\n",
      "{'chat': {'messages': [HumanMessage(content='你好')], 'history': [HumanMessage(content='你好！有什么可以帮助你的吗？')], 'result': '你好！有什么可以帮助你的吗？'}}\n"
     ]
    }
   ],
   "source": [
    "from langgraph.graph import StateGraph, END\n",
    "import time\n",
    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
    "\n",
    "# 创建图\n",
    "graph = StateGraph(State)\n",
    "\n",
    "\n",
    "prompt = ChatPromptTemplate.from_messages(\n",
    "    [\n",
    "        (\n",
    "            \"system\",\n",
    "            \"你是一个助手\",\n",
    "        ),\n",
    "        MessagesPlaceholder(variable_name=\"history\"),\n",
    "        MessagesPlaceholder(variable_name=\"messages\"),\n",
    "    ]\n",
    ")\n",
    "chat_agent = prompt | llm\n",
    "\n",
    "# 对话节点\n",
    "def chat(state):\n",
    "    # 模拟数据处理逻辑\n",
    "    result = chat_agent.invoke(state)\n",
    "    print(result)\n",
    "    state[\"history\"] += [HumanMessage(content=result.content)]\n",
    "    state['result'] = result.content\n",
    "    return state\n",
    "\n",
    "graph.add_node(\"chat\", chat)\n",
    "\n",
    "\n",
    "# 条件节点来决定是否继续聊天\n",
    "def should_continue(state: State):\n",
    "    return END\n",
    "\n",
    "graph.add_conditional_edges(\n",
    "    \"chat\", \n",
    "    should_continue, \n",
    "    {\"chat\": \"chat\", END: END}\n",
    ")\n",
    "\n",
    "\n",
    "# 设置入口点\n",
    "graph.set_entry_point(\"chat\")\n",
    "\n",
    "# 编译图形\n",
    "app = graph.compile()\n",
    "\n",
    "# 初始化状态\n",
    "initial_state = State(history=[], messages=[HumanMessage(content=\"你好\")], result=\"\")\n",
    "\n",
    "# 运行图处理流式数据\n",
    "state = initial_state\n",
    "for s in app.stream(state):\n",
    "    print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d65e023b-60df-4bc0-945f-83aa1e3359ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 还有一种方法将图保存到数据库中，历史记录持久化到数据库中\n",
    "# 请注意需要用下面代码替换或者插入到上面代码对应的位置\n",
    "from psycopg_pool import AsyncConnectionPool, ConnectionPool\n",
    "\n",
    "pool = ConnectionPool(\n",
    "    # Example configuration\n",
    "    conninfo=\"postgresql://username:password@ip:5433/app\",\n",
    "    max_size=20,\n",
    ")\n",
    "\n",
    "memory = PostgresSaver(\n",
    "    serializer=PickleCheckpointSerializer(),\n",
    "    sync_connection=pool,\n",
    ")\n",
    "\n",
    "graph = workflow.compile(checkpointer=memory)\n",
    "# 预测的时候加上会话id\n",
    "thread = {\"configurable\": {\"thread_id\": 1}}\n",
    "for s in graph.stream({\n",
    "    \"messages\": [\n",
    "        HumanMessage(content=meeting.question)\n",
    "    ], \"history\": history2\n",
    "}, thread):\n",
    "    print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61f4a2a4-21ab-400b-8831-364e018322e4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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