Langgraph 기본적인 그래프 만들어보기

01 기본적인 랭그래프 만들어보기.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## StateGraph 상태 만들기\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import operator\n",
    "from typing import TypedDict, Annotated\n",
    "from langgraph.graph import StateGraph, START, END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "class State(TypedDict):\n",
    "    \"\"\"StateGraph의 상태를 정의하는 클래스\"\"\"\n",
    "\n",
    "    counter: int\n",
    "    alphabet: Annotated[list[str], operator.add]\n",
    "\n",
    "\n",
    "graph_builder = StateGraph(State)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 노드 정의\n",
    "def node_a(state: State):\n",
    "    state['counter'] += 1\n",
    "    state['alphabet'] = [\"hello\"]\n",
    "    return state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<langgraph.graph.state.StateGraph at 0x12ec8d850>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 노드 추가\n",
    "graph_builder.add_node(\"chat_bot\", node_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<langgraph.graph.state.StateGraph at 0x12ec8d850>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 노드 연결\n",
    "graph_builder.add_edge(START, \"chat_bot\")\n",
    "graph_builder.add_edge(\"chat_bot\", END)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = graph_builder.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Graph(nodes={'__start__': Node(id='__start__', name='__start__', data=<class 'pydantic.v1.main.LangGraphInput'>, metadata=None), 'chat_bot': Node(id='chat_bot', name='chat_bot', data=chat_bot(tags=None, recurse=True, func_accepts_config=False, func_accepts={'writer': False}), metadata=None), '__end__': Node(id='__end__', name='__end__', data=<class 'pydantic.v1.main.LangGraphOutput'>, metadata=None)}, edges=[Edge(source='__start__', target='chat_bot', data=None, conditional=False), Edge(source='chat_bot', target='__end__', data=None, conditional=False)])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.get_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "Image(graph.get_graph().draw_mermaid_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'counter': 1, 'alphabet': ['hello']}\n",
      "{'counter': 2, 'alphabet': ['hello', 'hello']}\n",
      "{'counter': 3, 'alphabet': ['hello', 'hello', 'hello']}\n"
     ]
    }
   ],
   "source": [
    "initial_state: State = {\"counter\": 0, \"alphabet\": []}\n",
    "\n",
    "state = initial_state\n",
    "for _ in range(3):\n",
    "  state = graph.invoke(state)\n",
    "  print(state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Message를 담는 그래프 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import TypedDict, Annotated\n",
    "\n",
    "from langgraph.graph import StateGraph, START, END\n",
    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
    "from typing_extensions import TypedDict\n",
    "\n",
    "from langgraph.graph import StateGraph\n",
    "from langgraph.graph.message import add_messages\n",
    "\n",
    "from langchain_core.messages import ChatMessage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class State(TypedDict):\n",
    "  \"\"\"State of the chat.\"\"\"\n",
    "  messages: Annotated[list[ChatMessage], add_messages]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 상태 그래프(State Graph) 생성\n",
    "graph_builder = StateGraph(State)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LLM 생성\n",
    "llm = ChatGoogleGenerativeAI(model=\"gemini-1.5-flash\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chatbot(state: State):\n",
    "    answer = llm.invoke(state[\"messages\"])\n",
    "    return {\"messages\": [answer]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_builder.add_node(\"chatbot\", chatbot)\n",
    "graph_builder.set_entry_point(\"chatbot\")    # add_edge(START, \"chatbot\")\n",
    "graph_builder.set_finish_point(\"chatbot\")   # add_edge(\"chatbot\", END)\n",
    "\n",
    "graph = graph_builder.compile()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython import display\n",
    "\n",
    "display.Image(graph.get_graph().draw_mermaid_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: 안녕\n",
      "Chatbot: 안녕하세요! 무엇을 도와드릴까요? \n",
      "\n",
      "User: 1+1은 뭘까?\n",
      "Chatbot: 1 + 1은 2입니다. \n",
      "\n",
      "User: 잘가~\n",
      "Chatbot: 안녕히 가세요! 다음에 또 만나요! \n",
      "\n",
      "User: bye\n",
      "Chatbot: Bye! It was nice talking to you. Have a great day! \n",
      "\n",
      "Chatbot: Bye!\n"
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    user_input = input(\"User: \")\n",
    "    if user_input.lower() in [\"exit\", \"quit\", \"q\"]:\n",
    "        print(\"Chatbot: Bye!\")\n",
    "        break\n",
    "\n",
    "    print(f\"User: {user_input}\")\n",
    "    for event in graph.stream({\"messages\": {\"role\": \"user\", \"content\": user_input}}):\n",
    "        for value in event.values():\n",
    "            print(f\"Chatbot: {value.get('messages')[-1].content}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Langgraph의 MessageState 사용하기\n",
    "\n",
    "랭그래프에서 제공하는 MessageState는 단순한 챗봇 구현에 사용할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import TypedDict\n",
    "\n",
    "from langgraph.graph import StateGraph\n",
    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
    "\n",
    "from langgraph.graph import StateGraph, MessagesState\n",
    "from langgraph.graph.message import add_messages\n",
    "\n",
    "\n",
    "graph_builder = StateGraph(MessagesState)\n",
    "\n",
    "llm = ChatGoogleGenerativeAI(model=\"gemini-1.5-flash\")\n",
    "\n",
    "def chatbot(state: State):\n",
    "    return {\"messages\": [llm.invoke(state[\"messages\"])]}\n",
    "\n",
    "graph_builder.add_node(\"chatbot\", chatbot)\n",
    "graph_builder.set_entry_point(\"chatbot\")   \n",
    "graph_builder.set_finish_point(\"chatbot\") \n",
    "\n",
    "graph = graph_builder.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'messages': [HumanMessage(content='안녕하세요!', additional_kwargs={}, response_metadata={}, id='62c825ff-e6a2-43ef-8abe-69bbb4577841'),\n",
       "  AIMessage(content='안녕하세요! 무엇을 도와드릴까요? \\n', additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'safety_ratings': [{'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability': 'NEGLIGIBLE', 'blocked': False}]}, id='run-02dc7bd2-071e-487a-b8a8-11dbc02c7eca-0', usage_metadata={'input_tokens': 5, 'output_tokens': 14, 'total_tokens': 19})],\n",
       " 'counter': 1}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from  langchain_core.messages import HumanMessage\n",
    "\n",
    "# TypedDict 대신 MessagesState를 상속하여 새로운 State를 만들어보자\n",
    "class State(MessagesState):\n",
    "    counter: int = 0\n",
    "\n",
    "graph_builder = StateGraph(State)\n",
    "\n",
    "llm = ChatGoogleGenerativeAI(model=\"gemini-1.5-flash\")\n",
    "\n",
    "def chatbot(state: State):\n",
    "    return {\n",
    "        'counter': state.get('counter', 0) + 1,\n",
    "        'messages': [llm.invoke(state['messages'])]\n",
    "    }\n",
    "\n",
    "graph_builder.add_node(\"chatbot\", chatbot)\n",
    "graph_builder.set_entry_point(\"chatbot\")   \n",
    "graph_builder.set_finish_point(\"chatbot\") \n",
    "\n",
    "graph = graph_builder.compile()\n",
    "\n",
    "\n",
    "# 초기 상태 설정\n",
    "initial_state = {\n",
    "    \"messages\": [HumanMessage(\"안녕하세요!\")],\n",
    "    \"counter\": 0\n",
    "}\n",
    "\n",
    "# 그래프 실행\n",
    "result = graph.invoke(initial_state)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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