■ StateGraph 클래스의 compile 메소드를 사용해 도구 강화 챗봇을 만드는 방법을 보여준다.
※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.
※ TAVILY_API_KEY 환경 변수 값은 .env 파일에 정의한다.
▶ main.py
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import json from dotenv import load_dotenv from langchain_openai import ChatOpenAI from langchain_community.tools.tavily_search import TavilySearchResults from typing_extensions import TypedDict from typing import Annotated from langgraph.graph.message import add_messages from langgraph.graph import StateGraph from langchain_core.messages import ToolMessage from langgraph.graph import END from langgraph.graph import START load_dotenv() chatOpenAI = ChatOpenAI(model = "gpt-4o-mini") tavilySearchResults = TavilySearchResults(max_results = 2) toolList = [tavilySearchResults] runnableBinding = chatOpenAI.bind_tools(toolList) class State(TypedDict): messageList : Annotated[list, add_messages] stateGraph = StateGraph(State) def chat(state : State): messageList = state["messageList"] return {"messageList" : [runnableBinding.invoke(messageList)]} stateGraph.add_node("chatbot_node", chat) class BasicToolNode: """A node that runs the tools requested in the last AIMessage.""" def __init__(self, toolList : list) -> None: self.toolNameDictionary = {tool.name : tool for tool in toolList} def __call__(self, inputDictionary : dict): if messageList := inputDictionary.get("messageList", []): message = messageList[-1] else: raise ValueError("No message found in input") toolMessageList = [] for toolCall in message.tool_calls: toolResultDictionaryList = self.toolNameDictionary[toolCall["name"]].invoke(toolCall["args"]) toolMessageList.append(ToolMessage(content = json.dumps(toolResultDictionaryList), name = toolCall["name"], tool_call_id = toolCall["id"])) return {"messageList" : toolMessageList} basicToolNode = BasicToolNode(toolList = [tavilySearchResults]) stateGraph.add_node("tool_node", basicToolNode) def routeToolNode(state : State): """ Use in the conditional_edge to route to the ToolNode if the last message has tool calls. Otherwise, route to the end. """ if isinstance(state, list): aiMessage = state[-1] elif messages := state.get("messageList", []): aiMessage = messages[-1] else: raise ValueError(f"No messages found in input state to tool_edge: {state}") if hasattr(aiMessage, "tool_calls") and len(aiMessage.tool_calls) > 0: return "tool_node" return END # `tools_condition` 함수는 챗봇이 도구를 사용하라고 요청하면 "tool_node"를 반환하고, 직접 응답해도 괜찮으면 "END"를 반환합니다. # 이 조건부 라우팅은 주요 에이전트 루프를 정의한다. stateGraph.add_conditional_edges( "chatbot_node", routeToolNode, # 다음 사전은 그래프에 조건의 출력을 특정 노드로 해석하도록 지시할 수 있습니다. # 기본적으로 항등 함수로 설정되지만 "tool_node"가 아닌 다른 이름의 노드를 사용하려면 사전의 값을 다른 것으로 업데이트할 수 있습니다. 예 : "tool_node" : "test_node" {"tool_node" : "tool_node", END : END} ) # 도구가 호출될 때마다 우리는 챗봇으로 돌아가 다음 단계를 결정한다. stateGraph.add_edge(START, "chatbot_node") stateGraph.add_edge("tool_node", "chatbot_node") compiledStateGraph = stateGraph.compile() def query(userInput : str, messageList = []): messageList.append(("user", userInput)) for addableUpdatesDict in compiledStateGraph.stream({"messageList": messageList}): if "chatbot_node" in addableUpdatesDict: for valueDictionary in addableUpdatesDict.values(): content = valueDictionary["messageList"][-1].content if content: print("Assistant :", content) while True: userInput = input("User : ") if userInput.lower() in ["quit", "exit", "q"]: print("Goodbye!") break query(userInput) |
▶ requirements.txt
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aiohappyeyeballs==2.4.4 aiohttp==3.11.11 aiosignal==1.3.2 annotated-types==0.7.0 anyio==4.7.0 attrs==24.3.0 certifi==2024.12.14 charset-normalizer==3.4.1 colorama==0.4.6 dataclasses-json==0.6.7 distro==1.9.0 frozenlist==1.5.0 greenlet==3.1.1 h11==0.14.0 httpcore==1.0.7 httpx==0.28.1 httpx-sse==0.4.0 idna==3.10 jiter==0.8.2 jsonpatch==1.33 jsonpointer==3.0.0 langchain==0.3.13 langchain-community==0.3.13 langchain-core==0.3.28 langchain-openai==0.2.14 langchain-text-splitters==0.3.4 langgraph==0.2.60 langgraph-checkpoint==2.0.9 langgraph-sdk==0.1.48 langsmith==0.2.6 marshmallow==3.23.2 msgpack==1.1.0 multidict==6.1.0 mypy-extensions==1.0.0 numpy==2.2.1 openai==1.58.1 orjson==3.10.12 packaging==24.2 propcache==0.2.1 pydantic==2.10.4 pydantic-settings==2.7.0 pydantic_core==2.27.2 python-dotenv==1.0.1 PyYAML==6.0.2 regex==2024.11.6 requests==2.32.3 requests-toolbelt==1.0.0 sniffio==1.3.1 SQLAlchemy==2.0.36 tavily-python==0.5.0 tenacity==9.0.0 tiktoken==0.8.0 tqdm==4.67.1 typing-inspect==0.9.0 typing_extensions==4.12.2 urllib3==2.3.0 yarl==1.18.3 |
※ pip install python-dotenv langchain_community langchain_openai langgraph tavily-python 명령을 실행했다.