■ RunnableWithMessageHistory 클래스의 invoke 메소드를 사용해 채팅하는 방법을 보여준다.
※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.
▶ main.py
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from dotenv import load_dotenv from langchain_community.tools.tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain.agents import AgentExecutor from langchain.agents import create_tool_calling_agent from langchain_community.chat_message_histories import ChatMessageHistory from langchain_core.runnables.history import RunnableWithMessageHistory from langchain_core.messages import HumanMessage load_dotenv() chatOpenAI = ChatOpenAI(model = "gpt-4o-mini", temperature = 0) toolList = [TavilySearchResults(max_results = 1)] chatPromptTemplate = ChatPromptTemplate.from_messages( [ ("system" , "You are a helpful assistant. You may not need to use tools for every query - the user may just want to chat!"), ("placeholder", "{messages}" ), ("placeholder", "{agent_scratchpad}") ] ) runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, chatPromptTemplate) agentExecutor = AgentExecutor(agent = runnableSequence, tools = toolList, verbose = False) chatMessageHistory = ChatMessageHistory() runnableWithMessageHistory = RunnableWithMessageHistory( agentExecutor, lambda session_id : chatMessageHistory, input_messages_key = "messages", output_messages_key = "output" ) responseDictionary1 = runnableWithMessageHistory.invoke({"messages" : [HumanMessage("I'm Nemo!")]}, {"configurable" : {"session_id" : "unused"}}) print(responseDictionary1) """ { 'messages' : [HumanMessage(content = "I'm Nemo!", additional_kwargs = {}, response_metadata = {})], 'output' : 'Hi Nemo! How can I assist you today?' } """ responseDictionary2 = runnableWithMessageHistory.invoke({"messages" : [HumanMessage("What is my name?")]}, {"configurable" : {"session_id": "unused"}}) print(responseDictionary2) """ { 'messages' : [ HumanMessage(content = "I'm Nemo!", additional_kwargs = {}, response_metadata = {}), AIMessage(content = 'Hi Nemo! How can I assist you today?', additional_kwargs = {}, response_metadata = {}), HumanMessage(content = 'What is my name?', additional_kwargs = {}, response_metadata = {}) ], 'output' : 'Your name is Nemo!' } """ |
▶ requirements.txt
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aiohappyeyeballs==2.4.4 aiohttp==3.11.10 aiosignal==1.3.2 annotated-types==0.7.0 anyio==4.7.0 attrs==24.2.0 certifi==2024.12.14 charset-normalizer==3.4.0 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==0.3.12 langchain-community==0.3.12 langchain-core==0.3.25 langchain-openai==0.2.12 langchain-text-splitters==0.3.3 langsmith==0.2.3 marshmallow==3.23.1 multidict==6.1.0 mypy-extensions==1.0.0 numpy==2.2.0 openai==1.57.4 orjson==3.10.12 packaging==24.2 propcache==0.2.1 pydantic==2.10.3 pydantic-settings==2.7.0 pydantic_core==2.27.1 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==2.2.3 yarl==1.18.3 |
※ pip install python-dotenv langchain-community langchain-openai tavily-python 명령을 실행했다.