■ AgentExecutor 클래스의 invoke 메소드 사용시 채팅 히스토리를 설정하는 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_community.document_loaders import WebBaseLoader from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_openai import OpenAIEmbeddings from langchain_community.vectorstores import FAISS from langchain.tools.retriever import create_retriever_tool from langchain_openai import ChatOpenAI from langchain import hub from langchain.agents import create_tool_calling_agent from langchain.agents import AgentExecutor from langchain_core.messages import HumanMessage from langchain_core.messages import AIMessage load_dotenv() chatOpenAI = ChatOpenAI(model = "gpt-4o") tavilySearchResults = TavilySearchResults(max_results = 2) webBaseLoader = WebBaseLoader("https://docs.smith.langchain.com/overview") documentList = webBaseLoader.load() recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200) splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList) openAIEmbeddings = OpenAIEmbeddings() faiss = FAISS.from_documents(splitDocumentList, openAIEmbeddings) vectorStoreRetriever = faiss.as_retriever() vectorStoreRetrieverTool = create_retriever_tool( vectorStoreRetriever, "langsmith_search", "Search for information about LangSmith. For any questions about LangSmith, you must use this tool!", ) toolList = [tavilySearchResults, vectorStoreRetrieverTool] chatPromptTemplate = hub.pull("hwchase17/openai-functions-agent") runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, chatPromptTemplate) agentExecutor = AgentExecutor(agent = runnableSequence, tools = toolList) responseDictionary = agentExecutor.invoke( { "chat_history" : [ HumanMessage(content = "hi! my name is bob"), AIMessage(content = "Hello Bob! How can I assist you today?") ], "input" : "what's my name?" } ) print(responseDictionary) """ { 'chat_history' : [ HumanMessage(content = 'hi! my name is bob', additional_kwargs = {}, response_metadata = {}), AIMessage(content = 'Hello Bob! How can I assist you today?', additional_kwargs = {}, response_metadata = {}) ], 'input' : "what's my name?", 'output' : 'Your name is Bob.' } """ |
▶ requirements.txt
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aiohappyeyeballs==2.4.3 aiohttp==3.10.9 aiosignal==1.3.1 annotated-types==0.7.0 anyio==4.6.0 async-timeout==4.0.3 attrs==24.2.0 beautifulsoup4==4.12.3 bs4==0.0.2 certifi==2024.8.30 charset-normalizer==3.4.0 dataclasses-json==0.6.7 distro==1.9.0 exceptiongroup==1.2.2 faiss-gpu==1.7.2 frozenlist==1.4.1 greenlet==3.1.1 h11==0.14.0 httpcore==1.0.6 httpx==0.27.2 idna==3.10 jiter==0.6.1 jsonpatch==1.33 jsonpointer==3.0.0 langchain==0.3.3 langchain-community==0.3.2 langchain-core==0.3.10 langchain-openai==0.2.2 langchain-text-splitters==0.3.0 langsmith==0.1.132 marshmallow==3.22.0 multidict==6.1.0 mypy-extensions==1.0.0 numpy==1.26.4 openai==1.51.2 orjson==3.10.7 packaging==24.1 propcache==0.2.0 pydantic==2.9.2 pydantic-settings==2.5.2 pydantic_core==2.23.4 python-dotenv==1.0.1 PyYAML==6.0.2 regex==2024.9.11 requests==2.32.3 requests-toolbelt==1.0.0 sniffio==1.3.1 soupsieve==2.6 SQLAlchemy==2.0.35 tenacity==8.5.0 tiktoken==0.8.0 tqdm==4.66.5 typing-inspect==0.9.0 typing_extensions==4.12.2 urllib3==2.2.3 yarl==1.14.0 |
※ pip install python-dotenv langchain-community langchain-openai faiss-gpu bs4 명령을 실행했다.