■ AgentExecutor 클래스를 만드는 방법을 보여준다.
※ OPENAI_API_KEY 및 TAVILY_API_KEY 환경 변수 값은 .env 파일에 정의한다.
▶ main.py
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from dotenv import load_dotenv from langchain_openai import ChatOpenAI from langchain_community.tools.tavily_search import TavilySearchResults from langchain_core.prompts import ChatPromptTemplate from langchain.agents import create_tool_calling_agent from langchain.agents import AgentExecutor load_dotenv() chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125") toolList = [TavilySearchResults(max_results = 1)] chatPromptTemplate = ChatPromptTemplate.from_messages( [ ("system" , "You are a helpful assistant."), ("placeholder", "{chat_history}" ), ("human" , "{input}" ), ("placeholder", "{agent_scratchpad}" ) ] ) agentRunnableSequence = create_tool_calling_agent(chatOpenAI, toolList, chatPromptTemplate) agentExecutor = AgentExecutor(agent = agentRunnableSequence, tools = toolList) |
▶ requirements.txt
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aiohttp==3.9.5 aiosignal==1.3.1 annotated-types==0.7.0 anyio==4.4.0 async-timeout==4.0.3 attrs==23.2.0 certifi==2024.6.2 charset-normalizer==3.3.2 dataclasses-json==0.6.7 distro==1.9.0 exceptiongroup==1.2.1 frozenlist==1.4.1 greenlet==3.0.3 h11==0.14.0 httpcore==1.0.5 httpx==0.27.0 idna==3.7 jsonpatch==1.33 jsonpointer==3.0.0 langchain==0.2.5 langchain-community==0.2.5 langchain-core==0.2.9 langchain-openai==0.1.8 langchain-text-splitters==0.2.1 langsmith==0.1.80 marshmallow==3.21.3 multidict==6.0.5 mypy-extensions==1.0.0 numpy==1.26.4 openai==1.34.0 orjson==3.10.5 packaging==24.1 pydantic==2.7.4 pydantic_core==2.18.4 python-dotenv==1.0.1 PyYAML==6.0.1 regex==2024.5.15 requests==2.32.3 sniffio==1.3.1 SQLAlchemy==2.0.31 tavily-python==0.3.3 tenacity==8.4.1 tiktoken==0.7.0 tqdm==4.66.4 typing-inspect==0.9.0 typing_extensions==4.12.2 urllib3==2.2.2 yarl==1.9.4 |
※ pip install python-dotenv langchain langchain-community langchain-openai tavily-python 명령을 실행했다.