■ | 연산자를 사용해 LLM 모델과 출력 파서의 체인을 만드는 방법을 보여준다.
▶ main.py
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import os from langchain_core.messages import HumanMessage, SystemMessage from langchain_core.output_parsers import StrOutputParser from langchain_openai import ChatOpenAI os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>" chatOpenAI = ChatOpenAI(model = "gpt-4") strOutputParser = StrOutputParser() runnableSequence = chatOpenAI | strOutputParser messageList = [ SystemMessage(content = "Translate the following from English into Korean"), HumanMessage (content = "hi!") ] resultString = runnableSequence.invoke(messageList) print(resultString) """ 안녕! """ |
▶ 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 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==2.4 langchain==0.2.3 langchain-core==0.2.5 langchain-openai==0.1.8 langchain-text-splitters==0.2.1 langsmith==0.1.75 multidict==6.0.5 numpy==1.26.4 openai==1.33.0 orjson==3.10.3 packaging==23.2 pydantic==2.7.3 pydantic_core==2.18.4 PyYAML==6.0.1 regex==2024.5.15 requests==2.32.3 sniffio==1.3.1 SQLAlchemy==2.0.30 tenacity==8.3.0 tiktoken==0.7.0 tqdm==4.66.4 typing_extensions==4.12.2 urllib3==2.2.1 yarl==1.9.4 |
※ pip install langchain langchain-openai 명령을 실행했다.