[PYTHON/LANGCHAIN] @chain 데코레이터 : 커스텀 함수 만들기

■ @chain 데코레이터를 사용해 커스텀 함수를 만드는 방법을 보여준다.

※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.

▶ main.py


from dotenv                        import load_dotenv
from langchain_core.prompts        import ChatPromptTemplate
from langchain_core.runnables      import chain
from langchain_openai              import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser

load_dotenv()

topicChatPromptTemplate = ChatPromptTemplate.from_template("Tell me a joke about {topic}"             )
jokeChatPromptTemplate  = ChatPromptTemplate.from_template("What is the subject of this joke : {joke}")

@chain
def executeCustomChain(text):
    topicChatPromptValue   = topicChatPromptTemplate.invoke({"topic" : text})
    topicResponseAIMessage = ChatOpenAI().invoke(topicChatPromptValue)
    topicResponseString    = StrOutputParser().invoke(topicResponseAIMessage)

    jokeRunnableSequence = jokeChatPromptTemplate | ChatOpenAI() | StrOutputParser()
    jokeResponseString   = jokeRunnableSequence.invoke({"joke" : topicResponseString})

    return jokeResponseString

responseString = executeCustomChain.invoke("bears")

print(responseString)

"""
The subject of this joke is a bear and his relationship with his girlfriend.
"""

from dotenv import load_dotenv

from langchain_core.prompts import ChatPromptTemplate

from langchain_core.runnables import chain

from langchain_openai import ChatOpenAI

from langchain_core.output_parsers import StrOutputParser

load_dotenv()

topicChatPromptTemplate = ChatPromptTemplate.from_template("Tell me a joke about {topic}" )

jokeChatPromptTemplate = ChatPromptTemplate.from_template("What is the subject of this joke : {joke}")

@chain

def executeCustomChain(text):

topicChatPromptValue = topicChatPromptTemplate.invoke({"topic" : text})

topicResponseAIMessage = ChatOpenAI().invoke(topicChatPromptValue)

topicResponseString = StrOutputParser().invoke(topicResponseAIMessage)

jokeRunnableSequence = jokeChatPromptTemplate | ChatOpenAI() | StrOutputParser()

jokeResponseString = jokeRunnableSequence.invoke({"joke" : topicResponseString})

return jokeResponseString

responseString = executeCustomChain.invoke("bears")

print(responseString)

"""

The subject of this joke is a bear and his relationship with his girlfriend.

"""

▶ requirements.txt


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==3.0.0
langchain==0.2.5
langchain-core==0.2.9
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.81
multidict==6.0.5
numpy==1.26.4
openai==1.35.3
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
tenacity==8.4.1
tiktoken==0.7.0
tqdm==4.66.4
typing_extensions==4.12.2
urllib3==2.2.2
yarl==1.9.4

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==3.0.0

langchain==0.2.5

langchain-core==0.2.9

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.81

multidict==6.0.5

numpy==1.26.4

openai==1.35.3

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

tenacity==8.4.1

tiktoken==0.7.0

tqdm==4.66.4

typing_extensions==4.12.2

urllib3==2.2.2

yarl==1.9.4

※ pip install python-dotenv langchain langchain_openai 명령을 실행했다.