[PYTHON/LANGCHAIN] RunnableSequence 클래스 : with_fallbacks 메소드를 사용해 파싱 성능이 더 나은 모델로 대체하는 RunnableWithFallbacks 객체 만들기

■ RunnableSequence 클래스의 with_fallbacks 메소드를 사용해 파싱 성능이 더 나은 모델로 대체하는 RunnableWithFallbacks 객체를 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_openai         import ChatOpenAI
from langchain.output_parsers import DatetimeOutputParser

load_dotenv()

chatPromptTemplate = ChatPromptTemplate.from_template(
    "what time was {event} (in %Y-%m-%dT%H:%M:%S.%fZ format - only return this value)"
)

chatOpenAI35RunnableSequence = ChatOpenAI()                | DatetimeOutputParser()
chatOpenAI40RunnableSequence = ChatOpenAI(model = "gpt-4") | DatetimeOutputParser()

only35RunnableSequence   = chatPromptTemplate | chatOpenAI35RunnableSequence
fallbackRunnableSequence = chatPromptTemplate | chatOpenAI35RunnableSequence.with_fallbacks([chatOpenAI40RunnableSequence])

try:
    print(only35RunnableSequence.invoke({"event" : "the superbowl in 2024"}))
except Exception as exception:
    print(f"Error : {exception}")

try:
    print(fallbackRunnableSequence.invoke({"event" : "the superbowl in 2024"}))
except Exception as exception:
    print(f"Error: {exception}")

"""
2024-02-05 23:30:00 # 에러가 발생할 것으로 예상했으나 정상 처리되었다.
2024-02-05 23:30:00
"""

from dotenv import load_dotenv

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import ChatOpenAI

from langchain.output_parsers import DatetimeOutputParser

load_dotenv()

chatPromptTemplate = ChatPromptTemplate.from_template(

"what time was {event} (in %Y-%m-%dT%H:%M:%S.%fZ format - only return this value)"

)

chatOpenAI35RunnableSequence = ChatOpenAI() | DatetimeOutputParser()

chatOpenAI40RunnableSequence = ChatOpenAI(model = "gpt-4") | DatetimeOutputParser()

only35RunnableSequence = chatPromptTemplate | chatOpenAI35RunnableSequence

fallbackRunnableSequence = chatPromptTemplate | chatOpenAI35RunnableSequence.with_fallbacks([chatOpenAI40RunnableSequence])

try:

print(only35RunnableSequence.invoke({"event" : "the superbowl in 2024"}))

except Exception as exception:

print(f"Error : {exception}")

try:

print(fallbackRunnableSequence.invoke({"event" : "the superbowl in 2024"}))

except Exception as exception:

print(f"Error: {exception}")

"""

2024-02-05 23:30:00 # 에러가 발생할 것으로 예상했으나 정상 처리되었다.

2024-02-05 23:30:00

"""

▶ 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==0.1.9
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==0.1.9

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.