[PYTHON/LANGCHAIN] load 함수 : JSON 직렬화 가능 딕셔너리에서 RunnableSequence 객체 만들기

■ load 함수를 사용해 SON 직렬화 가능 딕셔너리에서 RunnableSequence 객체를 만드는 방법을 보여준다.

※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.

▶ main.py


import os

from dotenv                 import load_dotenv
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai       import ChatOpenAI
from langchain_core.load    import dumpd
from langchain_core.load    import load

load_dotenv()

chatPromptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system", "Translate the following text into {language} :"),
        ("user"  , "{text}")
    ]
)

chatOpenAI = ChatOpenAI(
    model       = "gpt-4",
    temperature = 0.7
)

runnableSequence1 = chatPromptTemplate | chatOpenAI

jsonSerizableDictionary = dumpd(runnableSequence1)

runnableSequence2 = load(jsonSerizableDictionary, secrets_map = {"OPENAI_API_KEY" : os.environ["OPENAI_API_KEY"]})

try:
    responseAIMessage = runnableSequence2.invoke(
        {
            "language": "Korean",
            "text": "I am a student"
        }
    )
    print("번역 결과 :", responseAIMessage.content)
except Exception as exception:
    print(f"오류 발생 : {str(exception)}")

"""
번역 결과 : 저는 학생입니다.
"""

import os

from dotenv import load_dotenv

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import ChatOpenAI

from langchain_core.load import dumpd

from langchain_core.load import load

load_dotenv()

chatPromptTemplate = ChatPromptTemplate.from_messages(

[

("system", "Translate the following text into {language} :"),

("user" , "{text}")

]

)

chatOpenAI = ChatOpenAI(

model = "gpt-4",

temperature = 0.7

)

runnableSequence1 = chatPromptTemplate | chatOpenAI

jsonSerizableDictionary = dumpd(runnableSequence1)

runnableSequence2 = load(jsonSerizableDictionary, secrets_map = {"OPENAI_API_KEY" : os.environ["OPENAI_API_KEY"]})

try:

responseAIMessage = runnableSequence2.invoke(

{

"language": "Korean",

"text": "I am a student"

}

)

print("번역 결과 :", responseAIMessage.content)

except Exception as exception:

print(f"오류 발생 : {str(exception)}")

"""

번역 결과 : 저는 학생입니다.

"""

▶ requirements.txt


annotated-types==0.7.0
anyio==4.6.2.post1
certifi==2024.8.30
charset-normalizer==3.4.0
colorama==0.4.6
distro==1.9.0
h11==0.14.0
httpcore==1.0.7
httpx==0.27.2
idna==3.10
jiter==0.7.1
jsonpatch==1.33
jsonpointer==3.0.0
langchain-core==0.3.21
langchain-openai==0.2.9
langsmith==0.1.146
openai==1.55.1
orjson==3.10.12
packaging==24.2
pydantic==2.10.1
pydantic_core==2.27.1
python-dotenv==1.0.1
PyYAML==6.0.2
regex==2024.11.6
requests==2.32.3
requests-toolbelt==1.0.0
sniffio==1.3.1
tenacity==9.0.0
tiktoken==0.8.0
tqdm==4.67.1
typing_extensions==4.12.2
urllib3==2.2.3

annotated-types==0.7.0

anyio==4.6.2.post1

certifi==2024.8.30

charset-normalizer==3.4.0

colorama==0.4.6

distro==1.9.0

h11==0.14.0

httpcore==1.0.7

httpx==0.27.2

idna==3.10

jiter==0.7.1

jsonpatch==1.33

jsonpointer==3.0.0

langchain-core==0.3.21

langchain-openai==0.2.9

langsmith==0.1.146

openai==1.55.1

orjson==3.10.12

packaging==24.2

pydantic==2.10.1

pydantic_core==2.27.1

python-dotenv==1.0.1

PyYAML==6.0.2

regex==2024.11.6

requests==2.32.3

requests-toolbelt==1.0.0

sniffio==1.3.1

tenacity==9.0.0

tiktoken==0.8.0

tqdm==4.67.1

typing_extensions==4.12.2

urllib3==2.2.3

※ install python-dotenv langchain-openai 명령을 실행했다.