[PYTHON/LANGCHAIN] RunnableSequence 클래스 : get_graph 메소드를 사용해 Graph 객체 구하기

■ RunnableSequence 클래스의 get_graph 메소드를 사용해 Graph 객체를 구하는 방법을 보여준다.

※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.

▶ main.py


from dotenv                           import load_dotenv
from langchain_community.vectorstores import FAISS
from langchain_openai                 import OpenAIEmbeddings
from langchain_core.prompts           import ChatPromptTemplate
from langchain_openai                 import ChatOpenAI
from langchain_core.runnables         import RunnablePassthrough
from langchain_core.output_parsers    import StrOutputParser

load_dotenv()

faiss = FAISS.from_texts(["harrison worked at kensho"], embedding = OpenAIEmbeddings())

vectorStoreRetriever = faiss.as_retriever()

chatPromptTemplateString = """Answer the question based only on the following context :
{context}

Question : {question}
"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

chatOpenAI = ChatOpenAI()

runnableSequence = {"context" : vectorStoreRetriever, "question" : RunnablePassthrough()} | chatPromptTemplate | chatOpenAI | StrOutputParser()

graph = runnableSequence.get_graph()

from dotenv import load_dotenv

from langchain_community.vectorstores import FAISS

from langchain_openai import OpenAIEmbeddings

from langchain_core.prompts import ChatPromptTemplate

from langchain_openai import ChatOpenAI

from langchain_core.runnables import RunnablePassthrough

from langchain_core.output_parsers import StrOutputParser

load_dotenv()

faiss = FAISS.from_texts(["harrison worked at kensho"], embedding = OpenAIEmbeddings())

vectorStoreRetriever = faiss.as_retriever()

chatPromptTemplateString = """Answer the question based only on the following context :

{context}

Question : {question}

"""

chatPromptTemplate = ChatPromptTemplate.from_template(chatPromptTemplateString)

chatOpenAI = ChatOpenAI()

runnableSequence = {"context" : vectorStoreRetriever, "question" : RunnablePassthrough()} | chatPromptTemplate | chatOpenAI | StrOutputParser()

graph = runnableSequence.get_graph()

▶ 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
dataclasses-json==0.6.7
distro==1.9.0
exceptiongroup==1.2.1
faiss-gpu==1.7.2
frozenlist==1.4.1
grandalf==0.8
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==0.1.9
langchain-text-splitters==0.2.1
langsmith==0.1.81
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.35.3
orjson==3.10.5
packaging==24.1
pydantic==2.7.4
pydantic_core==2.18.4
pyparsing==3.1.2
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-inspect==0.9.0
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

dataclasses-json==0.6.7

distro==1.9.0

exceptiongroup==1.2.1

faiss-gpu==1.7.2

frozenlist==1.4.1

grandalf==0.8

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==0.1.9

langchain-text-splitters==0.2.1

langsmith==0.1.81

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.35.3

orjson==3.10.5

packaging==24.1

pydantic==2.7.4

pydantic_core==2.18.4

pyparsing==3.1.2

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-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 faiss-gpu grandalf 명령을 실행했다.