■ RunnableSequence 클래스의 get_graph 메소드를 사용해 Graph 객체를 구하는 방법을 보여준다.
※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.
▶ main.py
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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
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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 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.