[PYTHON/LANGCHAIN] FAISS 클래스 : as_retriever 메소드를 사용해 벡터 스토어 검색기 구하기

■ FAISS 클래스의 as_retriever 메소드를 사용해 벡터 스토어 검색기를 구하는 방법을 보여준다.

※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.

▶ 예제 코드 (PY)


from dotenv import load_dotenv

from langchain_community.document_loaders import TextLoader
from langchain_text_splitters             import CharacterTextSplitter
from langchain_openai                     import OpenAIEmbeddings
from langchain_community.vectorstores     import FAISS

load_dotenv()

textLoader = TextLoader("state_of_the_union.txt")

documentList = textLoader.load()

characterTextSplitter = CharacterTextSplitter(chunk_size = 1000, chunk_overlap = 0)

splitDocumentList = characterTextSplitter.split_documents(documentList)

openAIEmbeddings = OpenAIEmbeddings()

faiss = FAISS.from_documents(splitDocumentList, openAIEmbeddings)

vectorStoreRetriever = faiss.as_retriever()

from dotenv import load_dotenv

from langchain_community.document_loaders import TextLoader

from langchain_text_splitters import CharacterTextSplitter

from langchain_openai import OpenAIEmbeddings

from langchain_community.vectorstores import FAISS

load_dotenv()

textLoader = TextLoader("state_of_the_union.txt")

documentList = textLoader.load()

characterTextSplitter = CharacterTextSplitter(chunk_size = 1000, chunk_overlap = 0)

splitDocumentList = characterTextSplitter.split_documents(documentList)

openAIEmbeddings = OpenAIEmbeddings()

faiss = FAISS.from_documents(splitDocumentList, openAIEmbeddings)

vectorStoreRetriever = faiss.as_retriever()