scikit-learn은 k nearest neighbors의 일부 구현을 포함한 오픈 소스 머신 러닝 알고리즘 모음입니다. SKLearnVectorStore는 이 구현을 래핑하고 vector store를 json, bson (binary json) 또는 Apache Parquet 형식으로 저장할 수 있는 기능을 추가합니다.
이 노트북은 SKLearnVectorStore vector database를 사용하는 방법을 보여줍니다. 이 integration을 사용하려면 pip install -qU langchain-community로 langchain-community를 설치해야 합니다 
pip install -qU  scikit-learn

# # if you plan to use bson serialization, install also:
pip install -qU  bson

# # if you plan to use parquet serialization, install also:
pip install -qU  pandas pyarrow
OpenAI embeddings를 사용하려면 OpenAI key가 필요합니다. platform.openai.com/account/api-keys에서 key를 얻거나 다른 embeddings를 자유롭게 사용하셔도 됩니다. 
import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass("Enter your OpenAI key:")

​
기본 사용법

​
샘플 문서 corpus 로드하기

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

loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()

​
SKLearnVectorStore 생성, 문서 corpus 인덱싱 및 샘플 쿼리 실행

import tempfile

persist_path = os.path.join(tempfile.gettempdir(), "union.parquet")

vector_store = SKLearnVectorStore.from_documents(
    documents=docs,
    embedding=embeddings,
    persist_path=persist_path,  # persist_path and serializer are optional
    serializer="parquet",
)

query = "What did the president say about Ketanji Brown Jackson"
docs = vector_store.similarity_search(query)
print(docs[0].page_content)

Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

​
vector store 저장 및 로드하기

vector_store.persist()
print("Vector store was persisted to", persist_path)

Vector store was persisted to /var/folders/6r/wc15p6m13nl_nl_n_xfqpc5c0000gp/T/union.parquet

vector_store2 = SKLearnVectorStore(
    embedding=embeddings, persist_path=persist_path, serializer="parquet"
)
print("A new instance of vector store was loaded from", persist_path)

A new instance of vector store was loaded from /var/folders/6r/wc15p6m13nl_nl_n_xfqpc5c0000gp/T/union.parquet

docs = vector_store2.similarity_search(query)
print(docs[0].page_content)

Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

​
정리

os.remove(persist_path)
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