USearch는 더 작고 빠른 단일 파일 Vector Search Engine입니다
USearch의 기본 기능은 FAISS와 동일하며, Approximate Nearest Neighbors 검색을 조사해본 적이 있다면 인터페이스가 익숙하게 느껴질 것입니다. FAISS는 고성능 vector search engine의 널리 인정받는 표준입니다. USearch와 FAISS는 모두 동일한 HNSW 알고리즘을 사용하지만, 설계 원칙에서 크게 다릅니다. USearch는 성능을 희생하지 않으면서도 컴팩트하고 광범위하게 호환되며, 사용자 정의 메트릭과 더 적은 의존성에 중점을 둡니다. 
pip install -qU  usearch langchain-community
OpenAIEmbeddings를 사용하려면 OpenAI API Key를 가져와야 합니다. 
import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

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

from langchain_community.document_loaders import TextLoader

loader = TextLoader("../../../extras/modules/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()

db = USearch.from_documents(docs, embeddings)

query = "What did the president say about Ketanji Brown Jackson"
docs = db.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.
similarity_search_with_score 메서드를 사용하면 문서뿐만 아니라 쿼리와의 거리 점수도 함께 반환할 수 있습니다. 반환되는 거리 점수는 L2 distance입니다. 따라서 점수가 낮을수록 더 좋습니다. 
docs_and_scores = db.similarity_search_with_score(query)

docs_and_scores[0]

(Document(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. \n\nTonight, 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. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', metadata={'source': '../../../extras/modules/state_of_the_union.txt'}),
 0.1845687)
Edit the source of this page on GitHub.
Connect these docs programmatically to Claude, VSCode, and more via MCP for real-time answers.
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