text: import MyScale, MyScaleSettingsfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../../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()for i, d in enumerate(docs):    d.metadata = {"doc_id": i}docsearch = MyScale.from_documents(docs, embeddings)Similarity search with scoreâ€‹The returned distance score is cosine distance. Therefore, a lower score is better.meta = docsearch.metadata_columnoutput = docsearch.similarity_search_with_relevance_scores(    "What did the president say about Ketanji Brown Jackson?",    k=4,    where_str=f"{meta}.doc_id<10",)for d, dist in output:    print(dist, d.metadata, d.page_content[:20] + "...")Deleting your dataâ€‹docsearch.drop()PreviousMongoDB AtlasNextOpenSearchSetting up envrionmentsGet connection info and data schemaFilteringSimilarity search with scoreDeleting your dataCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.