ComponentsVector storesAwaDBOn this pageAwaDBAwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.This notebook shows how to use functionality related to the AwaDB.pip install awadbfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import AwaDBfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)docs = text_splitter.split_documents(documents)db = AwaDB.from_documents(docs)query = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)print(docs[0].page_content)    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​The returned distance score is between 0-1. 0 is dissimilar, 1 is the most similardocs = db.similarity_search_with_score(query)print(docs[0])    (Document(page_content='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.', metadata={'source': '../../../state_of_the_union.txt'}), 0.561813814013747)Restore the table created and added data before​AwaDB automatically persists added document dataIf you can restore the table you created and added before, you can just do this as below:awadb_client = awadb.Client()ret = awadb_client.Load("langchain_awadb")if ret:    print("awadb load table success")else:    print("awadb load table failed")awadb load table successPreviousAtlasNextAzure Cognitive SearchSimilarity search with scoreRestore the table created and added data before