text: = getpass.getpass("OpenAI API Key:")Creation and usage of the Vector Storeâ€‹from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Cassandrafrom langchain.document_loaders import TextLoaderfrom 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)embedding_function = OpenAIEmbeddings()table_name = "my_vector_db_table"docsearch = Cassandra.from_documents(    documents=docs,    embedding=embedding_function,    session=session,    keyspace=keyspace_name,    table_name=table_name,)query = "What did the president say about Ketanji Brown Jackson"docs = docsearch.similarity_search(query)## if you already have an index, you can load it and use it like this:# docsearch_preexisting = Cassandra(#     embedding=embedding_function,#     session=session,#     keyspace=keyspace_name,#     table_name=table_name,# )# docsearch_preexisting.similarity_search(query, k=2)print(docs[0].page_content)Maximal Marginal Relevance Searchesâ€‹In addition to using similarity search in the retriever object, you can also use mmr as retriever.retriever = docsearch.as_retriever(search_type="mmr")matched_docs = retriever.get_relevant_documents(query)for i, d in enumerate(matched_docs):    print(f"\n## Document {i}\n")    print(d.page_content)Or use max_marginal_relevance_search directly:found_docs =