text: getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")activeloop_token = getpass.getpass("activeloop token:")embeddings = OpenAIEmbeddings()from 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()Create a dataset locally at ./deeplake/, then run similarity search. The Deeplake+LangChain integration uses Deep Lake datasets under the hood, so dataset and vector store are used interchangeably. To create a dataset in your own cloud, or in the Deep Lake storage, adjust the path accordingly.db = DeepLake(    dataset_path="./my_deeplake/", embedding_function=embeddings, overwrite=True)db.add_documents(docs)# or shorter# db = DeepLake.from_documents(docs, dataset_path="./my_deeplake/", embedding=embeddings, overwrite=True)query = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)print(docs[0].page_content)Later, you can reload the dataset without recomputing embeddingsdb = DeepLake(    dataset_path="./my_deeplake/", embedding_function=embeddings, read_only=True)docs = db.similarity_search(query)Deep Lake, for now, is single writer and multiple reader. Setting read_only=True helps to avoid acquiring the writer lock.Retrieval Question/Answeringâ€‹from langchain.chains import RetrievalQAfrom langchain.llms import OpenAIChatqa = RetrievalQA.from_chain_type(    llm=OpenAIChat(model="gpt-3.5-turbo"),    chain_type="stuff",