text: = OpenAIEmbeddings()dataset_path = "hub://" + org_id + "/data"2. Create sample dataâ€‹You can generate a sample group chat conversation using ChatGPT with this prompt:Generate a group chat conversation with three friends talking about their day, referencing real places and fictional names. Make it funny and as detailed as possible.I've already generated such a chat in messages.txt. We can keep it simple and use this for our example.3. Ingest chat embeddingsâ€‹We load the messages in the text file, chunk and upload to ActiveLoop Vector store.with open("messages.txt") as f:    state_of_the_union = f.read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)pages = text_splitter.split_text(state_of_the_union)text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)texts = text_splitter.create_documents(pages)print(texts)dataset_path = "hub://" + org + "/data"embeddings = OpenAIEmbeddings()db = DeepLake.from_documents(    texts, embeddings, dataset_path=dataset_path, overwrite=True)Optional: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps.# with open("messages.txt") as f:#     state_of_the_union = f.read()# text_splitter =