text: osfrom getpass import getpassos.environ["OPENAI_API_KEY"] = getpass()# Please manually enter OpenAI KeyAuthenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the platform at app.activeloop.aiactiveloop_token = getpass("Activeloop Token:")os.environ["ACTIVELOOP_TOKEN"] = activeloop_tokenPrepare dataâ€‹Load all repository files. Here we assume this notebook is downloaded as the part of the langchain fork and we work with the python files of the langchain repo.If you want to use files from different repo, change root_dir to the root dir of your repo.ls "../../../.."from langchain.document_loaders import TextLoaderroot_dir = "../../../.."docs = []for dirpath, dirnames, filenames in os.walk(root_dir):    for file in filenames:        if file.endswith(".py") and "/.venv/" not in dirpath:            try:                loader = TextLoader(os.path.join(dirpath, file), encoding="utf-8")                docs.extend(loader.load_and_split())            except Exception as e:                passprint(f"{len(docs)}")Then, chunk the filesfrom langchain.text_splitter import CharacterTextSplittertext_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(docs)print(f"{len(texts)}")Then embed chunks and upload them to the DeepLake.This can take several minutes. from langchain.embeddings.openai import OpenAIEmbeddingsembeddings =