text: in position 0: invalid start byte from tiktoken for some repositories1. Index the code base (optional)â€‹You can directly skip this part and directly jump into using already indexed dataset. To begin with, first we will clone the repository, then parse and chunk the code base and use OpenAI indexing.git clone https://github.com/twitter/the-algorithm # replace any repository of your choiceLoad all files inside the repositoryimport osfrom langchain.document_loaders import TextLoaderroot_dir = "./the-algorithm"docs = []for dirpath, dirnames, filenames in os.walk(root_dir):    for file in filenames:        try:            loader = TextLoader(os.path.join(dirpath, file), encoding="utf-8")            docs.extend(loader.load_and_split())        except Exception as e:            passThen, chunk the filesfrom langchain.text_splitter import CharacterTextSplittertext_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(docs)Execute the indexing. This will take about ~4 mins to compute embeddings and upload to Activeloop. You can then publish the dataset to be public.username = "davitbun"  # replace with your username from app.activeloop.aidb = DeepLake(    dataset_path=f"hub://{username}/twitter-algorithm",    embedding_function=embeddings,)db.add_documents(texts)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