text: via docstore idâ€‹vector_store.index_to_docstore_id    {0: '2d1498a8-a37c-4798-acb9-0016504ed798',     1: '2d30aecc-88e0-4469-9d51-0ef7e9858e6d',     2: '927f1120-985b-4691-b577-ad5cb42e011c',     3: '3056ddcf-a62f-48c8-bd98-b9e57a3dfcae'}some_docstore_id = 0  # texts[0]vector_store.docstore._dict[vector_store.index_to_docstore_id[some_docstore_id]]    Document(page_content='pizza is great', metadata={})# same document has distance 0vector_store.similarity_search_with_score_by_index(some_docstore_id, k=3)    [(Document(page_content='pizza is great', metadata={}), 0.0),     (Document(page_content='I love salad', metadata={}), 1.0734446048736572),     (Document(page_content='my car', metadata={}), 1.2895267009735107)]Save and loadâ€‹vector_store.save_local("my_annoy_index_and_docstore")    saving configloaded_vector_store = Annoy.load_local(    "my_annoy_index_and_docstore", embeddings=embeddings_func)# same document has distance 0loaded_vector_store.similarity_search_with_score_by_index(some_docstore_id, k=3)    [(Document(page_content='pizza is great', metadata={}), 0.0),     (Document(page_content='I love salad', metadata={}),