text: metric is angularvector_store = Annoy.from_texts(texts, embeddings_func)# allows for custom annoy parameters, defaults are n_trees=100, n_jobs=-1, metric="angular"vector_store_v2 = Annoy.from_texts(    texts, embeddings_func, metric="dot", n_trees=100, n_jobs=1)vector_store.similarity_search("food", k=3)    [Document(page_content='pizza is great', metadata={}),     Document(page_content='I love salad', metadata={}),     Document(page_content='my car', metadata={})]# the score is a distance metric, so lower is bettervector_store.similarity_search_with_score("food", k=3)    [(Document(page_content='pizza is great', metadata={}), 1.0944390296936035),     (Document(page_content='I love salad', metadata={}), 1.1273186206817627),     (Document(page_content='my car', metadata={}), 1.1580758094787598)]Create VectorStore from docsâ€‹from langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)docs[:5]    [Document(page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty