text: (Document(page_content='I love salad', metadata={}), 1.0734446048736572),     (Document(page_content='my car', metadata={}), 1.2895267009735107)]Construct from scratchâ€‹import uuidfrom annoy import AnnoyIndexfrom langchain.docstore.document import Documentfrom langchain.docstore.in_memory import InMemoryDocstoremetadatas = [{"x": "food"}, {"x": "food"}, {"x": "stuff"}, {"x": "animal"}]# embeddingsembeddings = embeddings_func.embed_documents(texts)# embedding dimf = len(embeddings[0])# indexmetric = "angular"index = AnnoyIndex(f, metric=metric)for i, emb in enumerate(embeddings):    index.add_item(i, emb)index.build(10)# docstoredocuments = []for i, text in enumerate(texts):    metadata = metadatas[i] if metadatas else {}    documents.append(Document(page_content=text, metadata=metadata))index_to_docstore_id = {i: str(uuid.uuid4()) for i in range(len(documents))}docstore = InMemoryDocstore(    {index_to_docstore_id[i]: doc for i, doc in enumerate(documents)})db_manually = Annoy(    embeddings_func.embed_query, index, metric, docstore, index_to_docstore_id)db_manually.similarity_search_with_score("eating!", k=3)    [(Document(page_content='pizza is great', metadata={'x': 'food'}),      1.1314140558242798),     (Document(page_content='I love salad', metadata={'x': 'food'}),      1.1668788194656372),