text: The returned distance score is L2 distance. Therefore, a lower score is better.docs_and_scores = db.similarity_search_with_score(query)docs_and_scores[0]    (Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while youâ€™re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, Iâ€™d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyerâ€”an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nationâ€™s top legal minds, who will continue Justice Breyerâ€™s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'}),     0.36913747)It is also possible to do a search for documents similar to a given embedding vector using similarity_search_by_vector which accepts an embedding vector as a parameter instead of a string.embedding_vector = embeddings.embed_query(query)docs_and_scores = db.similarity_search_by_vector(embedding_vector)Saving and loadingâ€‹You can also save and load a FAISS index. This is useful so you don't have to recreate it everytime you use it.db.save_local("faiss_index")new_db = FAISS.load_local("faiss_index", embeddings)docs = new_db.similarity_search(query)docs[0]    Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting