text: import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import PGEmbeddingfrom langchain.document_loaders import TextLoaderfrom langchain.docstore.document import Documentos.environ["DATABASE_URL"] = getpass.getpass("Database Url:")    Database Url:Â·Â·Â·Â·Â·Â·Â·Â·loader = TextLoader("state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()connection_string = os.environ.get("DATABASE_URL")collection_name = "state_of_the_union"db = PGEmbedding.from_documents(    embedding=embeddings,    documents=docs,    collection_name=collection_name,    connection_string=connection_string,)query = "What did the president say about Ketanji Brown Jackson"docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)for doc, score in docs_with_score:    print("-" * 80)    print("Score: ", score)    print(doc.page_content)    print("-" * 80)Working with vectorstore in Postgresâ€‹Uploading a vectorstore in PGâ€‹db = PGEmbedding.from_documents(    embedding=embeddings,    documents=docs,    collection_name=collection_name,    connection_string=connection_string,    pre_delete_collection=False,)Create HNSW Indexâ€‹By default, the extension performs a sequential scan search, with 100% recall. You might consider creating an HNSW index for approximate nearest neighbor (ANN) search to speed up similarity_search_with_score execution time. To