text: Falsefrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores.pgvector import PGVectorfrom langchain.document_loaders import TextLoaderfrom langchain.docstore.document import Documentloader = 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()# PGVector needs the connection string to the database.CONNECTION_STRING = "postgresql+psycopg2://harrisonchase@localhost:5432/test3"# # Alternatively, you can create it from enviornment variables.# import os# CONNECTION_STRING = PGVector.connection_string_from_db_params(#     driver=os.environ.get("PGVECTOR_DRIVER", "psycopg2"),#     host=os.environ.get("PGVECTOR_HOST", "localhost"),#     port=int(os.environ.get("PGVECTOR_PORT", "5432")),#     database=os.environ.get("PGVECTOR_DATABASE", "postgres"),#     user=os.environ.get("PGVECTOR_USER", "postgres"),#     password=os.environ.get("PGVECTOR_PASSWORD", "postgres"),# )Similarity Search with Euclidean Distance (Default)â€‹# The PGVector Module will try to create a table with the name of the collection.# So, make sure that the collection name is unique and the user has the permission to create a table.COLLECTION_NAME = "state_of_the_union_test"db = PGVector.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 =