text: To run, you should have an AnalyticDB instance up and running:Using AnalyticDB Cloud Vector Database. Click here to fast deploy it.from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import AnalyticDBSplit documents and get embeddings by call OpenAI APIfrom langchain.document_loaders import TextLoaderloader = 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()Connect to AnalyticDB by setting related ENVIRONMENTS.export PG_HOST={your_analyticdb_hostname}export PG_PORT={your_analyticdb_port} # Optional, default is 5432export PG_DATABASE={your_database} # Optional, default is postgresexport PG_USER={database_username}export PG_PASSWORD={database_password}Then store your embeddings and documents into AnalyticDBimport osconnection_string = AnalyticDB.connection_string_from_db_params(    driver=os.environ.get("PG_DRIVER", "psycopg2cffi"),    host=os.environ.get("PG_HOST", "localhost"),    port=int(os.environ.get("PG_PORT", "5432")),    database=os.environ.get("PG_DATABASE", "postgres"),    user=os.environ.get("PG_USER", "postgres"),    password=os.environ.get("PG_PASSWORD", "postgres"),)vector_db = AnalyticDB.from_documents(    docs,    embeddings,    connection_string=connection_string,)Query and retrieve dataquery = "What did the president say about Ketanji Brown Jackson"docs = vector_db.similarity_search(query)print(docs[0].page_content)    Tonight. I call on the Senate to: Pass the Freedom to