text: _input// We used OpenAI text-embedding-ada-002 for this examples, where #length_of_vector_embedding = 1536Now let's install the rockset-python-client. This is used by langchain to talk to the Rockset database.pip install rocksetThis is it! Now you're ready to start writing some python code to store vector embeddings in Rockset, and querying the database to find texts similar to your query! We support 3 distance functions: COSINE_SIM, EUCLIDEAN_DIST and DOT_PRODUCT.Exampleâ€‹import osimport rockset# Make sure env variable ROCKSET_API_KEY is setROCKSET_API_KEY = os.environ.get("ROCKSET_API_KEY")ROCKSET_API_SERVER = (    rockset.Regions.usw2a1)  # Make sure this points to the correct Rockset regionrockset_client = rockset.RocksetClient(ROCKSET_API_SERVER, ROCKSET_API_KEY)COLLECTION_NAME = "langchain_demo"TEXT_KEY = "description"EMBEDDING_KEY = "description_embedding"Now let's use this client to create a Rockset Langchain Vectorstore!1. Inserting textsâ€‹from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderfrom langchain.vectorstores.rocksetdb import RocksetDBloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)Now we have the documents we want to insert. Let's create a Rockset vectorstore and insert these docs into the Rockset collection. We will use OpenAIEmbeddings to create embeddings for the texts, but you're free to use whatever you want.# Make sure the environment variable