text: Redis Readme fileInstallingâ€‹pip install redisWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")Exampleâ€‹from langchain.embeddings import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores.redis import Redisfrom 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()If you're not interested in the keys of your entries you can also create your redis instance from the documents.rds = Redis.from_documents(    docs, embeddings, redis_url="redis://localhost:6379", index_name="link")If you're interested in the keys of your entries you have to split your docs in texts and metadatastexts = [d.page_content for d in docs]metadatas = [d.metadata for d in docs]rds, keys = Redis.from_texts_return_keys(    texts, embeddings, redis_url="redis://localhost:6379", index_name="link")rds.index_namequery = "What did the president say about Ketanji Brown Jackson"results = rds.similarity_search(query)print(results[0].page_content)print(rds.add_texts(["Ankush went to Princeton"]))query = "Princeton"results = rds.similarity_search(query)print(results[0].page_content)# Load from existing indexrds = Redis.from_existing_index(    embeddings, redis_url="redis://localhost:6379", index_name="link")query = "What did the president say about Ketanji Brown Jackson"results