text: embeddings for the texts, but you're free to use whatever you want.# Make sure the environment variable OPENAI_API_KEY is set upembeddings = OpenAIEmbeddings()docsearch = RocksetDB(    client=rockset_client,    embeddings=embeddings,    collection_name=COLLECTION_NAME,    text_key=TEXT_KEY,    embedding_key=EMBEDDING_KEY,)ids = docsearch.add_texts(    texts=[d.page_content for d in docs],    metadatas=[d.metadata for d in docs],)## If you go to the Rockset console now, you should be able to see this docs along with the metadata `source`2. Searching similar textsâ€‹Now let's try to search Rockset to find strings similar to our query string!query = "What did the president say about Ketanji Brown Jackson"output = docsearch.similarity_search_with_relevance_scores(    query, 4, RocksetDB.DistanceFunction.COSINE_SIM)print("output length:", len(output))for d, dist in output:    print(dist, d.metadata, d.page_content[:20] + "...")### output length: 4# 0.764990692109871 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...# 0.7485416901622112 {'source': '../../../state_of_the_union.txt'} And Iâ€™m taking robus...# 0.7468678973398306 {'source': '../../../state_of_the_union.txt'} And so many families...# 0.7436231261419488 {'source': '../../../state_of_the_union.txt'} Groups of citizens b...You can also use a where filter to prune your search space. You can add filters on text key, or any of the metadata fields. Note: Since Rockset stores