text: an application, select the appropriate base workflow for your type of data, and upload it (through the API as documented here or the UIs at clarifai.com).You an also add data directly from LangChain as well, and the auto-indexing will take place for you. You'll notice this is a little different than other vectorstores where you need to provde an embedding model in their constructor and have LangChain coordinate getting the embeddings from text and writing those to the index. Not only is it more convenient, but it's much more scalable to use Clarifai's distributed cloud to do all the index in the background.from langchain.vectorstores import Clarifaiclarifai_vector_db = Clarifai.from_texts(user_id=USER_ID, app_id=APP_ID, texts=texts, pat=CLARIFAI_PAT, number_of_docs=NUMBER_OF_DOCS, metadatas = metadatas)For more details, the docs on the Clarifai vector store provide a detailed walthrough.PreviousChromaNextClearMLInstallation and SetupModelsLLMsText Embedding ModelsVectorstoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.