text: by their app_id. Those IDs will be needed in additional to the model_id and optionally the version_id, so make note of all these IDs once you found the best model for your use case!Also note that given there are many models for images, video, text and audio understanding, you can build some interested AI agents that utilize the variety of AI models as experts to understand those data types.LLMsâ€‹To find the selection of LLMs in the Clarifai platform you can select the text to text model type here.from langchain.llms import Clarifaillm = Clarifai(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID)For more details, the docs on the Clarifai LLM wrapper provide a detailed walkthrough.Text Embedding Modelsâ€‹To find the selection of text embeddings models in the Clarifai platform you can select the text to embedding model type here.There is a Clarifai Embedding model in LangChain, which you can access with:from langchain.embeddings import ClarifaiEmbeddingsembeddings = ClarifaiEmbeddings(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID)For more details, the docs on the Clarifai Embeddings wrapper provide a detailed walthrough.Vectorstoreâ€‹Clarifai's vector DB was launched in 2016 and has been optimized to support live search queries. With workflows in the Clarifai platform, you data is automatically indexed by am embedding model and optionally other models as well to index that information in the DB for search. You can query the DB not only via the vectors but also filter by metadata matches, other AI predicted concepts, and even do geo-coordinate search. Simply create an application, select the appropriate base workflow for your type of data, and upload it (through the