Great question! We will be adding a ChatGPT-based user-defined function this week (https://github.com/georgia-tech-db/eva/pull/655/).

With LLM-based functions, EVA will support more interesting queries like this:

  SELECT ChatGPT(TextSummarizer(SpeechRecognizer(audio)),
         "Is this video related to the Russia-Ukraine war?")
  FROM VIDEO_CLIPS;

Here, EVA sends the audio of each video clip to a speech recognition model on Hugging Face. It then sends the recognized text to a text summarizer model. EVA executes both models on local GPUs. Lastly, EVA sends the text summary to ChatGPT as a part of the prompt. The ChatGPT UDF is executed remotely.

The critical feature of EVA is that the query optimizer factors the dollar cost of running models for a given AI task (like a question-answering LLM). It picks the appropriate model pipeline with the lowest price that satisfies the user's accuracy requirement.