We provide this functionality in Lantern cloud via our Lantern Extras extension: <https://github.com/lanterndata/lantern_extras https://github.com/lanterndata/l

We provide this functionality in Lantern cloud via our Lantern Extras extension: <https://github.com/lanterndata/lantern_extras https://github.com/lanterndata/lantern_extras> You can generate CLIP embeddings locally on the DB server via: SELECT abstract, introduction, figure1, clip_text(abstract) AS abstract_ai, clip_text(introduction) AS introduction_ai, clip_image(figure1) AS figure1_ai INTO papers_augmented FROM papers; Then you can search for embeddings via: SELECT abstract, introduction FROM papers_augmented ORDER BY clip_text(query) <=> abstract_ai LIMIT 10; The approach significantly decreases search latency and results in cleaner code. As an added bonus, EXPLAIN ANALYZE can now tell percentage of time spent in embedding generation vs search. The linked library enables embedding generation for a dozen open source models and proprietary APIs (list here: <https://lantern.dev/docs/develop/generate https://lantern.dev/docs/develop/generate>, and adding new ones is really easy.