Pixeltable vs Supabase

class Videos(TableModel, name='videos'):
    video: pxt.Video
    title: pxt.String
    audio = extract_audio(video, format='mp3')
    duration_sec = pxtf.video.get_duration(video)
    scenes = video.scene_detect_content(threshold=8.0)

class Frames(TableModel, name='frames', base=Videos,
             iterator=frame_iterator(Videos.video, fps=1.0)):
    still = pxtf.image.resize(frame, (320, 180))
    __indexes__ = [pxt.EmbeddingIndex(frame, embedding=VISUAL)]

class Chunks(TableModel, name='chunks', base=Videos,
             iterator=audio_splitter(Videos.audio, duration=10.0)):
    transcript = transcribe(audio_segment, model='base.en').text.astype(pxt.String)
    __indexes__ = [pxt.EmbeddingIndex(transcript, embedding=SEMANTIC)]

Videos.insert([{'video': 'lecture.mp4', 'title': 'CS101'}])

const { frames } = await compute("/extract-frames", { video_url, fps: FRAME_FPS });
const { embeddings } = await compute("/embed-clip", { images_b64: frames });
const frameRows = await Promise.all(frames.map(async (b64, i) => {
  const path = `videos/${videoId}/frame_${i}.jpg`;
  await supabase.storage.from("frames").upload(path, decodeBase64(b64), {
    contentType: "image/jpeg", upsert: true,
  });
  return { video_id: videoId, frame_idx: i, embedding: embeddings[i] };
}));
await supabase.from("frames").insert(frameRows);

sim = Frames.frame.similarity(string=query)
return (
    Frames.order_by(sim, asc=False)
    .limit(limit)
    .select(
        frame_url=Frames.still,
        frame_idx=Frames.pos,
        video_title=Frames.title,
        similarity=sim,
    )
)

CREATE FUNCTION search_frames(query_embedding vector(512), match_count INT)
RETURNS TABLE(frame_url TEXT, frame_idx INT, video_title TEXT, similarity FLOAT) AS $$
  SELECT f.frame_url, f.frame_idx, v.title,
         1 - (f.embedding OPERATOR(public.<=>) query_embedding)
  FROM public.frames f
  JOIN public.videos v ON f.video_id = v.id
  ORDER BY f.embedding OPERATOR(public.<=>) query_embedding
  LIMIT match_count;
$$ LANGUAGE sql STABLE;

class Videos(TableModel, name='videos'):
    ...
    __indexes__ = [pxt.EmbeddingIndex(title, embedding=SEMANTIC)]

# pxt schema update app.py media
# updated   media/videos
# unchanged media/frames, media/chunks, media/conversations

ALTER TABLE videos ADD COLUMN IF NOT EXISTS title_embedding vector(384);
CREATE INDEX IF NOT EXISTS videos_title_embedding_idx ON videos
    USING hnsw (title_embedding vector_cosine_ops);

// then a script, because Postgres cannot call a model:
const { data: rows } = await db.from("videos")
  .select("id,title").is("title_embedding", null);
for (let i = 0; i < rows.length; i += BATCH) {
  /* embed the batch, update each row */
}