Ouch! That’s fat! Which model is that?

We have built a few video-search system by now, using USearch and UForm for embedding. They are only 256 dims and you can concatenate a few from different parts of the video. Any chance it would help?

https://github.com/unum-cloud/uform

Damn! 2M dimension dense vector is huge. Maybe you need to do some dimensionality reduction before attempting ANN. Something like PCA should help.

Have you considered training a shallow MLP autoencoder, perhaps with tied weights between the encoder and the decoder to reduce the dimensionality of the embeddings? Another (IMO, better) approach I can think of off the top of my head, would be to use a semi-supervised contrastive learning approach, with labelled similar and dissimilar video pairs, like in this notebook[1] from OpenAI.

[1]: https://github.com/openai/openai-cookbook/blob/main/examples...