Thanks for the offer! This is a work project, though. I'm working with images. I can't give too many details due to confidentiality, but we're sub-billion images scale.

Usability is determined by trained annotators. If they find an object of interest and want to harvest more training data, they do a reverse image search across the whole training data and tag true matches.

The goal is to harvest training data for ML. If there is a difficult edge case the model is struggling with, the best way to improve model performance is to harvest additional training data for that edge case. You stop when the model performance meets your requirements.

>ScaNN interface features

Nope. Notice that the results have shape (10000, 20) instead of (20,). That is just doing a batched query i.e. "for each of these 10k input vectors, find me 20 neighbors". What I need is a joint query, i.e. "given these 10k positive examples, give me an additional 20 candidate samples".

Not exactly. I have millions of points, most of which are not related to my query vectors. I want to iteratively refine my search: search, mark results as "relevant" or "irrelevant", repeat search with updated query.

Thanks for the link!

That was an interesting read, but I don't think it solves my problem. Their examples don't show joint vector searches: https://github.com/google-research/google-research/blob/master/scann/docs/example.ipynb

What about marking samples as "irrelevant"?

What does it mean for a vector to attend to another vector?

As you probably know, ANN search often returns irrelevant data. How might I iteratively refine the search with human feedback: marking samples as "relevant" or "irrelevant" and repeating the search.

I've done a lit search and haven't found anything, maybe because I am using the wrong keywords.

No, because the embeddings are on a unit hypersphere. But taking the average vector on the surface of the hypersphere might work.