Hey @Kishore Nallan, @Krish asked 2 months ago about the vector search, wether the results can be filtered with "filter_by=vector_distance:>0.25". You answered then "Nope that's not possible but these distances don't carry any semantic absolute meaning. They are only useful as relative values." But just for my udneerstanding: The vector distance is the cosine similarity between the requested embedding and the on in typesense right? Because if I want to store face emebddings and the cosine similarity indicates how similar they are, the distance is very meaningful. Are there plans to add filter_by functionality for vector distance or is the "go-to-way" for the next years to postprocess the results?

Cosine similarity is a range between -1 to 1.

cosine_distance = 1 - cosine_similarity

When 2 vectors are exactly same, the cosine similarity be 1, so the cosine distance will be 0. Likewise, when 2 vectors are very different then the cosine similarity will be -1 so the cosine distance will be 2. We plan to add a way to restrict results based on a threshold. When I meant "don't carry any semantic absolute meaning" I meant generically across datasets. It's still useful to have a cutoff threshold for some datasets so we will be adding an option for that.