Amazing lol: for what it's worth, it's hugely, hugely, shocking to me how little anyone outside sentence-transformers bothers noting whether their embedding model is for asymmetric vs. symmetric search. i.e. in all seriousness, afaik, 90% of people are using the "wrong" embeddings, and either handwave about it, don't care, or think it's being picky.

So of course I want to reach to blame that here.

I'm doing a cross-platform app pipeline that basically search API => get pages => embed => run against prompt, so unfortunately I don't have much more insight.

MiniLM V3 is good enough, the nearest constraint at this point is more "will I download all 10 search results in time?" than "am I grabbing the absolutely best passages?" -- good on you for legitimately testing those things.

edit: actually, I do have one more thought: sometimes I end up with embeddings for the last part of a web page that match ~anything. i.e. imagine i have a 1005 word web page and split it into 250 word chunks to embed. Then I'll have 4 "full" vectors, and one that represents just 5 words. That 5 word tends to match almost ~anything, i.e. I'll see ones from previous search queries match the current search query. Maybe you're seeing noise because its just 3-5 words? But then again, you were probably just illustrating without splatting in huge chunks of texts.

That’s a good point! Actually I’ve been using these in the asymmetric search setting, and the queries are very short phrases (not full questions like you might see in a RAG application).