I have to say, neither of those answers are very satisfying. Neither is wrong but I don't come away with a clear explanation for why cosine similarity is the obvious metric to use.
For euclidean distance, we have (q-v)^2 = qq + vv - 2qv. The qq and vv don't tell us anything about the relationship between q and v, so are irrelevant to search. So really we are mostly trying to maximize inner product when we search.
Additionally, the scale of q (the query) is constant across any given search, so might as well normalize q; the results won't change.
Then the only question is whether the scale of v matters... It, again, doesn't tell us anything about the relationship between q and v. So, might as well normalize... And then we are just doing cosine similarity.
There is other aspect: if all vectors are normalized then cosine distance is just dot product. It is faster to calculate than euclidean distance.