Hi @FL33TW00D in my use case (emails), I was able to get good results with cosine similarity and 5 dimensions for the embedding space.
Although not strictly a metric, cosine similarity is nice because it doesn’t care about the size of the documents - if you need a proper metric then you could try using the L2 normalised Euclidean distance (Cosine similarity - Wikipedia). I wish I could say that I got the dim=5 value through some deep intuition of topology, but it was mostly a form of trial and error 
The other UMAP parameters were left on their default values, which incidentally is similar to those used in the top2vec paper: https://arxiv.org/pdf/2008.09470.pdf 1
I’m not aware of a principled way for deciding the optimal embedding dimension - perhaps you can try a simple gridsearch to see which one works best?