Here is the patch that introduces kNN search for cubes with euclidean, taxicab and chebyshev distances.
Thanks for this! I decided to give the patch a try at the bleeding edge with some high-dimensional vectors, specifically the 1.4 million 1000-dimensional Freebase entity vectors from the Google 'word2vec' project:
I believe the curse of dimensionality is affecting you here. I think it is impossible to get an improvement over sequential scan for 1000 dimensional vectors. Read here:
