Are there any open source implementations out there that can efficiently solve the following.
I'm given a fixed set $S$ of $n$-dimensional vectors of size $N$, where $N$ is of the order of a million.
Given an n-dimensional vector $v$, I want to find the top $K$ vectors $w_1,\ldots,w_k$ from $S$, such that the cosine similarity of $v$ and each $w_i$ is maximized.
Here $K$ should be a parameter that I can choose at query time. I know there are various metric data structures that can be used for queries such as these. There's also a paper from Google from last NIPS, where they do this. For example this paper from Google uses an internal library for it: