A data scientist is building a document deduplication system. Each document is represented as a high-dimensional TF-IDF embedding vector, but document lengths vary widely, so longer documents produce vectors with larger magnitudes. The team wants a similarity metric for a k-nearest-neighbors model that focuses on the orientation (topical content) of vectors rather than their magnitude, so that a short and a long document about the same topic are still judged similar. Which distance/similarity measure best fits this requirement?