clarification of distance metric

Hi,

This is a coolest project, really awesome :-)
Would you care to kindly comment on the distance metric/s implemented and the rationale thereof?

In many machine learning scenarios we pick e.g. cosine similarity on normalized vectors (so that we're working in a multi-dimensional sphere). Is this here very different in that you look at the plain vector distance?

Thanks in advance for your commenting!!

The issue names no file, test, or entry point; begin by locating the nearest-neighbor or distance-metric implementation in the repository. Document which metric is used and its rationale, including how it relates to cosine similarity and normalized vectors. Done means the explanation is recorded in the project documentation or issue discussion.

Written by the indexing model from the issue text.