On the second paragraph: That’s exactly how it worked originally. You can create composite embeddings, and then define some hybrid similarity measure to compute both physical proximity, sentiment of writing, and some categorical tags.

Coding it and experimenting with metrics, however, was much harder then. There was no Numba and I had to implement every experiment in C++, without JIT-ing: https://github.com/unum-cloud/usearch/releases/tag/v0.19.0