[picohen]

Relevance calculations are handled by the vector db but we try to improve such relevance with the use of metadata (you will see how our components have "selectors" so that metadata can flow all the way to the vector database at the vector level and have an influence when results/scores get retrieved at search time)

[eigenvalue]

Got it. I'd encourage you to expose more of that functionality at the level of your application if possible. I think there is a lot of potential in using more than just cosine similarity, especially when there are lots of candidates and you really want to sharpen up the top few recommendations to the best ones. You might find this open-source library I made recently useful for that:

https://github.com/Dicklesworthstone/fast_vector_similarity

I've had good results from starting with cosine similarity (using FAISS) and then "enriching" the top results from that with more sophisticated measures of similarity from my library to get the final ranking.