[sroussey]

You can use llama2 to do embedding and summaries and chat.

Turning the docs into questions is something I will test on stuff (just learning and getting a feel).

I am intrigued... what makes a good vector index??

[deckar01]

My heuristic is how much noise is in the closest vectors. Even if the top k matches seem good, if the following noise has practically identical distance scores, it is going to fail a lot in practice. Ideally you could calculate some constant threshold so that everything closer is relevant and everything further is irrelevant.

[sroussey]

Apologies for being naive, but how do you calculate noise?