I've been using Jev for all kinds of things. This morning I had a realization I kind of like:
Use it to make non-black-box embeddings.
Instead of an embedding model spitting out 1,536 numbers that mean nothing, you ask Jev questions about each document. The answers become the vector.
An email in Cora:
"I got charged twice this month, pls fix asap"
[is_customer, urgent, about_billing, needs_reply]
[1.0, 0.9, 1.0, 1.0]
A newsletter:
[0.0, 0.0, 0.0, 0.1]
A friend asking about lunch:
[0.0, 0.1, 0.0, 0.7]
Then it's just old-school cosine similarity search. Search "billing issues from customers" as [1, 0.5, 1, 0.5] and the double charge comes out on top.
Same idea for our articles at Every:
[is_tutorial, about_ai, contrarian, beginner_friendly]
Or support tickets:
[is_bug, angry, churn_risk, enterprise]
Every number has a name, so you can see why something matched. Need a new dimension? Add a question. Want urgent stuff first? Change the query vector.
Trying this in
@CoraComputer now to make search fast.