[anamexis]

We do something very similar with embeddings in our product. Users import files that they have to match to a dynamically-defined target schema. The embedding matching provides suggested matches to the user that are generally very accurate, so they don't have to go through and manually match up "telephone" to "phone number" etc. It even works across languages.

[magicalhippo]

I've got some similar use-cases. So, do I understand correctly that you take the source keyword and generate an embedding vector of it, then compare it using dot-product similarity or something to the embedded vectors of the target keywords?

[anamexis]

Exactly, although we use cosine similarity.

[magicalhippo]

Perfect. And yeah that's what I meant, so used to just normalizing vectors so dot product = cosine.

[momojo]

How much time dos this save your users? Is this QOL? Or more of a "our product wouldn't work without this feature" kind of thing?

[anamexis]

Quite a bit of time. The product would still work without the feature, but it is a major feature. It bypasses lots of wading through dropdowns (potentially dozens for a single session)