bedouin-ranger

Here is a scenario that demonstrates my concern: If you are going to search for a phone number, semantically all phone numbers almost have the same meaning.

The embedding space will project them all to the same part of the space. I do not see any modifier in the query will adjust that. For example, searching "631 887 9812" will not be able to be quite different from any number that starts with zip code 631. The results will be quite washed in my expecation.

There is a place for both:

If you are searching for a specific ID/ISBN some random token, keyword search will be always useful and easy to implement.

If the goal of the search is more semantically ambiguous and can not be expressed by a unique phrase, then neural search will be the way to go.

Most of the interesting applications of search will be semantically driven and therefore neural search has a big role to play.

I asked "what is the biggest gap between primes?" and I was delighted by the returned results.

[https://quantasearch.club/#what%20is%20the%20biggest%20gap%2...?]