3 ms·
I think your statement "adding to existing fulltext functionality" is subtly important: embeddings provide semantic search that complements traditional search a
by mhalle 3y ago
I think your statement "adding to existing fulltext functionality" is subtly important: embeddings provide semantic search that complements traditional search algorithms.
Specifically, many applications are heavily dependent on names or other proper nouns, often without much context. You might refer to your dog by name without explanation, and a particular embedding might not pick that up. Proper names (people, places, street names) may have outsized importance for anchoring personalized or domain-specific search, but modest generic language models won't know about them.
Is there a specific way of dealing with this problem?
- quartz 3y agoJust spitballing here but you could maybe do a 1-deep depth search… dot product on the initial search and also dot product on the highest confidence matched notes, then some filtering. So if you have notes that associate the name to your dog, and you search for “my dog”, you’d still find those related notes? Would require some experimentation but wouldn’t be surprised if that worked decently well out of the gate.