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I do agree with the article that this feature will be more or less available in all DB types. Vector databases are a gimmick at the moment. Ultimately conversa
by _pdp_ 3y ago
I do agree with the article that this feature will be more or less available in all DB types.
Vector databases are a gimmick at the moment. Ultimately conversational AI agents should be able to extract information from a diverse set of sources with a diverse set of tools. The approach that is currently taken is hit-and-miss at best. How often do you searched something and the first result happens to be the thing you are looking for? Why should it be any different with vector DBs? Obviously the query matters a lot no matter how the information is searched.
- nunodonato 3y agohard disagree. Extracting information is much more costly (fetch data, feed data (which might be huge) into the model). Embeddings work really well to store semantic meaning and are great for searching. Or, at least, a 1st stage of searching to filter out the non-relevant content. I'm working on my own "notes" app, based on embeddings because I 'm tired of never finding what I need due to bad search/tagging/categorizing
- marcinzm 3y ago>How often do you searched something and the first result happens to be the thing you are looking for? Searched where? Every search powered by a large tech company has almost certainly been using vector search for years. Then combined that with other non-vector results. Then run that through numerous ranking models. Then showed that to you. It's far from perfect but absurdly better than the average ElasticSearch results you might get elsewhere.