Excellent article, universality of vector search is only starting to become understood. But that is still only part of the story, as vectors also offer other unsurpassed advantages, such as capability to handle (tens of) billions of vectors. Such scale has been the rarefied province of only a few, namely Google/Bing, but that is changing too. One can check our system https://yottaanswers.com to see for themselves how it works.
Google is not what they used to be and systems that go for such world-class scale will become more prevalent, ChatGPT is really all about that even though they themselves do not quite get it yet, as they do not focus on scale of their innate knowledge as opposed to style prompting.
Google could/should have switched to a RAG-like system years ago but they have been hamstrung by enormous legacy costs and issues. Emergence of vectors and Generative AI is the price to be paid for not doing the switch and taking opportunity to be leaders in vector space.
Given X has plenty of existing databases in production, why do you think Grok/X eng. team decided to go with a dedicated vector DB solution? Might it be that all the extension options (pg_vector et al.) were insufficiently capable of operating at scale?
Excellent article, universality of vector search is only starting to become understood. But that is still only part of the story, as vectors also offer other unsurpassed advantages, such as capability to handle (tens of) billions of vectors. Such scale has been the rarefied province of only a few, namely Google/Bing, but that is changing too. One can check our system https://yottaanswers.com to see for themselves how it works.
Google is not what they used to be and systems that go for such world-class scale will become more prevalent, ChatGPT is really all about that even though they themselves do not quite get it yet, as they do not focus on scale of their innate knowledge as opposed to style prompting.
Google could/should have switched to a RAG-like system years ago but they have been hamstrung by enormous legacy costs and issues. Emergence of vectors and Generative AI is the price to be paid for not doing the switch and taking opportunity to be leaders in vector space.
So the Qdrant team claims Grok RAG is powered by Qdrant: https://twitter.com/qdrant_engine/status/1721097971830260030
Given X has plenty of existing databases in production, why do you think Grok/X eng. team decided to go with a dedicated vector DB solution? Might it be that all the extension options (pg_vector et al.) were insufficiently capable of operating at scale?