Do we think about vector dbs wrong?

1

Annoy

The focus on the top 10 in vector search is a product of wanting to prove value over keyword search. Keyword search is going to miss some conceptual matches. You can try to work around that with tokenization and complex queries with all variations but it's not easy. Vector search isn't all that new a concept. For example, the annoy library (https://github.com/spotify/annoy), an open source embeddings database.

#Search Engine #Utilities #Databases 35 social mentions

2

txtai

The focus on the top 10 in vector search is a product of wanting to prove value over keyword search. Keyword search is going to miss some conceptual matches. You can try to work around that with tokenization and complex queries with all variations but it's not easy. Vector search isn't all that new a concept. For example, the annoy library (https://github.com/spotify/annoy), an open source embeddings database.

#Search Engine #Databases #Utilities 63 social mentions

3

Elk.zone

And there are tons of third party clients. I think Tusky is the best one I’ve seen for Android, and there’s an interesting web-based one called Elk that’s very nice. You load up https://elk.zone and then use it as a front-end to sign in to your server.

#Twitter Tools #Twitter Automation #Social Media Marketing 15 social mentions

4

Vectara Neural Search

I agree. My experience is that hybrid search does provide better results in many cases, and is honestly not as easy to implement as may seem at first. In general, getting search right can be complicated today and the common thinking of "hey I'm going to put up a vector DB and use that" is simplistic. Disclaimer: I'm with Vectara (https://vectara.com), we provide an end-to-end platform for building GenAI products.

#Utilities #Search Engine #AI 13 social mentions