Vector Databases and the Future of AI-powered Search - Sam Partee

In this talk you will learn about how vector based search works at scale with the open-soure database REDIS. In nine lines of code you can leverage open source NLP models to encode your unstructured data and make it ready for search.
Sam is a Principal Applied AI Engineer at REDIS
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[@JesusNoland]

Great speaking skills by Sam. Vector databases are going to partner with LLMs beautifully.

[@johncult6948]

SingleStore DB has hybrid search capabilities.

[@greendsnow]

Good but, how does it compare against the other vector DBs?

[@ArunPunnath]

Is there a possibility if we can get the presentation deck?

[@aayushsmarten]

Can we search through the documents "which targets specific information"? Suppose, I want to search for the articles which talk about "young males in their 20s". Now, that doesn't necessarily look for that phrase because it is unlikely that it will be the case. Then how would I search like that? Should I pass metadata? How to pass additional information that can be used to filter the documents for the retrival?

[@PauloNascimento49]

Great explanation

[@ShaunRoot]

Come on people, his jokes weren't that bad.

[@helpmathbit3481]

Vector addition is high level term?

[@David-hl1wo]

I can't find the blog

[@ozne_2358]

@5:00, bottom of the slide

[@BeTheFeatureNotTheBug]

1 minus cosine similarity? Why?

[@datasciencetoday7127]

ai datab bases