This lightning session introduces a new idea in vector search - Wormhole vectors!

It has deep roots in physics and allows for transcending spaces of any nature: sparse, vector and behaviour (but could theoretically be any N-dimensional space).

Blog post on Medium: https://dmitry-kan.medium.com/novel-idea-in-vector-search-wormhole-vectors-6093910593b8

Session page on maven: https://maven.com/p/8c7de9/beyond-hybrid-search-with-wormhole-vectors?utm_campaign=NzI2NzIx&utm_medium=ll_share_link&utm_source=instructor

To try the managed OpenSearch (multi-cloud, automatic backups, disaster recovery, vector search and more), go here: https://console.aiven.io/signup?utm_source=youtube&utm_medium&&utm_content=vectorpodcast

Get credits to use Aiven's products (PG, Kafka, Valkey, OpenSearch, ClickHouse): https://aiven.io/startups

Timecodes:

00:00 Intro by Dmitry

01:48 Trey's presentation

03:05 Walk to the AI-Powered Search course by Trey and Doug

07:07 Intro to vector spaces and embeddings

19:03 Disjoint vector spaces and the need of hybrid search

23:11 Different modes of search

24:49 Wormhole vectors

47:49 Q&A

What you'll learn:

- What are "Wormhole Vectors"?

Learn how wormhole vectors work & how to use them to traverse between disparate vector spaces for better hybrid search.

- Building a behavioral vector space from click stream data

Learn to generate behavioral embeddings to be integrated with dense/semantic and sparse/lexical vector queries.

- Traverse lexical, semantic, & behavioral vectors spaces

Jump back and forth between multiple dense and sparse vector spaces in the same query

- Advanced hybrid search techniques (beyond fusion algorithms)

Hybrid search is more than mixing lexical + semantic search. See advanced techniques and where wormhole vectors fit in.

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