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-6093910593b8Session page on maven: https://maven.com/p/8c7de9/beyond-hybrid-search-with-wormhole-vectors?utm_campaign=NzI2NzIx&utm_medium=ll_share_link&utm_source=instructorTo 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=vectorpodcastGet credits to use Aiven's products (PG, Kafka, Valkey, OpenSearch, ClickHouse): https://aiven.io/startupsTimecodes:00:00 Intro by Dmitry01:48 Trey's presentation03:05 Walk to the AI-Powered Search course by Trey and Doug07:07 Intro to vector spaces and embeddings19:03 Disjoint vector spaces and the need of hybrid search23:11 Different modes of search24:49 Wormhole vectors47:49 Q&AWhat 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 dataLearn to generate behavioral embeddings to be integrated with dense/semantic and sparse/lexical vector queries.- Traverse lexical, semantic, & behavioral vectors spacesJump 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.YouTube: https://www.youtube.com/watch?v=fvDC7nK-_C0

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