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.
