Hybrid search live coded from scratch

Lightning Lessons

Practical skills & tools to accelerate your career

Hybrid search live coded from scratch

Hosted by Doug Turnbull and Ben Trent

206 students

What you'll learn

Internals of hybrid search

How a vector search index, like Elasticsearch's, implements filtering using algorithms to combine with lexical retrieval

See where hybrid search goes wrong

As you watch an expert live-code, see where the algorithm falls apart as a real-life expert makes a mistake

Deepen your vector search knowledge

Dense vectors provide specific constraints on retrieval solutions - learn what these are, how they can go wrong

Why this topic matters

RAG systems all use vector databases. HNSW (Hierarchical Navigable Small Worlds) is the most common algorithm. If you want to build RAG, you should appreciate how this algorithm works (Missed previous parts? catch up here https://maven.com/p/eca048/build-a-vector-database-from-scratch-part-two)

You'll learn from

Doug Turnbull

Led Search at Shopify, Reddit

Doug has done embedding-based retrieval since using Latent Semantic Indexing to generate search synonyms in 2013. Author of Relevant Search + AI Powered Search, he now helps teams build RAG and search applications. Previous work includes leading search at Reddit, Shopify, and several AI Startups.

Ben Trent

Sr. Principal Engineer Elastic

Husband of one, father of three. Ben has been doing tech related things for well over a decade now. Over the past 6 years his focus has been machine learning, and 4 of those years have been with Elastic working on Elasticsearch, in particular improving Elasticsearch's vector search implementation. His main programming language is currently Java, but his heart lies with Rust, Clojure and... Ruby (yes, he likes Ruby more than Python...).

Previously at