Feature URIs: Evolving Embeddings Without Migration
Summary
Learn how to evolve embedding models without painful re-indexing. Master Feature URIs—a core abstraction for managing the lifecycle of embeddings, extractors, and indexes. Discover why vector indexes are stateful, how to A/B test embedding models safely, and how to roll forward and roll back upgrades without downtime.
About this video
Learn how to evolve embedding models without painful re-indexing. Master Feature URIs—a core abstraction for managing the lifecycle of embeddings, extractors, and indexes. Discover why vector indexes are stateful, how to A/B test embedding models safely, and how to roll forward and roll back upgrades without downtime. What you'll learn: ⚡ Why vector indexes are inherently stateful and fragile ⚡ The 4 components of a Feature URI ⚡ How extractors, embedding models, versions, and inference endpoints are coupled ⚡ A/B testing embedding models without re-indexing ⚡ Rolling forward and rolling back embedding upgrades ⚡ Real examples using image collections and feature search ⚡ How Feature URIs enable hybrid search, re-ranking, and evaluation