Free Lesson
Building topk-embed-v1: frontier multi-vector embeddings
60 min
Oct 22, 2026 11:00 AM
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What you'll learn
Why multi-vector retrieval improves quality
See what multi-vector embeddings capture that single-vector representations miss.
How to make multi-vector embeddings cheaper
Learn how compression can preserve retrieval quality while cutting storage costs dramatically.
Why model and engine design belong together
See how co-designing the embedding model and retrieval system changes the quality-cost tradeoff.
How topk-embed-v1 was built
Walk through the design choices behind a scalable multi-vector retrieval system.
Why this topic matters
Multi-vector embeddings can improve retrieval quality, but their storage and compute costs often make them impractical at scale. This talk shows how topk-embed-v1 tackles that problem by co-designing the embedding model and retrieval engine, producing highly compressible representations that retain strong retrieval quality with a footprint closer to dense embeddings.
