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Free Lesson

Tuning HNSW parameters for filtered search

60 min
Apr 16, 2026 1:00 PM
Virtual (Zoom)

In this video

What you'll learn

Why filters make HNSW vector search slow

We'll start with a short intro on how HNSW works and why filters are expensive and hurt recall.

Parameters to improve performance/recall

Pros and cons of post filters, ACORN-1, brute force kNN, overfetching, and adaptive beam search.

Tools to tune vector search filter search

How HNSWTuner+VespaNNParameterOptimizer allows you to change these knobs and see the impact of latency and recall

Why this topic matters

Most vector search involves filtering, which has a big impact on both performance and quality compared to unfiltered Approximate Nearest Neighbor search on an HNSW. We'll discuss ways to limit this impact, keeping queries nice and fast.

You'll learn from

Radu Gheorghe

Radu Gheorghe

Software Engineer, Vespa.ai

Trey Grainger

Trey Grainger

Founder @ Searchkernel; Author, AI-Powered Search

Previously at

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