Staging environment

Tuning HNSW parameters for filtered search

Hosted by Radu Gheorghe and Trey Grainger

Thu, Apr 16, 2026

5:00 PM UTC (1 hour)

Virtual (Zoom)

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103 students

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Cheat at Search with Agents
Doug Turnbull
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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

Software Engineer, Vespa.ai

Radu has been in the search space for many years, mainly on Elasticsearch, Solr, OpenSearch, and, more recently, Vespa.ai. Helps users with both the relevance and the operations side of retrieval. Enjoys education in all its forms (training, blog posts, books, conferences...) and got the chance to be involved in all of them.


Trey Grainger

Founder @ Searchkernel; Author, AI-Powered Search

Trey is lead author of the book AI-Powered Search and is the founder of Searchkernel, a software company building the next generation of AI-powered search. He is an advisor to several startups and adjunct professor of computer science at Furman University. He previously served as CTO of Presearch, a decentralized web search engine, and as chief algorithms officer and SVP of engineering at Lucidworks, an search company whose search technology powers hundreds of the world’s leading organizations. Trey in an instructor for the AI-Powered Search course on Maven.

Previously at

Vespa.Ai
Reddit
Shopify.com
Wikipedia

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