Benjamin Trent

Here's how to add passage vectors to Lucene, the benefits of doing so and how existing Lucene structures can be used to create an efficient retrieval experience.

Aggregate billions of documents in milliseconds instead of minutes with Elastic. Learn more about how the new random_sampler aggregation gives you statistically robust results at a lower cost.

Discover what are bit vectors, their practical implications and how to use them in Elasticsearch.

Discover the recent enhancements and optimizations that notably improve vector search performance in Elasticsearch & Lucene vector database.

Optimizing scalar quantization for the vector database use case allows us to achieve significantly better performance for the same retrieval quality at high compression ratios.

This blog explains how int4 quantization works in Lucene, how it lines up, and the benefits of using int4 quantization.

Explore how the kNN query in Elasticsearch can be used and how it differs from top-level kNN search, including examples.

Explore how we brought maximum-inner-product into Lucene and the investigations undertaken to ensure its support.

Elasticsearch is introducing a new type of vector that has 8-bit integer dimensions. This is 4x smaller than the current vector with 32-bit float dimensions, which can result in substantial space savings.

Understand what scalar quantization is, how it works and its benefits. This guide also covers the math behind quantization and examples.

Explore how Elastic introduced scalar quantization into Lucene, including automatic byte quantization, quantization per segment & performance insights.

Looking back at Elastic's vector search innovations in Elasticsearch and Lucene