GPU-accelerated vector search in OpenSearch: A new frontier
OpenSearch’s adoption as a vector database has grown significantly with the rise of generative AI applications. Vector search workloads are scaling from millions... Read more.
Deep dive: Enabling pluggable storage in the OpenSearch vector engine
In 2019, OpenSearch introduced the Vector Engine, which supports three native engines: Non-Metric Space Library (NMSLIB), Facebook AI Similarity Search (Faiss),... Read more.
Introducing byte vector support for Faiss in the OpenSearch vector engine
The growing popularity of generative AI and large language models (LLMs) has led to an increased demand for efficient vector search and similarity operations. These... Read more.
Boosting vector search performance with concurrent segment search
In OpenSearch, data is stored in shards, which are further divided into segments. When you execute a search query, it runs sequentially across all segments of each... Read more.
Efficient filtering in OpenSearch vector engine
With the release of OpenSearch 2.9, we introduced efficient filtering, or “filter-while-search,” functionality for queries using the Facebook AI Similarity... Read more.
Improve search relevance with hybrid search, generally available in OpenSearch 2.10
In an earlier blog post, a group of Amazon scientists and engineers described methods of combining keyword-based search with dense vector search in order to... Read more.
Getting started with multilayer maps in OpenSearch
OpenSearch 2.5 introduced new multilayer maps for visualizing geographical data. Displaying multiple layers on a map lets you show data from different sources... Read more.
