Improving search efficiency and accuracy with the newest v2 neural sparse models
Neural sparse search is a novel and efficient method for semantic retrieval, introduced in OpenSearch 2.11. Sparse encoding models encode text into (token,... Read more.
Introducing the neural sparse two-phase algorithm
Neural sparse search is a new, efficient method of semantic retrieval introduced in OpenSearch 2.11. Like dense semantic matching, neural sparse search interprets... Read more.
A deep dive into faster semantic sparse retrieval in OpenSearch 2.12
In our last blog post, we introduced neural sparse search, a new efficient method of semantic retrieval made generally available in OpenSearch 2.11. We... Read more.
OpenSearch AI and machine learning: Recapping 2023 and what’s next
In 2023, generative artificial intelligence (generative AI) captured the mainstream imagination and drove widespread AI-related initiatives. Vector database technology... Read more.
Improving document retrieval with sparse semantic encoders
OpenSearch 2.11 introduced neural sparse search—a new efficient method of semantic retrieval. In this blog post, you’ll learn about using sparse encoders for... Read more.
You can now see ML model status in OpenSearch Dashboards
Before OpenSearch 2.6, checking the status of a deployed machine learning (ML) model in OpenSearch could only be done by using the ML Commons Profile API.... Read more.
