Blog
Building the future of OpenSearch together
As announced today, the OpenSearch Project is now part of the newly formed OpenSearch Software Foundation, a community-driven initiative under the Linux Foundation. This marks a major milestone in the history of the OpenSearch Project, and we couldn't be more excited to share our thoughts on what this means to...
Optimizing inference processors for cost efficiency and performance
Inference processors, such as text_embedding, text_image_embedding, and sparse_encoding, enable the generation of vector embeddings during document ingestion or updates. Today, these processors invoke model inference every time a document is...
Do more with less: Save up to 3x on storage with derived vector source
If you’re working with modern applications, from semantic search to recommendation systems, you’re likely implementing vector search. While you might focus on the accuracy and speed of vector similarity searches,...
Finding a replacement for JSM in OpenSearch 3.0
OpenSearch 3.0.0 introduced many innovative features that provide significant advancements in performance, data management, vector database functionality, and more. In the release announcement, we shared that OpenSearch has replaced the...
Intelligent troubleshooting using OpenSearch 3.0's plan-execute-reflect agent
OpenSearch 3.0 introduces the plan–execute–reflect agent—a powerful new capability that breaks down complex problems, selects and executes tools autonomously, and adapts through reflection. In this post, we’ll show you how...
Real-time query monitoring with live queries in OpenSearch 3.0
OpenSearch Query Insights has become an important tool for understanding search query performance, offering visibility into how queries execute and consume cluster resources. Building on our commitment to helping you...
Recipes to vectors: Building a hybrid search app with OpenSearch
Ever wondered if OpenSearch can double as a vector database? Spoiler: it absolutely can. Whether you’re building a modern search experience, exploring hybrid retrieval, or just curious about embeddings, OpenSearch...
What's new in OpenSearch Query Insights: Advanced grouping, dashboards, and historical analysis
OpenSearch Query Insights gives you essential visibility into how search queries perform, helping you understand how queries run and how they use cluster resources. Since introducing Query Insights, we’ve aimed...
Building effective hybrid search in OpenSearch: Techniques and best practices
OpenSearch provides a wide range of capabilities to support modern search needs, including traditional keyword-based techniques like lexical search using BM25 and more advanced methods like semantic and hybrid search....
Navigating pagination in hybrid queries with the pagination_depth parameter
OpenSearch 2.10 introduced hybrid queries, which have become a popular choice for improving semantic search relevance. By combining full-text lexical search with semantic search, hybrid queries deliver better results than...