This article chronicles an individual's journey from being a regular OpenSearch user at Cloudera to becoming a maintainer of the project's search relevance plugin. By starting with…
At OpenSearchCon Europe 2026, Laysa Uchoa argued that generative AI hallucinations are primarily a retrieval problem rather than an inherent limitation of Large Language Models. Sh…
OpenSearch 3.5 introduces "LLM as a Judge," a new feature that leverages large language models to automatically and at scale evaluate search result relevance. This approach provide…
The OpenSearch agent server is an experimental multi-agent orchestration platform that enables users to build and deploy specialized AI agents for specific tasks within OpenSearch.…
OpenSearch 3.7 introduces significant enhancements for observability and AI-powered search, focusing on unifying telemetry and accelerating performance. Key updates include native …
At OpenSearchCon Europe 2026, Carl Meadows announced three key innovations—OpenSearch Launchpad, the Relevance Agent, and a unified Observability Stack—to position OpenSearch as a …
Aiven engineers identified a bug where a single space in a KNN field name caused OpenSearch snapshot backups to fail because the snapshot repository rejected the resulting filename…
OpenSearch 3.5 introduces support for asymmetric embedding models, which enhance semantic search relevance by applying distinct encoding strategies to short queries and long, infor…
OpenSearch 3.6 introduces a significant shift from traditional search to supporting autonomous, agentic AI architectures through new features like pre-packaged Agent Skills and nat…
The OpenSearch team has developed four AI agents—Atlas, Ralph, Nitro, and Sentinel—to automate repetitive tasks throughout the software development lifecycle, ranging from knowledg…
OpenSearch agentic memory addresses the lack of conversational context in AI contact center agents by providing persistent, personalized memory across multiple interactions. By uti…
The OpenSearch Relevance Agent is an automated, multi-agent system designed to replace slow, manual search tuning with data-driven precision. By analyzing user behavior and automat…