OpenSearch Project Blog

Learn to deploy OpenSearch MCP server on Bedrock AgentCore Runtime to securely connect AI agents with OpenSearch clusters using CloudFormation or CLI methods.

A look at how OpenSearch has grown into a trusted open-source platform for next-gen analytics, AI, and global collaboration One year ago, the OpenSearch Project joined the Linux Foundation with...

Executive Takeaway To support its vast cloud ecosystem, SAP is advancing its observability strategy with a strong focus on open standards. A key pillar of this strategy is SAP Cloud...

OpenSearch Benchmark 2.0 redefines the benchmarking experience and introduces long-awaited features that offer users the essential tools needed to measure, track, and optimize OpenSearch performance.

Learn how to use OpenSearch Ingestion pipelines to automate ML batch inference for offline ingestion.

Explore key performance optimizations in OpenSearch's security layer, including privilege evaluation and security enhancements that deliver major throughput improvements for clusters of all sizes.

OpenSearch 3.1 introduces the Search Relevance Workbench, a comprehensive toolkit that helps improve and fine-tune your search relevance through experimentation.

Explore distributed tracing with OpenSearch 3.1 and OpenTelemetry, featuring enhanced service maps and trace analytics. Learn how to collect and analyze telemetry data using OpenSearch Dashboards.

Dotan Horovits talks with Carl Meadows and Pallavi Priyadarshini about how the May 2025 launch of OpenSearch 3.0 reflects the project’s evolution under Linux Foundation governance.

Retrieval-Augmented Generation (RAG) boosts large language model performance by grounding responses in real-time data. Learn how OpenSearch enables scalable, accurate, and trusted RAG solutions.

Learn how OpenSearch improves the performance of date histogram aggregations by using the BKD index tree, with support for sub-aggregations and multi-range traversal.

Semantic search improves result relevance by using a machine learning (ML) model to generate dense or sparse vector embeddings from unstructured text. Traditionally, enabling semantic search has required several manual...

An overview of the OpenSearch User Group program and some observations Kris Freedain shared as part of a keynote presentation at OpenSearchCon Europe 2025.

Go beyond keyword search and learn how to build a multimodal search engine in OpenSearch. This post will show you how to implement one in OpenSearch using the ML inference ingest and search request processors.

At KRUU, Europe’s leading photo booth rental provider, smooth logistics are critical to customer satisfaction. The company’s signature product, a rentable, shippable photo booth for weddings and special events, is...

Traditional lexical search, based on term frequency models like blend term frequency models (BM25), is widely used and effective for many search applications. However, lexical search techniques require significant investment...

The Approximation Framework is a performance optimization in OpenSearch that introduces early termination during BKD tree traversal. It enhances the efficiency of numeric range, sort, and match_all queries by collecting only the required number of documents. This framework delivers exact results with significantly reduced query latency.

Explore advanced semantic field features in OpenSearch, including text chunking, support for externally hosted and custom models, cross-cluster usage, and updating model references.

Explore OpenSearch 3.0's semantic highlighting, an AI-powered feature that intelligently identifies relevant passages based on meaning rather than exact keyword matches. Learn how to enhance your search applications with this powerful capability.