OpenSearch Project Blog

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.

A look back at the last 48 months of OpenSearch and a look to what the future holds.

Discover OpenSearch 3.1's system ingest pipeline feature, enabling plugin developers to automate document processing during ingestion. Learn how it simplifies configuration, improves user experience, and enhances data transformation capabilities.

Learn how to use ML inference processors to implement semantic search and summarization in OpenSearch.

OpenSearch 3.1 delivers up to 3.8x faster hybrid query throughput by optimizing score collection, batching, and segment-level execution—resulting in lower latency and more predictable performance under load.