OpenSearch Project Blog

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...

This blog provides an overview of the OpenSearch User Group program and some observations I shared as part of a keynote presentation at this year’s OpenSearchCon Europe. The OpenSearch Project has grown...

In today’s data-rich world, information isn’t just text anymore. It’s images, videos, audio, and more. To truly understand and retrieve relevant information, search engines need to go beyond keywords and...

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.

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.

Discover how to implement neural sparse models in Sentence Transformers with OpenSearch. Learn to encode text into sparse vectors with just a few lines of code for efficient search.

OpenSearch Data Prepper 2.12 introduces new features to enhance your data ingestion and processing pipeline. Discover the unified OTLP source for streamlined telemetry data ingestion, the Amazon SQS sink for efficient message queuing, and integration with AWS X-Ray for observability.

The buzz at OpenSearchCon India was electric. And for good reason. In a packed keynote session, leaders from AWS and Freshworks unpacked how search is evolving in the age of...

OpenSearch 3.1 brings further innovation into the 3.x line with a host of updates to help you boost indexing performance, improve search results, dig deeper into your observability data, build more powerful agentic AI solutions, and maintain security for your OpenSearch deployments.

OpenSearch Benchmark's new redline testing feature automatically determines cluster throughput limits through dynamic load scaling and real-time monitoring. Eliminate manual tuning and discover your true performance ceiling.

Learn how to simplify hybrid search filtering with common filter support in OpenSearch 3.0.

Learn how to use OpenSearch 3.0's new PPL commands to enhance your log analysis. Explore practical examples and understand the Calcite-powered optimization.

Discover how to implement agentic AI in OpenSearch using MCP server and Amazon Q Developer CLI for powerful, conversational search experiences and natural language analytics.