Category: Amazon OpenSearch Service

Amazon OpenSearch Service also offers a multi-tiered storage solution to its customers in the form of UltraWarm and Cold tiers. In this post, we discuss this new capability and its use cases, and provide a cost-benefit analysis in different scenarios.

As we enter 2025, OpenSearch Service support for OpenSearch 2.17 brings these improvements to the service. In this post, we walk through 2024’s innovations with an eye to how you can adopt new features to lower your cost, reduce your latency, and improve the accuracy of your search results and generated text.

In this post, we will walk you through how Prime Video used Amazon OpenSearch Service and its AI and machine learning (AI/ML) capabilities to build a more intuitive and enhanced sports search experience.

In this post, we demonstrate how to use Amazon OpenSearch Service with purpose-built document ETL tools, Aryn DocParse and Sycamore, to quickly build a RAG application that relies on complex documents. We use over 75 PDF reports from the National Transportation Safety Board (NTSB) about aircraft incidents. You can refer to the following example document from the collection. As you can see, these documents are complex, containing tables, images, section headings, and complicated layouts.

In this post, you’ll learn how to use OpenSearch Service and Amazon Bedrock to build AI-powered search and generative AI applications. You’ll learn about how AI-powered search systems employ foundation models (FMs) to capture and search context and meaning across text, images, audio, and video, delivering more accurate results to users. You’ll learn how generative AI systems use these search results to create original responses to questions, supporting interactive conversations between humans and machines.

OpenSearch Service provides rich capabilities for RAG use cases, as well as vector embedding-powered semantic search. You can use the flexible connector framework and search flow pipelines in OpenSearch to connect to models hosted by DeepSeek, Cohere, and OpenAI, as well as models hosted on Amazon Bedrock and SageMaker. In this post, we build a connection to DeepSeek’s text generation model, supporting a RAG workflow to generate text responses to user queries.

OpenSearch Vector Engine can now run vector search at a third of the cost on OpenSearch 2.17+ domains. You can now configure k-NN (vector) indexes to run on disk mode, optimizing it for memory-constrained environments, and enable low-cost, accurate vector search that responds in low hundreds of milliseconds. Disk mode provides an economical alternative to memory mode when you don’t need near single-digit latency. In this post, you’ll learn about the benefits of this new feature, the underlying mechanics, customer success stories, and getting started.

In this post, we demonstrate how to use the OpenSearch Ingestion’s Lambda processor to generate embeddings for your source data and ingest them to an OpenSearch Serverless vector collection. This solution uses the flexibility of OpenSearch Ingestion pipelines with a Lambda processor to dynamically generate embeddings.

Juicebox is an AI-powered talent sourcing search engine, using advanced natural language models to help recruiters identify the best candidates from a vast dataset of over 800 million profiles. At the core of this functionality is Amazon OpenSearch Service, which provides the backbone for Juicebox’s powerful search infrastructure, enabling a seamless combination of traditional full-text search methods with modern, cutting-edge semantic search capabilities. In this post, we share how Juicebox uses OpenSearch Service for improved search.

This post showcases how to use Spark on AWS Glue to seamlessly ingest data into OpenSearch Service. We cover batch ingestion methods, share practical examples, and discuss best practices to help you build optimized and scalable data pipelines on AWS.