Author: Jon Handler

In this post, we show how automatic semantic enrichment removes friction and makes the implementation of semantic search for text data seamless, with step-by-step instructions to enhance your search functionality.

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 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.

Today the Linux Foundation announced the OpenSearch Software Foundation. As part of the creation of the OpenSearch Foundation, AWS has transferred ownership of OpenSearch to the Linux Foundation. At the launch of the project in April of 2021, in introducing OpenSearch, we spoke of our desire to “ensure users continue to have a secure, high-quality, fully open source search and analytics suite with a rich roadmap of new and innovative functionality.” We’ve maintained that desire and commitment, and with this transfer, are deepening that commitment, and bringing in the broader community with open governance to help with that goal.

When you use Amazon OpenSearch Service for time-bound data like server logs, service logs, application logs, clickstreams, or event streams, storage cost is one of the primary drivers for the overall cost of your solution. Over the last year, OpenSearch Service has released features that have opened up new possibilities for storing your log data […]

2023 was been a busy year for Amazon OpenSearch Service! Learn more about the releases that OpenSearch Service launched in the first half of 2023. In the second half of 2023, OpenSearch Service added the support of two new OpenSearch versions: 2.9 and 2.11 These two versions introduce new features in the search space, machine […]

Using Amazon OpenSearch Service’s vector database capabilities, you can implement semantic search, Retrieval Augmented Generation (RAG) with LLMs, recommendation engines, and search in rich media. Learn how.

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. In this post, you will learn how you can use Amazon Kinesis Data Firehose to build a log ingestion pipeline to send VPC flow logs to Amazon OpenSearch Serverless. First, you create […]