Category: Amazon OpenSearch Service

In this post, we demonstrate how OfferUp transformed its foundational search architecture using Amazon Titan Multimodal Embeddings and OpenSearch Service, significantly increasing user engagement, improving search quality and offering users the ability to search with both text and images. OfferUp selected Amazon Titan Multimodal Embeddings and Amazon OpenSearch Service for their fully managed capabilities, enabling the development of a robust multimodal search solution with high accuracy and a faster time to market for search and recommendation use cases.

This blog post with accompanying code presents a solution to experiment with real-time machine translation using foundation models (FMs) available in Amazon Bedrock. It can help collect more data on the value of LLMs for your content translation use cases.

Organizations are continuously seeking ways to use their proprietary knowledge and domain expertise to gain a competitive edge. With the advent of foundation models (FMs) and their remarkable natural language processing capabilities, a new opportunity has emerged to unlock the value of their data assets. As organizations strive to deliver personalized experiences to customers using […]

In this post, we’ll show how anyone in your company can use Amazon Bedrock IDE to quickly create a generative AI chat agent application that analyzes sales performance data. Through simple conversations, business teams can use the chat agent to extract valuable insights from both structured and unstructured data sources without writing code or managing complex data pipelines.

This post presents a strategy for optimizing LLM-based applications. Given the increasing need for efficient and cost-effective AI solutions, we present a serverless read-through caching blueprint that uses repeated data patterns. With this cache, developers can effectively save and access similar prompts, thereby enhancing their systems’ efficiency and response times.

In this post, we demonstrate how to create an automated email response solution using Amazon Bedrock and its features, including Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails.

Today, we are happy to announce the availability of Binary Embeddings for Amazon Titan Text Embeddings V2 in Amazon Bedrock Knowledge Bases and Amazon OpenSearch Serverless. This post summarizes the benefits of this new binary vector support and gives you information on how you can get started.

This post explores a solution that uses the power of AWS generative AI capabilities like Amazon Bedrock and OpenSearch vector search to perform damage appraisals for insurers, repair shops, and fleet managers.

In this post, you will learn how to extract key objects from image queries using Amazon Rekognition and build a reverse image search engine using Amazon Titan Multimodal Embeddings from Amazon Bedrock in combination with Amazon OpenSearch Serverless Service.

In this post, we will explore building a reusable RAG data pipeline on LangChain—an open source framework for building applications based on LLMs—and integrating it with AWS Glue and Amazon OpenSearch Serverless. The end solution is a reference architecture for scalable RAG indexing and deployment.