Artificial Intelligence

Category: Amazon OpenSearch Service

Tyson Foods elevates customer search experience with an AI-powered conversational assistant

In this post, we explore how Tyson Foods collaborated with the AWS Generative AI Innovation Center to revolutionize their customer interaction through an intuitive AI assistant integrated into their website. The AI assistant was built using Amazon Bedrock,

Building a RAG chat-based assistant on Amazon EKS Auto Mode and NVIDIA NIMs

In this post, we demonstrate the implementation of a practical RAG chat-based assistant using a comprehensive stack of modern technologies. The solution uses NVIDIA NIMs for both LLM inference and text embedding services, with the NIM Operator handling their deployment and management. The architecture incorporates Amazon OpenSearch Serverless to store and query high-dimensional vector embeddings for similarity search.

How Handmade.com modernizes product image and description handling with Amazon Bedrock and Amazon OpenSearch Service

In this post, we explore how Handmade.com, a leading hand-crafts marketplace, modernized their product description handling by implementing an AI-driven pipeline using Amazon Bedrock and Amazon OpenSearch Service. The solution combines Anthropic’s Claude 3.7 Sonnet LLM for generating descriptions, Amazon Titan Text Embeddings V2 for vector embedding, and semantic search capabilities to automate and enhance product descriptions across their catalog of over 60,000 items.

Multi-tenant RAG implementation with Amazon Bedrock and Amazon OpenSearch Service for SaaS using JWT

In this post, we introduce a solution that uses OpenSearch Service as a vector data store in multi-tenant RAG, achieving data isolation and routing using JWT and FGAC. This solution uses a combination of JWT and FGAC to implement strict tenant data access isolation and routing, necessitating the use of OpenSearch Service.

Kyruus Guide Architecture

Kyruus builds a generative AI provider matching solution on AWS

In this post, we demonstrate how Kyruus Health uses AWS services to build Guide. We show how Amazon Bedrock, a fully managed service that provides access to foundation models (FMs) from leading AI companies and Amazon through a single API, and Amazon OpenSearch Service, a managed search and analytics service, work together to understand everyday language about health concerns and connect members with the right providers.

Amazon Bedrock Knowledge Bases now supports Amazon OpenSearch Service Managed Cluster as vector store

Amazon Bedrock Knowledge Bases has extended its vector store options by enabling support for Amazon OpenSearch Service managed clusters, further strengthening its capabilities as a fully managed Retrieval Augmented Generation (RAG) solution. This enhancement builds on the core functionality of Amazon Bedrock Knowledge Bases , which is designed to seamlessly connect foundation models (FMs) with internal data sources. This post provides a comprehensive, step-by-step guide on integrating an Amazon Bedrock knowledge base with an OpenSearch Service managed cluster as its vector store.

Solution architecture diagram

Adobe enhances developer productivity using Amazon Bedrock Knowledge Bases

Adobe partnered with the AWS Generative AI Innovation Center, using Amazon Bedrock Knowledge Bases and the Vector Engine for Amazon OpenSearch Serverless. This solution dramatically improved their developer support system, resulting in a 20% increase in retrieval accuracy. In this post, we discuss the details of this solution and how Adobe enhances their developer productivity.

Solution workflow

Implement semantic video search using open source large vision models on Amazon SageMaker and Amazon OpenSearch Serverless

In this post, we demonstrate how to use large vision models (LVMs) for semantic video search using natural language and image queries. We introduce some use case-specific methods, such as temporal frame smoothing and clustering, to enhance the video search performance. Furthermore, we demonstrate the end-to-end functionality of this approach by using both asynchronous and real-time hosting options on Amazon SageMaker AI to perform video, image, and text processing using publicly available LVMs on the Hugging Face Model Hub. Finally, we use Amazon OpenSearch Serverless with its vector engine for low-latency semantic video search.