Amazon OpenSearch Service

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

Tyson Foodservice operates as a critical division within Tyson Foods Inc., using its extensive protein production capabilities to supply a diverse array of foodservice clients across multiple sectors. As one of the largest protein providers in the US, Tyson Foods produces approximately 20% of the nation’s beef, pork, and chicken, which forms the foundation of […]

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

Chat-based assistants powered by Retrieval Augmented Generation (RAG) are transforming customer support, internal help desks, and enterprise search, by delivering fast, accurate answers grounded in your own data. With RAG, you can use a ready-to-deploy foundation model (FM) and enrich it with your own data, making responses relevant and context-aware without the need for fine-tuning

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

Handmade.com is a leading hand-crafts product marketplace, offering unique, seller-contributed items to customers around the world. With over 60,000 products in the catalog and some percentage of listings containing basic descriptions that could be improved for better search and search engine optimization (SEO) performance, the need for automation became evident. Manual processing, consuming on average

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

In recent years, the emergence of large language models (LLMs) has accelerated AI adoption across various industries. However, to further augment LLMs’ capabilities and effectively use up-to-date information and domain-specific knowledge, integration with external data sources is essential. Retrieval Augmented Generation (RAG) has gained attention as an effective approach to address this challenge. RAG is

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Kyruus builds a generative AI provider matching solution on AWS

This post was written with Zach Heath of Kyruus Health. When health plan members need care, they shouldn’t need a dictionary. Yet millions face this exact challenge—describing symptoms in everyday language while healthcare references clinical terminology and complex specialty classifications. This disconnect forces members to become amateur medical translators, attempting to convert phrases like “my

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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

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Optimize RAG in production environments using Amazon SageMaker JumpStart and Amazon OpenSearch Service

Generative AI has revolutionized customer interactions across industries by offering personalized, intuitive experiences powered by unprecedented access to information. This transformation is further enhanced by Retrieval Augmented Generation (RAG), a technique that allows large language models (LLMs) to reference external knowledge sources beyond their training data. RAG has gained popularity for its ability to improve

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Adobe enhances developer productivity using Amazon Bedrock Knowledge Bases

Adobe Inc. excels in providing a comprehensive suite of creative tools that empower artists, designers, and developers across various digital disciplines. Their product landscape is the backbone of countless creative projects worldwide, ranging from web design and photo editing to vector graphics and video production. Adobe’s internal developers use a vast array of wiki pages,

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

As companies and individual users deal with constantly growing amounts of video content, the ability to perform low-effort search to retrieve videos or video segments using natural language becomes increasingly valuable. Semantic video search offers a powerful solution to this problem, so users can search for relevant video content based on textual queries or descriptions.

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Using Amazon OpenSearch ML connector APIs

When ingesting data into Amazon OpenSearch, customers often need to augment data before putting it into their indexes. For instance, you might be ingesting log files with an IP address and want to get a geographic location for the IP address, or you might be ingesting customer comments and want to identify the language they

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