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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning Blog

Prerequisites To implement the proposed solution, make sure that you have the following: An AWS account and a working knowledge of FMs, Amazon Bedrock , Amazon SageMaker , Amazon OpenSearch Service , Amazon S3 , and AWS Identity and Access Management (IAM). Amazon Titan Multimodal Embeddings model access in Amazon Bedrock.

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Build a Search Engine: Setting Up AWS OpenSearch

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Home Table of Contents Build a Search Engine: Setting Up AWS OpenSearch Introduction What Is AWS OpenSearch? What AWS OpenSearch Is Commonly Used For Key Features of AWS OpenSearch How Does AWS OpenSearch Work? Why Use AWS OpenSearch for Semantic Search? Looking for the source code to this post?

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Build a Search Engine: Semantic Search System Using OpenSearch

PyImageSearch

In this tutorial, well explore how OpenSearch performs k-NN (k-Nearest Neighbor) search on embeddings. Beyond Keyword Matching) Traditional keyword-based search works by matching exact words in a query to those present in indexed documents. The search is based on vector distance (e.g.,

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Enhancing Search Relevancy with Cohere Rerank 3.5 and Amazon OpenSearch Service

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Amazon OpenSearch Service Amazon OpenSearch Service is a fully managed service that simplifies the deployment, operation, and scaling of OpenSearch in the AWS Cloud to provide powerful search and analytics capabilities. Lexical search relies on exact keyword matching between the query and documents.

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Build cost-effective RAG applications with Binary Embeddings in Amazon Titan Text Embeddings V2, Amazon OpenSearch Serverless, and Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

Amazon Titan Text Embeddings models generate meaningful semantic representations of documents, paragraphs, and sentences. It supports exact and approximate nearest-neighbor algorithms and multiple storage and matching engines. RAG helps FMs deliver more relevant, accurate, and customized responses.

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Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

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The Retrieval-Augmented Generation (RAG) framework augments prompts with external data from multiple sources, such as document repositories, databases, or APIs, to make foundation models effective for domain-specific tasks. MongoDB Atlas Vector Search uses a technique called k-nearest neighbors (k-NN) to search for similar vectors.

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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning Blog

OpenAI launched GPT-4o in May 2024, and Amazon introduced Amazon Nova models at AWS re:Invent in December 2024. One of the most critical applications for LLMs today is Retrieval Augmented Generation (RAG), which enables AI models to ground responses in enterprise knowledge bases such as PDFs, internal documents, and structured data.