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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 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 OpenSearch Serverless is a serverless deployment option for Amazon OpenSearch Service, a fully managed service that makes it simple to perform interactive log analytics, real-time application monitoring, website search, and vector search with its k-nearest neighbor (kNN) plugin.

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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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AWS empowers sales teams using generative AI solution built on Amazon Bedrock

AWS Machine Learning Blog

At AWS, we are transforming our seller and customer journeys by using generative artificial intelligence (AI) across the sales lifecycle. It will be able to answer questions, generate content, and facilitate bidirectional interactions, all while continuously using internal AWS and external data to deliver timely, personalized insights.

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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. A poor initial retrieval can limit the effectiveness of even the most sophisticated re-ranking algorithms.

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

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MongoDB Atlas Vector Search uses a technique called k-nearest neighbors (k-NN) to search for similar vectors. k-NN works by finding the k most similar vectors to a given vector. Specify the AWS Lambda function that will interact with MongoDB Atlas and the LLM to provide responses.

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Talk to your slide deck using multimodal foundation models on Amazon Bedrock – Part 3

AWS Machine Learning Blog

Part 1 uses AWS services including Amazon Bedrock , Amazon SageMaker , and Amazon OpenSearch Serverless. We performed a k-nearest neighbor (k-NN) search to retrieve the most relevant embedding matching the question. You can do this by deleting the stacks using the AWS CloudFormation console.

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