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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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Use language embeddings for zero-shot classification and semantic search with Amazon Bedrock

AWS Machine Learning Blog

Amazon Bedrock offers a serverless experience, so you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using Amazon Web Services (AWS) services without having to manage infrastructure. AWS Lambda The API is a Fastify application written in TypeScript.

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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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Enhancing Search Relevancy with Cohere Rerank 3.5 and Amazon OpenSearch Service

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improves search results for best matching 25 (BM25), a keyword-based algorithm that performs lexical search, in addition to semantic search. It supports advanced features such as result highlighting, flexible pagination, and k-nearest neighbor (k-NN) search for vector and semantic search use cases.

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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. The goal is to index these five webpages dynamically using a common embedding algorithm and then use a retrieval (and reranking) strategy to retrieve chunks of data from the indexed knowledge base to infer the final answer.

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OfferUp improved local results by 54% and relevance recall by 27% with multimodal search on Amazon Bedrock and Amazon OpenSearch Service

AWS Machine Learning Blog

Previously, OfferUps search engine was built with Elasticsearch (v7.10) on Amazon Elastic Compute Cloud (Amazon EC2), using a keyword search algorithm to find relevant listings. The listing indexer AWS Lambda function continuously polls the queue and processes incoming listing updates.

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Semantic image search for articles using Amazon Rekognition, Amazon SageMaker foundation models, and Amazon OpenSearch Service

AWS Machine Learning Blog

It also relies on the images in the repository being tagged correctly, which can also be automated (for a customer success story, refer to Aller Media Finds Success with KeyCore and AWS ). Using the k-nearest neighbors (k-NN) algorithm, you define how many images to return in your results.