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OpenSearch Vector Engine is now disk-optimized for low cost, accurate vector search

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Overview of vector search and the OpenSearch Vector Engine Vector search is a technique that improves search quality by enabling similarity matching on content that has been encoded by machine learning (ML) models into vectors (numerical encodings). These benchmarks arent designed for evaluating ML models.

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Enhancing Search Relevancy with Cohere Rerank 3.5 and Amazon OpenSearch Service

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It supports advanced features such as result highlighting, flexible pagination, and k-nearest neighbor (k-NN) search for vector and semantic search use cases. Lexical search relies on exact keyword matching between the query and documents. The querys encoding is then compared to pre-computed document embeddings.

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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. He is focused on OpenSearch Serverless and has years of experience in networking, security and AI/ML.

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Build a Search Engine: Semantic Search System Using OpenSearch

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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. It uses vector similarity (e.g.,

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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. Amazon SageMaker enables enterprises to build, train, and deploy machine learning (ML) models.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

Such data often lacks the specialized knowledge contained in internal documents available in modern businesses, which is typically needed to get accurate answers in domains such as pharmaceutical research, financial investigation, and customer support. For example, imagine that you are planning next year’s strategy of an investment company.

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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning Blog

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. Dr. Hemant Joshi has over 20 years of industry experience building products and services with AI/ML technologies.