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

Flipboard

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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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. How OpenSearch Uses Neural Search and k-NN Indexing Figure 6 illustrates the entire workflow of how OpenSearch processes a neural query and retrieves results using k-Nearest Neighbor (k-NN) search.

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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

It works by analyzing the visual content to find similar images in its database. Store embeddings : Ingest the generated embeddings into an OpenSearch Serverless vector index, which serves as the vector database for the solution. Display results : Display the top K similar results to the user. b64encode(resized_image).decode('utf-8')

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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning Blog

Vector database FloTorch selected Amazon OpenSearch Service as a vector database for its high-performance metrics. Retrieval (and reranking) strategy FloTorch used a retrieval strategy with a k-nearest neighbor (k-NN) of five for retrieved chunks. Each provisioned node was r7g.4xlarge,

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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. Its vector data store seamlessly integrates with operational data storage, eliminating the need for a separate database.

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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

These databases typically use k-nearest (k-NN) indexes built with advanced algorithms such as Hierarchical Navigable Small Worlds (HNSW) and Inverted File (IVF) systems. OpenSearch Service then uses the vectors to find the k-nearest neighbors (KNN) to the vectorized search term and image to retrieve the relevant listings.

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

AWS Machine Learning Blog

We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) 7b) model to generate text responses to user questions based on the most similar slide retrieved from the vector database. Archana is an aspiring member of the AI/ML technical field community at AWS.

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