AWS, Database and K-nearest Neighbors

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

AWS Machine Learning Blog

NOVEMBER 13, 2024

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.

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Build a Search Engine: Setting Up AWS OpenSearch

Flipboard

MAY 5, 2025

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

MAY 19, 2025

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

AWS Machine Learning Blog

FEBRUARY 13, 2025

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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AWS K-nearest Neighbors Clustering Algorithm

Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning Blog

MARCH 11, 2025

OpenAI launched GPT-4o in May 2024, and Amazon introduced Amazon Nova models at AWS re:Invent in December 2024. Vector database FloTorch selected Amazon OpenSearch Service as a vector database for its high-performance metrics. Interested users are invited to try out FloTorch from AWS Marketplace or from GitHub.

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K-nearest Neighbors AWS Database AI

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

Flipboard

NOVEMBER 17, 2023

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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K-nearest Neighbors AWS Clustering Database

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

AWS Machine Learning Blog

AUGUST 26, 2024

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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AWS AI AI K-nearest Neighbors

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2

AWS Machine Learning Blog

NOVEMBER 15, 2024

The available data sources are: Stock Prices Database Contains historical stock price data for publicly traded companies. Analyst Notes Database Knowledge base containing reports from Analysts on their interpretation and analyis of economic events. Stock Prices Database The question is about a stock price.

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

AWS Machine Learning Blog

DECEMBER 10, 2024

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. You can do this by deleting the stacks using the AWS CloudFormation console.

AWS

AWS K-nearest Neighbors Database ML

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

FEBRUARY 5, 2025

The listing indexer AWS Lambda function continuously polls the queue and processes incoming listing updates. 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.

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Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch

PyImageSearch

MAY 12, 2025

Home Table of Contents Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch Introduction What Will We Do in This Blog? However, we will also provide AWS OpenSearch instructions so you can apply the same setup in the cloud. This is useful for running OpenSearch locally for testing before deploying it on AWS.

AWS

AWS K-nearest Neighbors Deep Learning Deep Learning

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

AWS Machine Learning Blog

SEPTEMBER 8, 2023

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 ). In this post, we demonstrate how to use Amazon Rekognition , Amazon SageMaker JumpStart , and Amazon OpenSearch Service to solve this business problem.

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Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 2

AWS Machine Learning Blog

APRIL 19, 2024

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. OpenSearch Serverless is an on-demand serverless configuration for Amazon OpenSearch Service.

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Use DeepSeek with Amazon OpenSearch Service vector database and Amazon SageMaker

Flipboard

FEBRUARY 7, 2025

This post shows you how to set up RAG using DeepSeek-R1 on Amazon SageMaker with an OpenSearch Service vector database as the knowledge base. You will execute scripts to create an AWS Identity and Access Management (IAM) role for invoking SageMaker, and a role for your user to create a connector to SageMaker.

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Build a contextual text and image search engine for product recommendations using Amazon Bedrock and Amazon OpenSearch Serverless

AWS Machine Learning Blog

APRIL 3, 2024

We detail the steps to use an Amazon Titan Multimodal Embeddings model to encode images and text into embeddings, ingest embeddings into an OpenSearch Service index, and query the index using the OpenSearch Service k-nearest neighbors (k-NN) functionality. Virginia) and US West (Oregon) AWS Regions.

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Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 1

AWS Machine Learning Blog

JANUARY 30, 2024

and AWS services including Amazon Bedrock and Amazon SageMaker to perform similar generative tasks on multimodal data. We use OpenSearch Serverless as a vector database for storing embeddings generated by the Titan Multimodal Embeddings model. Setting k=1 retrieves the most relevant slide to the user question.

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AWS ML ML K-nearest Neighbors

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 1

AWS Machine Learning Blog

OCTOBER 24, 2024

The AWS Generative AI Innovation Center (GenAIIC) is a team of AWS science and strategy experts who have deep knowledge of generative AI. They help AWS customers jumpstart their generative AI journey by building proofs of concept that use generative AI to bring business value. doc,pdf, or.txt).

AWS

AWS K-nearest Neighbors Database AI

Implement serverless semantic search of image and live video with Amazon Titan Multimodal Embeddings

AWS Machine Learning Blog

JUNE 3, 2024

Kinesis Video Streams makes it straightforward to securely stream video from connected devices to AWS for analytics, machine learning (ML), playback, and other processing. This solution was created with AWS Amplify. It enables real-time video ingestion, storage, encoding, and streaming across devices.

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Power recommendations and search using an IMDb knowledge graph – Part 3

AWS Machine Learning Blog

JANUARY 6, 2023

Many AWS media and entertainment customers license IMDb data through AWS Data Exchange to improve content discovery and increase customer engagement and retention. We downloaded the data from AWS Data Exchange and processed it in AWS Glue to generate KG files. Background. Solution overview. Launch solution resources.

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

AWS Machine Learning Blog

DECEMBER 6, 2023

Another driver behind RAG’s popularity is its ease of implementation and the existence of mature vector search solutions, such as those offered by Amazon Kendra (see Amazon Kendra launches Retrieval API ) and Amazon OpenSearch Service (see k-Nearest Neighbor (k-NN) search in Amazon OpenSearch Service ), among others.

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Implement semantic video search using open source large vision models on Amazon SageMaker and Amazon OpenSearch Serverless

Flipboard

JUNE 6, 2025

The proposed solution architecture provides efficient semantic video search by using open source LVMs and AWS services. We use OpenSearch Serverless as a vector database for storing embeddings generated by the LVM. OpenSearch Serverless is an on-demand serverless version for Amazon OpenSearch Service.

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AWS Clustering K-nearest Neighbors ML

How IDIADA optimized its intelligent chatbot with Amazon Bedrock

AWS Machine Learning Blog

FEBRUARY 25, 2025

The integration with Amazon Bedrock is achieved through the Boto3 Python module, which serves as an interface to the AWS, enabling seamless interaction with Amazon Bedrock and the deployment of the classification model. Take the first step in your generative AI transformationconnect with an AWS expert today to begin your journey.

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Build a multimodal social media content generator using Amazon Bedrock

AWS Machine Learning Blog

SEPTEMBER 25, 2024

The whole process is shown in the following image: Implementation steps This solution has been tested in AWS Region us-east-1. To set up a JupyterLab space Sign in to your AWS account and open the AWS Management Console. However, it can also work in other Regions where the following services are available.

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Understanding and Building Machine Learning Models

Pickl AI

NOVEMBER 18, 2024

Structured data refers to neatly organised data that fits into tables, such as spreadsheets or databases, where each column represents a feature and each row represents an instance. This data can come from databases, APIs, or public datasets. K-Nearest Neighbors), while others can handle large datasets efficiently (e.g.,

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

Build a Search Engine: Setting Up AWS OpenSearch

Webinars

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Build a Search Engine: Semantic Search System Using OpenSearch

Webinars

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

Benchmarking Amazon Nova and GPT-4o models with FloTorch

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

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

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2

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

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

Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch

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

Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 2

Use DeepSeek with Amazon OpenSearch Service vector database and Amazon SageMaker

Build a contextual text and image search engine for product recommendations using Amazon Bedrock and Amazon OpenSearch Serverless

Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 1

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 1

Implement serverless semantic search of image and live video with Amazon Titan Multimodal Embeddings

Power recommendations and search using an IMDb knowledge graph – Part 3

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

Implement semantic video search using open source large vision models on Amazon SageMaker and Amazon OpenSearch Serverless

How IDIADA optimized its intelligent chatbot with Amazon Bedrock

Build a multimodal social media content generator using Amazon Bedrock

Understanding and Building Machine Learning Models

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