PyImageSearch

DECEMBER 23, 2024

Or think about a real-time facial recognition system that must match a face in a crowd to a database of thousands. This is where Approximate Nearest Neighbor (ANN) search algorithms come into play. Imagine a database with billions of samples ( ) (e.g., Imagine a database with billions of samples ( ) (e.g.,

K-nearest Neighbors 101

K-nearest Neighbors Algorithm Deep Learning Deep Learning 101

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. b64encode(resized_image).decode('utf-8')

AWS 114

AWS Database K-nearest Neighbors AI 114

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. When combined with Amazon OpenSearch Service , it enables robust Retrieval Augmented Generation (RAG) applications.

Database 83

Database AWS Python ML 83

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. You then display the top similar results.

K-nearest Neighbors 127

K-nearest Neighbors AWS Machine Learning Machine Learning 127

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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AWS ML ML Database 125

AWS Machine Learning Blog

JANUARY 30, 2024

We use OpenSearch Serverless as a vector database for storing embeddings generated by the Titan Multimodal Embeddings model. In the user interaction phase, a question from the user is converted into embeddings and a similarity search is run on the vector database to find a slide that could potentially contain answers to user question.

AWS 126

AWS ML ML K-nearest Neighbors 126

PyImageSearch

JANUARY 27, 2025

Home Table of Contents Approximate Nearest Neighbor with Locality Sensitive Hashing (LSH) What Is Locality Sensitive Hashing (LSH)? Jump Right To The Downloads Section What Is Locality Sensitive Hashing (LSH)? On Line 28 , we sort the distances and select the top k nearest neighbors.

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K-nearest Neighbors Algorithm Data Preparation Database 82