Towards AI

APRIL 23, 2025

The general perception is that you can simply feed data into an embedding model to generate vector embeddings and then transfer these vectors into your vector database to retrieve the desired results. how to perform a vector search Many vector database providers promote their capabilities with descriptors like easy, user-friendly, and simple.

K-nearest Neighbors 58

K-nearest Neighbors Database ETL Machine Learning 58

Flipboard

MAY 5, 2025

In this series, we will set up AWS OpenSearch , which will serve as a vector database for a semantic search application that well develop step by step. Figure 2 : Amazon OpenSearch Service for Vector Search: Demo Key Features of AWS OpenSearch Scalability: Easily scale clusters up or down based on workload demands.

AWS 119

AWS Clustering Deep Learning Deep Learning 119

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.

SQL 132

SQL AWS Analytics Analytics 132

AWS Machine Learning Blog

SEPTEMBER 25, 2024

Testing the Streamlit app in a SageMaker environment is intended for a temporary demo. LuxuryBrand #TimelessElegance #ExclusiveCollection Retrieve and analyze the top three relevant posts The next step involves using the generated image and text to search for the top three similar historical posts from a vector database.

AWS 97

AWS K-nearest Neighbors ML ML 97