Hello! This is the PR department of EQUES Inc., a startup from the Matsuo Lab at the University of Tokyo 🐎Based on the ...

Vector embeddings are numerical representations that capture the relationships and meaning of words, phrases and other data types. Through vector embeddings, essential characteristics or features of ...

When you start learning about RAG, you encounter the term "vector database" quite early on.Create embeddings. Search for ...

BERLIN & NEW YORK, July 15, 2025--(BUSINESS WIRE)--Qdrant, the leading provider of high-performance, open-source vector search, today announced the launch of Qdrant Cloud Inference. This fully managed ...

Learn how to use vector databases for AI SEO and enhance your content strategy. Find the closest semantic similarity for your target query with efficient vector embeddings. A vector database is a ...

Have you ever searched for something online, only to feel frustrated when the results didn’t quite match what you had in mind? Maybe you were looking for an image similar to one you had, or trying to ...

In the age of generative AI (genAI), vector databases are becoming increasingly important. They provide a critical capability for storing and retrieving high-dimensional vector representations, ...

A vector database is a type of database technology that's used to store, manage and search vector embeddings, numerical representations of unstructured data that are also referred to simply as vectors ...

Vector similarity search uses machine learning to translate the similarity of text, images, or audio into a vector space, making search faster, more accurate, and more scalable. Suppose you wanted to ...

Timescale is looking to further advance its namesake open-source database platform with new AI capabilities announced today. Timescale was founded in 2017 as a time series database (TSDB) technology ...