Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...

Vector databases and search aren’t new, but vectorization is essential for generative AI and working with LLMs. Here's what you need to know. One of my first projects as a software developer was ...

When designing search systems, the decision to use keyword-based search, vector-based search, or a hybrid approach can significantly impact performance, relevance, and user satisfaction. Each method ...

MongoDB enables millions of developers to securely build AI applications on any infrastructure, from local machines to on-premises data centers NEW YORK, Sept. 17, 2025 /PRNewswire/ -- MongoDB, Inc.

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 ...

What is vector search and how is it transforming the search experience? Edo Liberty, CEO of Pinecone and former head of Amazon's AI lab, explains. We’ve been talking with search industry pros and ...

With KIOXIA AiSAQ (TM) technology now integrated into Milvus, Kioxia and the open-source community are enabling a new class of scalable, cost-efficient vector search solutions designed to meet the ...

Most vector search systems struggle with a basic problem: how to break complex documents into searchable pieces. The typical approach is to split text into fixed size chunks of 200 to 500 tokens, this ...

Vector databases explained through speed vs velocity: why AI needs vectors, not rows and columns, to manage context, ...

A language that's data-intensive naturally should have a way to dig into the data effectively. Here's a look at some of the R functions for searching and sorting through it all. Searching and sorting ...