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 ...

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 ...

The emergence of vector databases and vector search for handling massive quantities of complex data have radically transformed the way AI is implemented and managed. As a specialized approach for ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Real time database vendor Rockset is ...

Timescale Inc., the creator of a cloud time-series database that’s based on PostgreSQL, is looking to cater to artificial intelligence developers with the launch of its latest vector capabilities.

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 ...

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 ...

The dramatic advances in generational AI—chatGPT in particular—have motivated almost all technology companies to find an AI story that can break through the heavily AI-oriented technology news feeds.

Ontology-based algorithms provide a rigorous framework for organising and processing complex data by representing domain knowledge as structured concepts and their interrelationships. These methods ...