Google's new multi-vector retrieval algorithm (MUVERA) improves search speed and performs better on complex queries.

Researchers at EPFL have created a mathematical model that helps explain how breaking language into sequences makes modern AI ...

MariaDB has recently released MariaDB Community Server 11.8 as generally available, its yearly long-term support (LTS) ...

As AI transforms search, understanding vectors is key to staying visible. Here’s how to optimize your content for this new era of SEO.

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.

“Weaviate Embeddings makes it simple to build and manage AI-native applications,” he said. “For those who prefer a custom approach, our open-source database supports any way they want to work.

To date, Pinecone, like many other vector database technologies, has relied on dense vectors. Jones explained that dense text embedding models produce fixed-length vectors that capture semantic ...

Vector embeddings achieve this by assigning each piece of data a position in a multidimensional space. The closer two pieces of data are in this space, the more similar they are considered to be.

These embeddings are then stored in a vector database. For instance, in a contact centre scenario, a customer might first engage with a support representative via chat and later follow up with a call.

Vector databases are at the heart of this, because that’s how data created by AI modelling is stored and from where it is accessed during AI inference.. In this article, we look at vector ...