Artificial intelligence (AI) processing rests on the use of vectorised data. In other words, AI turns real-world information into data that can be used to gain insight, searched for and manipulated.

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 ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More When a question is presented to an artificial intelligence (AI) algorithm ...

Embedding pipelines are fundamentally a data engineering problem, not an entirely new AI discipline. It’s still ETL (Extract, Load, Transform) at its core, but with embeddings and vector stores as the ...

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 ...

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 ...

PostgreSQL with the pgvector extension allows tables to be used as storage for vectors, each of which is saved as a row. It also allows any number of metadata columns to be added. In an enterprise ...

Vector embeddings are the backbone of modern enterprise AI, powering everything from retrieval-augmented generation (RAG) to semantic search. But a new study from Google DeepMind reveals a fundamental ...