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.

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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 ...

In today’s data-driven world, the exponential growth of unstructured data is a phenomenon that demands our attention. The rise of generative AI and large language models (LLMs) has added even more ...

When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing — a ...

As developers look to harness the power of AI in their applications, one of the most exciting advancements is the ability to enrich existing databases with semantic understanding through vector search ...

Oracle Corp. today is rolling out the latest version of its Exadata database-optimized computing platform, claiming 55% better performance on vector searches used in artificial intelligence model ...

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 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 ...

As artificial intelligence reshapes marketing technology, vector search has emerged as a critical capability for 2025 data strategies. For marketing leaders, understanding how this technology impacts ...

The latest trends in software development from the Computer Weekly Application Developer Network. Hazelcast has made sure it keeps its platform progression evolving in line with current major trends ...