Vector databases have built-in search capability that quickly delivers optimized and relevant results, especially with complex data sets such as image, video, and audio.

Vector embeddings represent data in multi-dimensional space, with specialized models like CLIP, GloVe, and Wav2Vec generating embeddings for images, text, and audio, respectively. Key applications ...

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 ...

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 ...

Text, images, and videos are all examples of unstructured data. Vector databases capture and store the essence of a particular piece of data that a machine-learning program or LLM can then pull from.

Zilliz Announces Key Contributions to Milvus 2.1, ... Machine learning is producing vast pools of scalar and vector data on a daily basis. ... such as reverse image search and intelligent chatbot; ...

He explained that the new service helps customers analyze existing Neptune graph data or data lakes on top of S3 storage, taking advantage of vector search to find key insights.