Vector databases unlock the insights buried in complex data including documents, videos, images, audio files, workflows, and system-generated alerts. Here’s how.

Vector databases are ideal for applications where the goal is to find data points similar to a given vector. For example, a vector database can find images similar to a given image, or text ...

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 ...

As an example, vector databases were never designed to be transactional, so if you have a business that is analysing and classifying data from the physical world in real time – it could be video ...

Unique among vector databases, KDB.AI Cloud enables developers to bring temporal and semantic context and relevancy to their AI-powered application. Built to handle high-speed, time-series data and ...

Here, the authors adopt strategies from machine learning for the unsupervised classification of single-molecule charge transport data without a prioriassumptions.

SeMI takes unstructured data and creates a semantic search engine so that you can actually ask your data questions and get answers.

Embeddings are vector representations of other data, for example text. Search engines often use vector embeddings, a cosine metric, and a nearest-neighbor algorithm to find text that is relevant ...

The proliferation of large language models and generative AI has created fertile ground for vector database technologies to flourish.