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 ...

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

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 ...

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

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 ...

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

Redis is introducing a 'unified release' to make products easier for users to adopt and effectively use and manage their data.