Real-Time Results Using Vector Search in Redis Enterprise

You can do more with Redis than you thought.

Users expect search functionality in every application and website they encounter. Yet more than 80% of business data is unstructured, stored as text, images, audio, video, or other formats.

Organizations need to reimagine the ways to make every kind of data discoverable – not least of which is because users demand it. Powerful search features will fuel the next generation of applications.

Hence using AI to enable searching unstructured data has become critical.

A vector database stores data in vectors, or mathematical representations of data points. AI and machine learning transforms unstructured data into numeric representations (vectors) that capture meaning and context, benefiting from advances in natural language processing and computer vision.

Vector search is a key feature of a vector database. It finds data points similar to a given query vector in a vector database. Popular vector search uses include chatbots, recommendation systems, document search, image and video search, natural language processing, and anomaly detection.

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