Since the advent of ChatGPT, Vector Similarity Search has been gaining prominence. At its core, VSS enables developers to make queries and retrieve information over unstructured data such as audio, natural language, images and video.
With advances in deep learning, data scientists build models to transform almost any data into its vector representation. This could be a transaction, a user profile, an image, a sound, a long piece of text (sentence or paragraph), a time series, or a graph. Any of these can be turned into its “feature vector,” also known as “embedding.” These embeddings capture the most essential features of an entity in a way that makes it easier for computers and databases to compare them.. Consequently, if a model generates two similar vectors for two entities, the two original entities are similar in some fundamental way.
Learn how Redis stores, indexes, and queries vector data. It enables developers to store a vector just as easily as any other field in a Redis hash or JSON. Then, Search and Query, uses advanced indexing and search capabilities to perform low-latency search in large vector spaces – typically ranging from tens of thousands to hundreds of millions of vectors distributed across a number of machines – quickly and efficiently
Please note this webinar will be delivered in Arabic and English.
Senior Solution Architect
Mohammad Reda Katby
Principal Technology Advisor ML