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Driving AI Innovation: Making it easy to build Generative AI apps with Redis Enterprise

With so many people – like your CIO – hyped about artificial intelligence,  machine learning, and generative AI products like ChatGPT, you’re likely under a lot of pressure to develop applications with those technologies for your company.

Like, yesterday.

But how do you turn terabytes of unstructured data – ranging from text to images, audio, and video – into useful information for the AI application?

The answer is a vector database. Vectorizing converts unstructured data into numerical representations known as vector embeddings, and Redis stores, indexes, and retrieves them. Vectors make searching for similar images or text or documents – vector similarity search (VSS) – faster and easier. Moreover, an external vector database with domain-specific, proprietary, or more recent data – used with public general purpose large language models – improves AI application responses.

Not to mention, keeping it from making things up.  

In this tech talk, find out how to use Redis’s vector database capabilities for real-time AI apps. We show the elements of building generative AI applications – using Redis technology you already know.

Watch to find out more about it.

  • What’s a vector database
  • Examples
  • Why LLMs need real-time response
  • Redis Enterprise VSS features and differentiators 

Event Speaker

Tyler Hutcherson headshot

Tyler Hutcherson

Senior Applied Machine Learning Engineer


Watch Now!