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
Senior Applied Machine Learning Engineer
Redis
Stop testing, start deploying your AI apps. See how with MIT Technology Review’s latest research.
