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
