Driving AI Innovation: Making It Easy to Build Generative AI Apps with Redis

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