artificial intelligence | Recovery Augmented Generation | Multimodality
Augmented generation multimodal retrieval is an emerging design paradigm that allows ai models to interact with stores of text, images, videos, and more.
To explore this topic, we will first cover what Retrieval Augmented Generation (RAG) is, the idea of multimodality, and how the two combine to create modern multimodal RAG systems. Once we understand the fundamental concepts of multimodal RAG, we will build a multimodal RAG system ourselves using Google Gemini and a CLIP-style model for coding.
Who is this useful for? Anyone interested in modern ai?
How far along is this post? Although multimodal RAG is at the cutting edge of ai, it is intuitively simple and accessible. This article should be interesting for experienced ai researchers, while being simple enough for a beginner.
Prerequisites: None
Before we dive into multimodal RAG, let’s briefly review traditional Recovery Augmented Generation (RAG). Basically, the idea…
