Author(s): Youssef Hosni

Originally published on Towards AI.

This member-only story is on us. Upgrade to access all of Medium.

In the second article of the Building Multimodal RAG Application series, we explore the process of building a multimodal retrieval-augmented generation (RAG) application using multimodal embeddings.

We start by providing an overview of multimodal embeddings, explaining how they bridge different data types, such as text and images, by embedding them into a shared vector space.

Next, we introduce the Bridge Tower model, a state-of-the-art solution for computing these embeddings. The guide then walks through the process of setting up your work environment and computing multimodal embeddings for both text and images.

We will also cover techniques for measuring the similarity between these embedding vectors, which is crucial for cross-modal retrieval tasks. Finally, we demonstrate how to visualize high-dimensional embeddings using UMAP, enabling a deeper understanding of the structure and relationships within the data.

This comprehensive guide will equip you with the tools and knowledge to build a multimodal RAG system, enhancing your ability to work with text-image interactions.

This article is the second in the ongoing series of Building Multimodal RAG Application:

Introduction to Multimodal RAG Applications (Published)Multimodal Embeddings (You are here!)Multimodal RAG Application Architecture (Coming soon!)Processing Videos for Multimodal RAG (Coming soon!)Multimodal Retrieval from… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI