Building AI Solutions with Multimodal Retrieval-Augmented Generation (RAG): Unifying Text, Image, and Video Search

AI, ML and Data Science Intermediate

Discover how to implement multimodal Retrieval-Augmented Generation (RAG) using Amazon Bedrock to unify text, image, and video search. Learn practical AI solutions that solve real-world challenges like document analysis and image/video content retrieval.

Suman Debnath

Principal Machine Learning Advocate at Amazon Web Services

This session will guide developers through the practical application of multimodal Retrieval-Augmented Generation (RAG) using Amazon Bedrock and OpenSearch. Attendees will learn how to integrate different data modalities—such as text, images, and video—into AI-driven systems to build more advanced search and retrieval solutions.

Key takeaways include:

Introduction to Multimodal RAG: Understand the importance of combining different data types for AI-driven applications.
Implementing Multimodal Search: Learn how to build search systems that unify text, images, and video using Amazon Bedrock and OpenSearch.
Practical Use Cases: Apply RAG techniques to solve real-world challenges, including document analysis, video search, and image captioning.
Hands-on Examples: Step-by-step guidance on building multimodal AI solutions and aligning embeddings across modalities.
Advanced Techniques: Explore the latest AI tools and techniques for optimizing multimodal search and retrieval in production environments.

By the end of the session, developers will have the skills to implement cutting-edge AI applications, improve productivity, and create innovative solutions for complex AI challenges.