Building RAG Applications Using Multimodal Data

12 P.M. ET

Your AI Search needs to be diverse, because the internet is filled with heterogeneous content spanning multiple formats and modalities. To build truly effective AI systems, you must move beyond text-only approaches.

Join MongoDB Senior AI Developer Advocate Apoorva Joshi in a webinar exploring advanced strategies for processing multimodal data within RAG pipelines. Learn how to navigate the complex landscape of mixed-modality content to create more comprehensive AI search capabilities.

You’ll also discover how MongoDB is revolutionizing AI applications by integrating state-of-the-art embedding models from Voyage AI.

You’ll learn:

• Multimodal embedding techniques for diverse data formats (text, images, tables, and figures)
• How Voyage AI's multimodal embedding models differ from competitors and drive more accurate retrievals
• Using Vision Language Models (VLMs) as an alternative to traditional chunking approaches
• Comparative evaluation of Voyage multimodal models against other solutions for mixed-modality data
• Advanced strategies for exploring and retrieving from multimodal datasets

Experience a live code walkthrough with real-world examples of multimodal RAG implementation. We'll demonstrate how MongoDB Atlas integrates with Voyage AI's embedding capabilities to handle the full spectrum of content types found in real-world applications.

Don't miss this opportunity to enhance your multimodal AI development skills and see firsthand how MongoDB and Voyage AI are setting new standards for processing diverse data formats.