Building RAG Applications Using Multimodal Data

AI search must evolve beyond text-only approaches to handle the diverse, multimodal content found across the internet. To build truly effective retrieval-augmented generation (RAG) applications, you need strategies that process text, images, tables, and figures seamlessly.

In this on-demand session, MongoDB Senior AI Developer Advocate Apoorva Joshi explores advanced techniques for integrating multimodal data into your RAG pipelines. Watch the session to 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

Discover how MongoDB and Voyage AI empower AI applications to retrieve and process information across multiple modalities. Watch now to take your multimodal AI search to the next level!