Beyond Text: Building Multimodal RAG Systems

​Retrieval-Augmented Generation (RAG) doesn’t stop at text. The future is multimodal RAG, where models can reason over documents, images, charts, and more.

​In this hands-on session, we’ll explore:

• ​What Multimodal RAG is and why it matters

• ​How to combine text + images in a retrieval pipeline

• ​Using vision-language embeddings for storing & searching multimodal data

• ​Running live demos with small VLMs (Vision-Language Models) and vector databases

• ​Practical use cases: compliance checks, document Q&A, product search, and research workflows

​🔹 Format: Interactive demo + live coding walkthrough
🔹 Who’s it for: AI engineers, researchers, and product teams building advanced AI systems
🔹 Takeaway: A working notebook + examples of multimodal retrieval powering next-gen AI apps