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