Multimodal RAG and Cross-Modal Retrieval

Vision-native document RAG is one slice. Production multimodal RAG goes wider — retrieving across text, images, audio, and video for workflows like trip planning ("find me a quiet vegan brunch with natural light"), medical triage ("what injury matches this photo + these notes"), e-commerce ("outfits similar to this selfie, in my size"), and field service ("diagnose this engine sound plus photo of the part"). Three 2025 surveys — Abootorabi et al., Mei et al., Zhao et al. — codified the sub-problems: cross-modal retrieval, retrieval fusion, generation grounding, multimodal evaluation. This lesson reads the surveys and designs a production pipeline.

Build 3 hr Python (stdlib, cross-modal retriever with fusion + grounded generator)

Prerequisites: Phase 12 · 23 (ColPali), Phase 11 (RAG basics)

#Learning Objectives

Design cross-modal retrieval: text → image, image → text, audio → video, etc.
Compare three fusion strategies: score fusion, attention-based fusion, MoE fusion.
Explain generation grounding: what "cite your sources" looks like when sources are a mix of modalities.
Name the three canonical multimodal RAG surveys of 2025 and their sub-problem taxonomy.

#Learning Objectives

Keep going. The rest of this lesson is one step away.