Phase 4: Multimodal Deployment · ~180 minutes · Python
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.
What you will learn
- 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.
Introduction
- Type: Build
- Languages: Python (stdlib, cross-modal retriever with fusion + grounded generator)
- Prerequisites: ColPali and Vision-Native Document RAG, the ML & AI Engineering course
- Time: ~180 minutes
Objective
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.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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