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. 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. Single-modality RAG is a solved pattern: embed query, embed chunks, retrieve, stuff into LLM. Multimodal RAG requires: Multiple retrieval heads (each modality needs embeddings in a compatible space). Fusion of retrieval results across modalities. Generation grounding that cites sources across modalities. Evaluation metrics that cover cross-modal signal. The 2025 surveys all arrive at the same taxonomy. Retrieve documents of modality B given a query of modality A. Three patterns:…
Multimodal RAG and Cross-Modal Retrieval: Vision-native document RAG is one slice. Production multimodal RAG goes wider — retrieving across text, images,…
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
Browse the complete course catalog or open this lesson on GitHub.