Multimodal RAG: Retrieving Across Text, Images, Audio, and Video

Retrieval beyond text

Every architecture so far assumes the knowledge base is text. Multimodal RAG extends retrieval to images, audio, and video, so a query can retrieve, and reason over, content that was never text in the first place, a product photo, a recorded meeting, a diagram in a slide deck.

How cross-modal matching works

The key enabling idea is a shared embedding space: models like CLIP are trained specifically so that an image and a text description of that image land close together in the same vector space, the same closeness principle from Lesson 3, now spanning two different kinds of content instead of one. This lets a text query retrieve a relevant image directly, without needing a human-written caption for every image in the index, and lets an image query retrieve relevant text the same way.

Audio and video are typically handled by a preprocessing step first, transcribing audio to text (making it usable by every text-based technique in this course) and extracting representative frames or generating captions for video, then treating the results as a multimodal collection indexed the same way images are.

Where it still falls short

Cross-modal matching is generally weaker than same-modal matching: text-to-text retrieval is more mature and more precise than text-to-image retrieval, because far less paired training data exists connecting arbitrary images to arbitrary text than exists connecting text to text. Multimodal RAG is the right tool when the knowledge genuinely lives outside text, not a strictly-better replacement for text retrieval on data that is already text.