Elastic (@elastic) “3 patterns for multimodal RAG. Here's how they differ and when each one breaks d” — TopicDigg

3 patterns for multimodal RAG. Here's how they differ and when each one breaks down. Most RAG systems add multimodal support by converting everything to text first. Is your system natively multimodal, or just a conversion pipeline? The architecture choice shapes what you can query and what you lose. Shared vector space - Cross-modal search without format conversion - Requires large multimodal training datasets - Semantic drift is a real risk if training data is narrow Single grounded modality - Works with any existing text search setup - Spatial relationships in images don't survive conversion - Retrieval quality depends on captioning/transcription accuracy Separate retrieval pipelines - Best per-modality retrieval accuracy - Most complex to rank across modalities - Highest compute cost, independent search per modality Pick your pattern, clone the repo, and build it.