Multimodal RAG is becoming essential as AI applications need to retrieve and reason over images, diagrams, and text together. This engineering post from a Chinese developer shares hands-on experience with joint embedding pipelines. The author discusses trade-offs between late fusion and early fusion of image and text embeddings, practical considerations for using CLIP-like models, and indexing strategies for hybrid search. Key insights include the importance of aligning embedding dimensions, handling missing modalities, and optimizing retrieval latency. For teams building document understanding or visual Q&A systems, this provides a concrete reference point. The post is not a full tutorial but a collection of engineering decisions worth evaluating. We recommend treating it as a signal for the broader industry shift toward multimodal retrieval, not as a copy-paste guide.
A practical guide to combining image and text embeddings for multimodal retrieval-augmented generation, covering model selection, fusion strategies, and indexing.