Multimodal embeddings map text, images, audio, and video into a shared embedding space, enabling semantic search across all modalities without lossy format conversion. The post explains the intuition behind contrastive learning (CLIP, ImageBind), the 'modality gap' problem, and key design decisions like chunking strategy, vector dimensions, and Matryoshka Representation Learning. Three practical RAG implementations are walked through using Weaviate and Gemini Embedding 2: searching audio recordings without transcripts, indexing PDFs as visual pages to preserve layout and diagrams, and finding specific moments in video by visual content rather than captions. The post also advises when multimodal embeddings are worth the added cost versus sticking with text-only pipelines.
Table of contents
Embeddings, Briefly The Shared Embedding Space How Models Learn to Align Modalities Decisions that Shape Multimodal Retrieval Building Multimodal Systems (3 Examples) When to Use Multimodal Embeddings (And When Not To) Summary Ready to start building? Don't want to miss another blog post?Sort: