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,
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: