What is Multimodal Search
Multimodal Search - Search across multiple data types like text, images, video, and audio in a single query
Multimodal search is a retrieval paradigm that enables querying and discovering content across different data modalities — text, images, video, audio, and documents — using a unified interface. Unlike traditional search limited to a single data type, multimodal search uses shared embedding spaces to enable cross-modal queries such as finding video clips from a text description or locating similar images from an audio description.
How It Works
Multimodal search relies on embedding models that map different data types into a shared vector space. When data is ingested, each modality is processed by its respective encoder (vision, language, audio) to produce vectors. At query time, the user's input — regardless of its modality — is encoded into the same space, and nearest neighbor search finds the most similar items across all modalities.
Technical Details
Modern multimodal search systems use contrastive learning models like CLIP (for vision-language) or ImageBind (for six modalities) to align representations. The search pipeline typically includes query encoding, approximate nearest neighbor lookup (using HNSW or IVF indices), metadata filtering, and result ranking. Hybrid approaches combine dense vector search with sparse keyword matching for better precision.
Best Practices
- Use models that align the specific modalities your application needs
- Combine vector search with metadata filters for precision
- Implement hybrid search blending keyword and semantic results
- Preprocess data consistently (resolution, encoding, language) before embedding
- Benchmark retrieval quality with modality-specific evaluation metrics