Multimodal Search
Search systems that can query across different data types — finding images with text, videos with audio descriptions, or documents that contain specific visual elements.
Why It Matters
Multimodal search makes all organizational content searchable regardless of format. Text-only search misses insights buried in presentations, images, and videos.
Example
Searching a corporate knowledge base for 'quarterly revenue chart showing growth trend' and finding the relevant PowerPoint slide containing that specific chart.
Think of it like...
Like a search engine for your entire sensory experience — you can find a song by humming it, a product by showing a photo, or a document by describing a chart you remember.
Related Terms
Semantic Search
Search that understands the meaning and intent behind a query rather than just matching keywords. It uses embeddings to find results that are conceptually related even if they use different words.
Multimodal Embedding
Embeddings that map different data types (text, images, audio) into the same vector space, enabling cross-modal search and comparison.
Vector Database
A specialized database designed to store, index, and search high-dimensional vector embeddings efficiently. It enables fast similarity searches across millions or billions of vectors.
CLIP
Contrastive Language-Image Pre-training — an OpenAI model trained to understand the relationship between images and text. CLIP can match images to text descriptions without being trained on specific image categories.