Traditional embedding models convert text into fixed-size vectors. Multimodal embedding models extend this by mapping inputs from different modalities (text, images, audio, or video) into a shared embedding space. This means you can compare a text query against image documents (or vice versa) using the same similarity functions you're already familiar with.

Similarly, traditional reranker (Cross Encoder) models compute relevance scores between pairs of texts. Multimodal rerankers can score pairs where one or both elements are images, combined text-image documents, or other modalities.

For example, you can compare a text query against image documents, find video clips matching a description, or build RAG pipelines that work across modalities.

Multimodal models require some extra dependencies. Install the extras for the modalities you need (see Installation for more details):