Jina Adds Multimodal Search Without Re-Embedding Text

Jina has released jina-embeddings-v5-omni-small, a 1.74-billion-parameter model that maps text, images, video, and audio into one vector space. Items with related meaning land near one another, enabling cross-modal nearest-neighbor search. Its text embeddings are bit-for-bit identical to those from the corresponding v5 Text model when configuration and task settings match, so existing text vectors can remain in place while teams add media queries and documents.

Compatibility depends on using the corresponding v5 Text variant, the same task adapter, the same query or document role, and the same output dimension. Under those conditions, a RAG system built on v5 Text can search its current index with an image, audio clip, or video without re-embedding the stored text corpus.

The model accepts formats including .mp4, .wav, .mp3, .pdf, .jpg, and .png. Matryoshka training allows applications to retain only the leading dimensions of each vector, reducing storage and search costs at the expense of some retrieval quality.

Choose the objective

• retrieval: asymmetric query-to-document search and RAG