Jina AI develops and distributes compact, high-performance Search Foundation models for multilingual text and image processing.
Jina Embeddings v3 constructs semantic embedding vectors for texts in 89
languages and allows users to select the output embedding size from a maximum
of 1024 dimensions down to 64. These compressed embeddings are nearly equal
to the full embeddings in performance, but they are dramatically smaller and our
experiments show they speed up search applications proportionately.
This model also provides task-specialized embeddings for classification,
clustering, and semantic similarity, while supporting asymmetric retrieval by
letting users encode search queries and targets in different, optimized ways.
Jina CLIP v2 provides multimodal embeddings for texts and images, enabling
cross-modal applications like image search, but also brings image support to
typical text AI applications like semantic similarity, classification, and clustering.
By producing the same embedding vectors for texts and images, it acts as a drop-
in for any vector-based application framework supporting text.
Jina Reranker v2 provides high-quality reanalysis of semantic matches, especially
in text-based information retrieval, compensating for the limitations of semantic
embedding vectors by analyzing the semantics of texts more closely. Rerankers
are typically used in retrieval applications to enhance precision.
Together with Elasticsearch, Jina AI's search foundation models create powerful
search applications - encode your text and images as compact embeddings, store
them efficiently in Elasticsearch, and enhance result relevance through reranking,
enabling high-performance multilingual and multimodal search at scale.
Getting Started
Using Jina AI’s web API, you have one million free tokens to try the models
out. The pages below have instructions for accessing and using Jina AI models.