State-of-the-art text embedding via the Gemini API

Google has introduced a new experimental Gemini Embedding text model, gemini-embedding-exp-03-07, through the Gemini API. This model, trained on the Gemini model itself, offers superior capabilities by capturing semantic meaning and context via numerical representations, surpassing previous models like text-embedding-004. The Gemini Embedding model excels in various domains such as finance, science, and legal, and ranks first on the Massive Text Embedding Benchmark (MTEB) Multilingual leaderboard with a mean task score of 68.32. It supports applications including efficient retrieval, retrieval-augmented generation, clustering, categorization, classification, and text similarity. Notable features include a longer input token limit of 8K tokens, output dimensions of 3K, Matryoshka Representation Learning for scalable storage, and expanded language support for over 100 languages. Although currently experimental with limited capacity, the model promises a stable release in the future, and feedback from users is encouraged to refine its capabilities.

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