Introduction to Gemini Embedding

On Friday, Google introduced a new experimental “embedding” model, known as Gemini Embedding, to its Gemini developer API. This move is part of the company’s ongoing efforts to improve its text analysis capabilities.

What are Embedding Models?

Embedding models are designed to translate text inputs, such as words and phrases, into numerical representations called embeddings. These embeddings capture the semantic meaning of the text, making them useful in various applications, including document retrieval and classification. By utilizing embeddings, companies can reduce costs and improve latency, making them an essential tool in natural language processing.

Comparison to Other Models

Several companies, including Amazon, Cohere, and OpenAI, offer embedding models through their APIs. Google has previously offered embedding models, but Gemini Embedding is the first to be trained on the Gemini family of AI models, setting it apart from its predecessors.

Features of Gemini Embedding

According to Google, the Gemini Embedding model has inherited the Gemini model’s understanding of language and nuanced context, making it suitable for a wide range of applications. As stated in a blog post, “We’ve trained our model to be remarkably general, delivering exceptional performance across diverse domains, including finance, science, legal, search, and more.”

Performance and Capabilities

Google claims that Gemini Embedding outperforms its previous state-of-the-art embedding model, text-embedding-004, and achieves competitive results on popular embedding benchmarks. Additionally, Gemini Embedding can accept larger chunks of text and code at once and supports twice as many languages (over 100) compared to text-embedding-004.

Availability and Future Development

Currently, Gemini Embedding is in an experimental phase with limited capacity and is subject to change. However, Google is working towards a stable, generally available release in the coming months, as stated in the company’s blog post.