Gemini Embedding AI Model

The Gemini Embedding AI Model is part of Google’s next-generation AI architecture designed to represent natural language text as mathematical vectors. These vectors, called embeddings, help machines understand the semantic meaning of the text in a format that can be processed for a variety of applications like search, classification, recommendation, and clustering.

Gemini’s embedding model supports over 100 languages and offers a flexible embedding size, which can be adjusted based on your needs for performance or accuracy. This makes it highly adaptable for global and multilingual applications.

What is the Gemini API?

The Gemini API is a service provided by Google to access the capabilities of the Gemini AI models. It allows developers to interact with powerful foundation models, including language generation, vision processing, and embedding models. With the Gemini API, you can generate embeddings for text and other content, run multi-modal queries, and integrate AI-powered features into your applications efficiently.

Gemini Embedding AI Model Pricing

Pricing for the Gemini Embedding Model is based on the number of tokens processed per million tokens. Below is a sample cost model for embedding text data:

 

Model	Price (per 1M tokens)
gemini-embedding-001	$0.15 USD

Massive Text Embedding Benchmark (MTEB)

The Massive Text Embedding Benchmark (MTEB) is a comprehensive benchmark suite designed to evaluate the performance of text embedding models across a wide range of real-world tasks. These include classification, clustering, retrieval, re-ranking, and summarization.

MTEB helps developers understand how well a model generalizes across different domains and tasks, making it a valuable tool for comparing embedding models like Gemini against other state-of-the-art approaches. Gemini Embedding Model performs well and achieves top-ranking scores on MTEB leader-board.

Example: Generating Text Embeddings Using Gemini

Here’s a simple example of how you can use the Gemini API to generate text embeddings using the embed_content method in Python:

from google import genai

client = genai.Client()

result = client.models.embed_content(
 model="gemini-embedding-001",
 contents="What is love?"
)

print(result.embeddings)

 

This code snippet sends the sentence “What is love?” to the Gemini embedding model and prints the resulting vector representation. These embeddings can then be used for downstream machine learning tasks or semantic similarity applications.