genai-toolbox/docs/en/resources/embeddingModels/gemini.md

title, type, weight, description

title	type	weight	description
Gemini Embedding	docs	1	Use Google's Gemini models to generate high-performance text embeddings for vector databases.

About

Google Gemini provides state-of-the-art embedding models that convert text into high-dimensional vectors.

Authentication

Toolbox uses your Application Default Credentials (ADC) to authorize with the Gemini API client.

Optionally, you can use an API key obtain an API Key from the Google AI Studio.

We recommend using an API key for testing and using application default credentials for production.

Behavior

Automatic Vectorization

When a tool parameter is configured with embeddedBy: <your-gemini-model-name>, the Toolbox intercepts the raw text input from the client and sends it to the Gemini API. The resulting numerical array is then formatted before being passed to your database source.

Dimension Matching

The dimension field must match the expected size of your database column (e.g., a vector(768) column in PostgreSQL). This setting is supported by newer models since 2024 only. You cannot set this value if using the earlier model (models/embedding-001). Check out available Gemini models for more information.

Example

embeddingModels:
  gemini-model:
    kind: gemini
    model: gemini-embedding-001
    apiKey: ${GOOGLE_API_KEY}
    dimension: 768

{{< notice tip >}} Use environment variable replacement with the format ${ENV_NAME} instead of hardcoding your secrets into the configuration file. {{< /notice >}}

Reference

field	type	required	description
kind	string	true	Must be gemini.
model	string	true	The Gemini model ID to use (e.g., gemini-embedding-001).
apiKey	string	false	Your API Key from Google AI Studio.
dimension	integer	false	The number of dimensions in the output vector (e.g., 768).