Gemini Embedding

About

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

Authentication

Toolbox supports two authentication modes:

1. Google AI (API Key): Used if you provide apiKey (or set GOOGLE_API_KEY/GEMINI_API_KEY environment variables). This uses the Google AI Studio backend.
2. Vertex AI (ADC): Used if provided project and location (or set GOOGLE_CLOUD_PROJECT/GOOGLE_CLOUD_LOCATION environment variables). This uses Application Default Credentials (ADC).

We recommend using an API key for quick testing and using Vertex AI with ADC for production environments.

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

Using Google AI

Google AI uses API Key for authentication. You can get an API key from Google AI Studio.

kind: embeddingModel
name: gemini-model
type: gemini
model: gemini-embedding-001
apiKey: ${GOOGLE_API_KEY}
dimension: 768

Using Vertex AI

Vertex AI uses Application Default Credentials (ADC) for authentication. Learn how to set up ADC here.

kind: embeddingModel
name: gemini-model
type: gemini
model: gemini-embedding-001
project: ${GOOGLE_CLOUD_PROJECT}
location: us-central1
dimension: 768

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

Reference