Version: Next

Google Vertex AI Embedding Models

To use a hosted Google embedding model, specify the google path in the from field of your configuration.

Include the model ID in the from field; a model ID is required. For example, google:gemini-embedding-001.

Requests are sent to Vertex AI, which is scoped to a GCP project and region and authenticated with Google Cloud credentials. There is no API-key path: the Google AI Studio (generativelanguage.googleapis.com) endpoint and its google_api_key parameter are no longer supported.

The following parameters are specific to Google Vertex AI embedding models:

Parameter	Description	Default
`google_project`	Required. The GCP project ID. Only lowercase letters, digits, and hyphens.	-
`google_location`	Required. The GCP region (for example `us-central1`), or `global`. A region is served by `<region>-aiplatform.googleapis.com`; `global` by `aiplatform.googleapis.com`.	-
`google_service_account_path`	Path to a GCP service account JSON key file.	-
`google_service_account_key`	The GCP service account JSON key itself, as a string. Supports secret replacement.	-
`google_application_default_credentials`	Set to `true` to authenticate with Application Default Credentials, read from the path in the `GOOGLE_APPLICATION_CREDENTIALS` environment variable.	`false`
`dimensions`	The output dimensionality of the embeddings. Some embedding models support dynamic output sizes.	-

google_project and google_location are both required, and exactly one of google_service_account_path, google_service_account_key, or google_application_default_credentials must be set — setting none, or more than one, fails the embedding model at load with an error naming the three.

Below is an example configuration in spicepod.yaml:

embeddings:
  - from: google:gemini-embedding-001
    name: gemini_embeddings
    params:
      google_project: my-gcp-project
      google_location: us-central1
      google_service_account_path: /etc/spice/gcp-service-account.json
      dimensions: 768 # optional parameter

See Vertex AI text embeddings for the embedding models available in your project and region.

For detailed instructions and examples on running vector searches, refer to the Vector-Based Search documentation.