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Databricks Model Provider

To use an embedding model deployed to Databricks Mosaic AI Model Serving, specify the model endpoint name prefixed with databricks: in the from field and include the required parameters in the params section.

Parameters​

ParameterDescription
databricks_endpointThe Databricks workspace endpoint, e.g., dbc-a12cd3e4-56f7.cloud.databricks.com.
databricks_tokenThe Databricks API token to authenticate with the Databricks Models API. Use the secret replacement syntax to reference a secret, e.g., ${secrets:my_databricks_token}.
databricks_client_idThe Databricks Service Principal Client ID. Can't be used with databricks_token.
databricks_client_secretThe Databricks Service Principal Client Secret. Can't be used with databricks_token.

Example spicepod.yaml configuration, using personal access token​

To learn more about how to set up personal access tokens, see Databricks PAT docs.

embeddings:
  - from: databricks:databricks-gte-large-en
    name: gte-large-en
    params:
      databricks_endpoint: dbc-46470731-42e5.cloud.databricks.com
      databricks_token: ${ secrets:SPICE_DATABRICKS_TOKEN }

Example spicepod.yaml configuration, using Databricks service principal​

Spice supports the Machine-to-Machine (M2M) OAuth flow with service principal credentials by utilizing the databricks_client_id and databricks_client_secret parameters. The runtime will automatically refresh the token.

The service principal must be granted the "Can Query" permission for model serving.

To learn more about how to set up the service principal, see Databricks M2M OAuth docs.

embeddings:
  - from: databricks:databricks-gte-large-en
    name: gte-large-en
    params:
      databricks_endpoint: dbc-42424242-4242.cloud.databricks.com
      databricks_client_id: ${secrets:DATABRICKS_CLIENT_ID}
      databricks_client_secret: ${secrets:DATABRICKS_CLIENT_SECRET}

Additional Information​

Refer to the Mosaic AI Model Serving documentation for more details on available models and configurations.