Pydantic AI (Google Vertex AI) Connection¶

The pydanticai-vertex connection type configures access to Google Vertex AI via the pydantic-ai framework. It backs PydanticAIVertexHook, the dedicated subclass of PydanticAIHook for Google Cloud’s project/location/service-account credential shape — none of which fit the plain api_key + base_url shape that the generic Pydantic AI Connection connection assumes. All fields live in extra; the password and host fields are hidden in the connection form.

Default Connection IDs¶

The PydanticAIVertexHook uses pydanticai_vertex_default by default.

Configuring the Connection¶

All fields below are extra (JSON) fields.

Model: Google model identifier (e.g. google-cloud:gemini-2.0-flash). The google-cloud: prefix is required — it is what makes pydantic-ai instantiate the GoogleCloudProvider, which is what accepts this hook’s project / location / service_account_info fields (see “Credentials” below).
GCP Project: Google Cloud project ID. Falls back to the GOOGLE_CLOUD_PROJECT environment variable.
Location / Region: Vertex AI region (e.g. us-central1). Falls back to the GOOGLE_CLOUD_LOCATION environment variable.
Force Vertex AI Mode: Legacy flag from pydantic-ai 1.x, where a single GoogleProvider took a vertexai argument. Not needed here: the google-cloud: model prefix above already makes GoogleCloudProvider hard-code vertexai=True unconditionally when it builds its client.

Important

Leave this field unset. Setting it currently breaks the connection: neither GoogleProvider nor GoogleCloudProvider accept a vertexai constructor argument, so the hook silently discards every other field on this connection (project, location, service account, API key) and falls back to resolving credentials from environment variables only. If auth unexpectedly falls back to env vars, check the task log for a “rejected kwargs” warning.
API Key: Google API key for Vertex AI Express Mode. Falls back to the GOOGLE_API_KEY environment variable. Cannot be combined with project / location / service_account_info (those select the credentials/ADC path instead, which takes precedence and nulls the API key). For the Generative Language API (non-Vertex, API-key-only), use the google: prefix on the generic Pydantic AI Connection connection instead.
Service Account Info: Service account key as an inline JSON object (with type, project_id, private_key, etc.) — not a file path.
Custom Endpoint URL: Override the Google API base URL (optional).

Credentials¶

The hook passes every field you set on to GoogleCloudProvider together; when more than one credential source is set at once, credentials / project / location take precedence over api_key (which is then ignored):

service_account_info — loaded into Google Cloud credentials and passed as credentials to the provider.
Application Default Credentials (GOOGLE_APPLICATION_CREDENTIALS, gcloud auth application-default login, Workload Identity, …) — used automatically once project and/or location are set without service_account_info.
api_key — for Vertex AI Express Mode, only used when none of the above are set.

Examples¶

Application Default Credentials (recommended)

Leave the credential fields empty and configure GOOGLE_APPLICATION_CREDENTIALS (or another ADC source) in the worker environment:

{
    "conn_type": "pydanticai-vertex",
    "extra": "{\"model\": \"google-cloud:gemini-2.0-flash\", \"project\": \"my-gcp-project\", \"location\": \"us-central1\"}"
}

Inline service account

{
    "conn_type": "pydanticai-vertex",
    "extra": "{\"model\": \"google-cloud:gemini-2.0-flash\", \"project\": \"my-gcp-project\", \"location\": \"us-central1\", \"service_account_info\": {\"type\": \"service_account\", \"project_id\": \"my-gcp-project\", \"private_key\": \"<contents of the service account JSON key's private_key field>\", \"client_email\": \"sa@my-gcp-project.iam.gserviceaccount.com\"}}"
}