Gemini on Vertex AI

GeminiVertexProvider runs Google's Gemini models through Vertex AI in a pinned Google Cloud region instead of the public Gemini Developer API. Same models, same code — but the request is processed in the region you choose and is not retained to train Google's models. That makes it the backend to reach for when data residency matters (e.g. Québec Law 25 / PIPEDA, EU data boundaries).

It is a thin subclass of GeminiAIProvider — only the client construction differs (Vertex mode + Application Default Credentials instead of an API key). Generation, streaming, thinking, and the model catalog are all inherited.

Install & authenticate

pip install roomkit[gemini]              # no extra dependency — same SDK
gcloud auth application-default login    # provides ADC

Vertex uses Application Default Credentials — the standard Google Cloud chain (gcloud auth application-default login, GOOGLE_APPLICATION_CREDENTIALS, or a workload-identity service account). There is no API key.

Quick start

from roomkit import AIChannel, RoomKit
from roomkit.providers.gemini import GeminiVertexProvider, GeminiVertexConfig

provider = GeminiVertexProvider(
    GeminiVertexConfig(
        project="my-gcp-project",
        location="northamerica-northeast1",   # Montréal — required, no default
        model="gemini-3.1-flash-lite",
    )
)

kit = RoomKit()
ai = AIChannel("assistant", provider=provider, system_prompt="You are helpful.")
kit.register_channel(ai)

Why location is required

location has no default on purpose. Data residency is the reason to use Vertex, and a convenience default (e.g. global) could route requests out of your region and quietly defeat it. Pin it explicitly to the region your compliance regime requires:

Region	Location id
Montréal	northamerica-northeast1
Toronto	northamerica-northeast2
Belgium (EU)	europe-west1
Iowa (US)	us-central1

See Vertex AI locations for the full list and which models each region serves.

How it works

Aspect	Behaviour
Client	genai.Client(vertexai=True, project=…, location=…) — the same google-genai SDK as GeminiAIProvider, in Vertex mode
Auth	Application Default Credentials (no API key); GeminiVertexConfig.api_key is optional and ignored
Config	GeminiVertexConfig subclasses GeminiConfig, inheriting every generation field (model, max_tokens, temperature, thinking_level) so the two never drift
Models	The same Gemini catalog — available_models() / list_models() inherited
Thinking	Inherited: thinking_level requests thought summaries, surfaced as StreamThinkingDelta

See examples/gemini_vertex_ai.py for an end-to-end run.