Google Vertex AI

OverviewCopy Anchor Link

GoogleVertexLLMService runs Gemini models through Vertex AI instead of the Gemini API. It extends GoogleLLMService, so streaming, tools, thinking, safety settings and grounding work the same way. Only authentication and the endpoint differ.

• GoogleVertexLLMService: Gemini on Vertex AI, with a Google Cloud project, a location and service account credentials or Application Default Credentials (ADC).

InstallationCopy Anchor Link

bun add @pipedog/google

import { GoogleVertexLLMService } from "@pipedog/google/vertex/llm";

PrerequisitesCopy Anchor Link

• A Google Cloud project with the Vertex AI API enabled.
• A service account with access to Vertex AI, as a key file or a JSON string, or ADC (gcloud auth application-default login, or the attached service account on GCP).
• The examples read GOOGLE_CLOUD_PROJECT_ID, and GOOGLE_APPLICATION_CREDENTIALS (a key file path) or GOOGLE_CREDENTIALS_JSON (the key itself).

GoogleVertexLLMServiceCopy Anchor Link

ConfigurationCopy Anchor Link

SettingsCopy Anchor Link

GoogleVertexLLMService.Settings has the same fields and defaults as GoogleLLMService.Settings: the default model is "gemini-3.6-flash", maxTokens is 4096, and the other settings start as null.

UsageCopy Anchor Link

import { GoogleVertexLLMService } from "@pipedog/google/vertex/llm";

const llm = new GoogleVertexLLMService({
  credentialsPath: process.env.GOOGLE_APPLICATION_CREDENTIALS ?? "",
  projectId: process.env.GOOGLE_CLOUD_PROJECT_ID ?? "",
  settings: new GoogleVertexLLMService.Settings({
    model: "gemini-3.6-flash",
    systemInstruction: "You are a helpful voice assistant.",
  }),
});

With the service account key as a JSON string:

// A service account key as a JSON string, e.g. from a secret store.
const fromJson = new GoogleVertexLLMService({
  credentials: process.env.GOOGLE_CREDENTIALS_JSON ?? "",
  projectId: process.env.GOOGLE_CLOUD_PROJECT_ID ?? "",
});

With Application Default Credentials and a regional endpoint:

// No credentials option: Application Default Credentials, resolved on the
// first request. Gemini 2.5 models are served from regional endpoints.
const regional = new GoogleVertexLLMService({
  projectId: process.env.GOOGLE_CLOUD_PROJECT_ID ?? "",
  location: "us-east4",
  settings: new GoogleVertexLLMService.Settings({ model: "gemini-2.5-flash" }),
});

AuthenticationCopy Anchor Link

The service picks credentials in this order:

1. credentials: a service account key as a JSON string. It is parsed in the constructor.
2. credentialsPath: the path to a key file, read by the client.
3. Neither: Application Default Credentials.

All three go to @google/genai as googleAuthOptions with the cloud-platform scope, and google-auth-library inside the SDK loads the key file, finds ADC and refreshes the access token as it expires.

Event handlersCopy Anchor Link

The same as GoogleLLMService.

NotesCopy Anchor Link

• Passing apiKey throws: Vertex AI takes credentials, credentialsPath or ADC.
• projectId is required. location defaults to "global", the only location that serves the Gemini 3 series. Regional endpoints ("us-east4", "us-central1", ...) serve the Gemini 2.5 series.
• The Vertex adapter also accepts gs:// file URLs in the context: Vertex reads them through its own IAM.

Runtime notesCopy Anchor Link

Generated from pipecat.runtimes and pipecat.compat in packages/google/package.json: ✅ means the compat smoke test passes on that runtime.

Authentication goes through google-auth-library, which only the Node build of @google/genai supports. GoogleVertexLLMService runs on Bun, Node and Deno, not on Cloudflare Workers. On Workers, use GoogleLLMService with a Gemini API key.

The deprecated upstream model, params and systemInstruction constructor options are not ported; use settings.