Vertex AI plugin

The Vertex AI plugin provides access to Google Cloud’s enterprise-grade AI platform, offering advanced features beyond basic model access. Use this for enterprise applications that need grounding, Vector Search, Model Garden, or evaluation capabilities.

Accessing Google GenAI Models via Vertex AI

All languages support accessing Google’s generative AI models (Gemini, Imagen, etc.) through Vertex AI with enterprise authentication and features.

The unified Google GenAI plugin provides access to models via Vertex AI using the vertexAI initializer:

Basic Model Access

Installation

npm i --save @genkit-ai/google-genai

Configuration

import { genkit } from 'genkit';
import { vertexAI } from '@genkit-ai/google-genai';

const ai = genkit({
  plugins: [
    vertexAI({ location: 'us-central1' }), // Regional endpoint
    // vertexAI({ location: 'global' }),      // Global endpoint
  ],
});

Authentication Methods:

Application Default Credentials (ADC): The standard method for most Vertex AI use cases, especially in production. It uses the credentials from the environment (e.g., service account on GCP, user credentials from gcloud auth application-default login locally). This method requires a Google Cloud Project with billing enabled and the Vertex AI API enabled.
Vertex AI Express Mode: A streamlined way to try out many Vertex AI features using just an API key, without needing to set up billing or full project configurations. This is ideal for quick experimentation and has generous free tier quotas. Learn More about Express Mode.

// Using Vertex AI Express Mode (Easy to start, some limitations)
// Get an API key from the Vertex AI Studio Express Mode setup.
vertexAI({ apiKey: process.env.VERTEX_EXPRESS_API_KEY }),

Note: When using Express Mode, you do not provide projectId and location in the plugin config.

Basic Usage

import { genkit } from 'genkit';
import { vertexAI } from '@genkit-ai/google-genai';

const ai = genkit({
  plugins: [vertexAI({ location: 'us-central1' })],
});

const response = await ai.generate({
  model: vertexAI.model("gemini-pro-latest"),
  prompt: 'Explain Vertex AI in simple terms.',
});

console.log(response.text());

Text Embedding

const embeddings = await ai.embed({
  embedder: vertexAI.embedder('text-embedding-005'),
  content: 'Embed this text.',
});

Image Generation (Imagen)

const response = await ai.generate({
  model: vertexAI.model('imagen-3.0-generate-002'),
  prompt: 'A beautiful watercolor painting of a castle in the mountains.',
});

const generatedImage = response.media();

Thinking Config

Thinking Level (Gemini 3.0+)

const response = await ai.generate({
  model: googleAI.model("gemini-3.1-pro-preview"),
  prompt: 'what is heavier, one kilo of steel or one kilo of feathers',
  config: {
    thinkingConfig: {
      thinkingLevel: "HIGH", // Or 'LOW' or 'MEDIUM'
      includeThoughts: true,
    },
  },
});

Thinking Budget (Gemini 2.5)

const { message } = await ai.generate({
  model: googleAI.model('gemini-2.5-pro'),
  prompt: 'what is heavier, one kilo of steel or one kilo of feathers',
  config: {
    thinkingConfig: {
      thinkingBudget: 1024,
      includeThoughts: true,
    },
  },
});

Enterprise Features (JavaScript Only)

Model Garden Integration

Access third-party models through Vertex AI Model Garden:

Claude 4.6 Models

import { vertexModelGarden } from "@genkit-ai/vertexai/modelgarden";

const ai = genkit({
  plugins: [vertexAIModelGarden({ location: "us-central1" })],
});

const response = await ai.generate({
  model: vertexModelGarden.model("claude-sonnet-4-6"),
  prompt: 'What should I do when I visit Melbourne?',
});

For the full list of available Claude models see: Available Claude models

Llama 4

const ai = genkit({
  plugins: [vertexAIModelGarden({ location: "us-central1" })],
});

const response = await ai.generate({
  model: vertexModelGarden.model(
    "meta/llama-4-maverick-17b-128e-instruct-maas",
  ),
  prompt: "Write a function that adds two numbers together",
});

For the full list of available Llama models see: Fully-managed Llama models

Mistral AI Models

const ai = genkit({
  plugins: [vertexAIModelGarden({ location: "us-central1" })],
});

const response = await ai.generate({
  model: vertexModelGarden.model("mistral-medium-3"),
  prompt: 'Write a function that adds two numbers together',
  config: {
    temperature: 0.7,
    maxOutputTokens: 1024,
    topP: 0.9,
    topK: 40,
  },
});

For the full list of available Mistral AI models see: Mistral AI models

Evaluation Metrics

Use Vertex AI Rapid Evaluation API for model evaluation:

import {
  vertexAIEvaluation,
  VertexAIEvaluationMetricType,
} from "@genkit-ai/vertexai/evaluation";

const ai = genkit({
  plugins: [
    vertexAIEvaluation({
      location: 'us-central1',
      metrics: [
        VertexAIEvaluationMetricType.SAFETY,
        {
          type: VertexAIEvaluationMetricType.ROUGE,
          metricSpec: {
            rougeType: 'rougeLsum',
          },
        },
      ],
    }),
  ],
});

Available metrics:

BLEU: Translation quality
ROUGE: Summarization quality
Fluency: Text fluency
Safety: Content safety
Groundedness: Factual accuracy
Summarization Quality/Helpfulness/Verbosity: Summary evaluation

Run evaluations:

genkit eval:run
genkit eval:flow -e vertexai/safety

Vector Search

Use Vertex AI Vector Search for enterprise-grade vector operations:

Setup

Create a Vector Search index in the Google Cloud Console
Configure dimensions based on your embedding model:
- gemini-embedding-001: 768 dimensions
- text-multilingual-embedding-002: 768 dimensions
- multimodalEmbedding001: 128, 256, 512, or 1408 dimensions
Deploy the index to a standard endpoint

Configuration

import { vertexAIVectorSearch } from '@genkit-ai/vertexai/vectorsearch';
import {
  getFirestoreDocumentIndexer,
  getFirestoreDocumentRetriever,
} from "@genkit-ai/vertexai/vectorsearch";

const ai = genkit({
  plugins: [
    vertexAIVectorSearch({
      projectId: 'your-project-id',
      location: 'us-central1',
      vectorSearchOptions: [
        {
          indexId: 'your-index-id',
          indexEndpointId: 'your-endpoint-id',
          deployedIndexId: 'your-deployed-index-id',
          publicDomainName: 'your-domain-name',
          documentRetriever: firestoreDocumentRetriever,
          documentIndexer: firestoreDocumentIndexer,
          embedder: vertexAI.embedder('gemini-embedding-001'),
        },
      ],
    }),
  ],
});

Usage

import {
  vertexAiIndexerRef,
  vertexAiRetrieverRef,
} from "@genkit-ai/vertexai/vectorsearch";

// Index documents
await ai.index({
  indexer: vertexAiIndexerRef({
    indexId: 'your-index-id',
  }),
  documents,
});

// Retrieve similar documents
const results = await ai.retrieve({
  retriever: vertexAiRetrieverRef({
    indexId: 'your-index-id',
  }),
  query: queryDocument,
});

Next Steps

Learn about generating content to understand how to use these models effectively
Explore evaluation to leverage Vertex AI’s evaluation metrics
See RAG to implement retrieval-augmented generation with Vector Search
Check out creating flows to build structured AI workflows
For simple API key access, see the Google AI plugin