Documentation Index
Fetch the complete documentation index at: https://mintlify.com/firebase/genkit/llms.txt
Use this file to discover all available pages before exploring further.
Vertex AI Plugin
The Vertex AI plugin is now part of the unified @genkit-ai/google-genai package, which provides access to both Google AI (Gemini Developer API) and Vertex AI models.
This page documents the Vertex AI functionality within the @genkit-ai/google-genai plugin. For the complete plugin documentation including Google AI features, see Google GenAI Plugin. Installation
npm install @genkit-ai/google-genai
Quick Start
import { genkit } from 'genkit';
import { vertexAI } from '@genkit-ai/google-genai';
const ai = genkit({
plugins: [vertexAI()],
model: vertexAI.model('gemini-2.5-flash'),
});
const { text } = await ai.generate('Hello from Vertex AI!');
console.log(text);
Authentication
Vertex AI supports two authentication methods:
Application Default Credentials (Production)
The standard method for production deployments. Uses credentials from:
- Service account on Google Cloud Platform
- User credentials from
gcloud auth application-default login locally
Requirements:
- Google Cloud Project with billing enabled
- Vertex AI API enabled
- Proper IAM permissions
import { genkit } from 'genkit';
import { vertexAI } from '@genkit-ai/google-genai';
const ai = genkit({
plugins: [
vertexAI({
location: 'us-central1', // Regional endpoint
// projectId: 'my-project', // Optional, auto-detected from ADC
}),
],
});
Vertex AI Express Mode (Development)
Streamlined access using just an API key, without billing setup. Ideal for:
- Quick experimentation
- Learning and prototyping
- Generous free tier quotas
Learn more about Express Mode
import { genkit } from 'genkit';
import { vertexAI } from '@genkit-ai/google-genai';
const ai = genkit({
plugins: [
vertexAI({
apiKey: process.env.VERTEX_EXPRESS_API_KEY,
}),
],
});
projectId or location.
Available Models
Gemini Models
gemini-2.5-flash - Fast, efficient for most tasksgemini-2.5-pro - Advanced reasoning and complex tasksgemini-1.5-flash - Previous generation fast modelgemini-1.5-pro - Previous generation advanced model
Image Generation
imagen-3.0-generate-002 - High-quality image generation
Music Generation
lyria-002 - AI music generation (Vertex AI exclusive)
Embeddings
text-embedding-005 - Text embeddings
Usage Examples
Text Generation
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-2.5-pro'),
prompt: 'Explain quantum computing in simple terms.',
});
console.log(response.text());
const response = await ai.generate({
model: vertexAI.model('gemini-2.5-flash'),
prompt: [
{ text: 'What is in this image?' },
{ media: { url: 'https://example.com/image.jpg' } },
],
});
console.log(response.text());
Structured Output
import { z } from 'genkit';
const RecipeSchema = z.object({
name: z.string(),
ingredients: z.array(z.string()),
instructions: z.array(z.string()),
});
const response = await ai.generate({
model: vertexAI.model('gemini-2.5-pro'),
prompt: 'Create a recipe for chocolate chip cookies',
output: { schema: RecipeSchema },
});
console.log(response.output);
Text Embeddings
const embeddings = await ai.embed({
embedder: vertexAI.embedder('text-embedding-005'),
content: 'Text to embed for semantic search',
});
console.log(embeddings[0].embedding);
Image Generation with Imagen
const response = await ai.generate({
model: vertexAI.model('imagen-3.0-generate-002'),
prompt: 'A serene landscape with mountains and a lake at sunset',
});
const image = response.media();
console.log('Image URL:', image.url);
Music Generation with Lyria
const response = await ai.generate({
model: vertexAI.model('lyria-002'),
prompt: 'An upbeat electronic dance track with synthesizers',
});
const audio = response.media();
console.log('Audio URL:', audio.url);
Using in Flows
import { z } from 'genkit';
const summarizeFlow = ai.defineFlow(
{
name: 'summarizeDocument',
inputSchema: z.string(),
outputSchema: z.string(),
},
async (document) => {
const response = await ai.generate({
model: vertexAI.model('gemini-2.5-flash'),
prompt: `Summarize this document: ${document}`,
});
return response.text();
}
);
const summary = await summarizeFlow('Long document text...');
Configuration Options
Plugin Configuration
vertexAI({
// For ADC authentication:
location: 'us-central1', // GCP region (required for ADC)
projectId: 'my-project', // GCP project ID (optional, auto-detected)
// For Express Mode:
apiKey: 'your-api-key', // API key (don't use with location/projectId)
})
Model Configuration
const response = await ai.generate({
model: vertexAI.model('gemini-2.5-pro'),
prompt: 'Your prompt',
config: {
temperature: 0.7, // Randomness (0.0-1.0)
maxOutputTokens: 1024, // Max response length
topK: 40, // Top-K sampling
topP: 0.95, // Nucleus sampling
stopSequences: ['END'], // Stop generation sequences
},
});
Vertex AI Features
Vertex AI offers enterprise features beyond the Gemini Developer API:
Enterprise Capabilities
- IAM Integration - Google Cloud IAM for access control
- VPC Support - Private networking options
- Audit Logging - Comprehensive audit trails
- Data Residency - Regional data processing
- SLA Support - Enterprise service level agreements
Advanced Features
- Fine-tuning - Custom model training
- Model Garden - Access to multiple model families
- Lyria Music Generation - AI-powered music creation
- Batch Prediction - Efficient bulk processing
- Model Monitoring - Performance tracking
Pricing
Vertex AI uses Google Cloud billing. See Vertex AI Pricing for details.
Express Mode offers generous free tier quotas for experimentation.
Best Practices
Development vs Production
Development:
// Use Express Mode for quick prototyping
const ai = genkit({
plugins: [
vertexAI({ apiKey: process.env.VERTEX_EXPRESS_API_KEY }),
],
});
// Use ADC for production deployments
const ai = genkit({
plugins: [
vertexAI({
location: 'us-central1',
// projectId auto-detected from environment
}),
],
});
Error Handling
try {
const response = await ai.generate({
model: vertexAI.model('gemini-2.5-flash'),
prompt: 'Your prompt',
});
console.log(response.text());
} catch (error) {
console.error('Vertex AI error:', error);
// Handle quota limits, authentication errors, etc.
}
Rate Limiting
Implement retry logic for production applications:
import { retry } from 'genkit/model/middleware';
const response = await ai.generate({
model: vertexAI.model('gemini-2.5-flash'),
prompt: 'Your prompt',
use: [
retry({
maxRetries: 3,
initialDelayMs: 1000,
backoffFactor: 2,
}),
],
});
Migration from Legacy Plugin
If migrating from the old @genkit-ai/vertexai package:
Old:
import { vertexAI } from '@genkit-ai/vertexai';
import { vertexAI } from '@genkit-ai/google-genai';
Links