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.

Getting Started

For simple API key access to Google’s AI models, start with the Google AI plugin. This page covers enterprise features available through Vertex AI.

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 Google Generative AI plugin provides access to Google’s Gemini models through Vertex AI using Google Cloud authentication.

Configuration

To use this plugin, import the googlegenai package and pass googlegenai.VertexAI to WithPlugins() in the Genkit initializer:

import "github.com/firebase/genkit/go/plugins/googlegenai"

g := genkit.Init(context.Background(), genkit.WithPlugins(&googlegenai.VertexAI{}))

The plugin requires you to specify your Google Cloud project ID, the region to which you want to make Vertex API requests, and your Google Cloud project credentials.

• By default, googlegenai.VertexAI gets your Google Cloud project ID from the GOOGLE_CLOUD_PROJECT environment variable.

  You can also pass this value directly:

genkit.WithPlugins(&googlegenai.VertexAI{ProjectID: "my-project-id"})

• By default, googlegenai.VertexAI gets the Vertex AI API location from the GOOGLE_CLOUD_LOCATION environment variable.

  You can also pass this value directly:

genkit.WithPlugins(&googlegenai.VertexAI{Location: "us-central1"})

• To provide API credentials, you need to set up Google Cloud Application Default Credentials.

  1. To specify your credentials:

     • If you’re running your flow from a Google Cloud environment (Cloud Functions, Cloud Run, and so on), this is set automatically.

     • On your local dev environment, do this by running:

gcloud auth application-default login

• For other environments, see the Application Default Credentials docs.

2. In addition, make sure the account is granted the Vertex AI User IAM role (roles/aiplatform.user). See the Vertex AI access control docs.

Usage

Generative models

To get a reference to a supported model, specify its identifier to googlegenai.VertexAIModel:

model := googlegenai.VertexAIModel(g, "gemini-2.5-flash")

Alternatively, you may create a ModelRef which pairs the model name with its config:

modelRef := googlegenai.VertexAIModelRef("gemini-2.5-flash", &genai.GenerateContentConfig{
    Temperature: genai.Ptr[float32](0.5),
    MaxOutputTokens: genai.Ptr[int32](500),
    // Other configuration...
})

The following models are supported: gemini-1.5-pro, gemini-1.5-flash, gemini-2.0-pro, gemini-2.5-flash, and other experimental models.

Model references have a Generate() method that calls the Vertex AI API:

resp, err := genkit.Generate(ctx, g, ai.WithModel(modelRef), ai.WithPrompt("Tell me a joke."))
if err != nil {
    return err
}

log.Println(resp.Text())

See Generating content with AI models for more information.

Embedding models

To get a reference to a supported embedding model, specify its identifier to googlegenai.VertexAIEmbedder:

embeddingModel := googlegenai.VertexAIEmbedder(g, "text-embedding-004")

The following models are supported:

• textembedding-gecko@003, textembedding-gecko@002, textembedding-gecko@001, text-embedding-004, textembedding-gecko-multilingual@001, text-multilingual-embedding-002, and multimodalembedding

Embedder references have an Embed() method that calls the Vertex AI API:

resp, err := genkit.Embed(ctx, g, ai.WithEmbedder(embeddingModel), ai.WithTextDocs(userInput))
if err != nil {
    return err
}

You can retrieve docs by passing in an input to a Retriever’s Retrieve() method:

resp, err := genkit.Retrieve(ctx, g, ai.WithRetriever(myRetriever), ai.WithTextDocs(userInput))
if err != nil {
    return err
}

See Retrieval-augmented generation (RAG) for more information.

Advanced Features

Advanced Vertex AI features like Vector Search, Model Garden, and evaluation metrics require custom implementation using the Google Cloud SDK directly. See the Vertex AI documentation for implementation details.

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