The Google Generative AI plugin provides interfaces to Google's Gemini models through either the Gemini API or the Gemini API in Vertex AI.

Configuration

The configuration depends on which provider you choose:

Google AI

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

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

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

The plugin requires an API key for the Gemini API, which you can get from Google AI Studio.

Configure the plugin to use your API key by doing one of the following:

• Set the GEMINI_API_KEY environment variable to your API key.

• Specify the API key when you initialize the plugin:

  ai.WithPlugins(&googlegenai.GoogleAI{APIKey: "YOUR_API_KEY"})
  

  However, don't embed your API key directly in code! Use this feature only in conjunction with a service like Cloud Secret Manager or similar.

Vertex AI

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, err := 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 either googlegenai.GoogleAIModel or googlgenai.VertexAIModel:

model := googlegenai.GoogleAIModel(g, "gemini-2.0-flash")

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

modelRef := googlegenai.GoogleAIModelRef("gemini-2.0-flash", &googlegenai.GeminiConfig{
    Temperature: 0.5,
    MaxOutputTokens: 500,
    // Other configuration...
})

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

Model references have a Generate() method that calls the Google 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 either googlegenai.GoogleAIEmbedder or googlgenai.VertexAIEmbedder:

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

The following models are supported:

• Google AI

  text-embedding-004 and embedding-001

• Vertex AI

  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 Google AI API:

resp, err := ai.Embed(ctx, embeddingModel, ai.WithDocs(userInput))
if err != nil {
      return err
}

You can also pass an Embedder to an indexer's Index() method and a retriever's Retrieve() method:

if err := ai.Index(ctx, myIndexer, ai.WithDocs(docsToIndex...)); err != nil {
      return err
}

resp, err := ai.Retrieve(ctx, myRetriever, ai.WithDocs(userInput))
if err != nil {
      return err
}

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