Google Gen AI SDK

Das Google Gen AI SDK bietet eine einheitliche Schnittstelle zu Gemini 2.5 Pro- und Gemini 2.0-Modellen über die Gemini Developer API und die Gemini API in Vertex AI. Mit wenigen Ausnahmen kann Code, der auf einer Plattform ausgeführt wird, auf beiden Plattformen ausgeführt werden. Das bedeutet, dass Sie eine Anwendung mit der Gemini Developer API prototypisieren und dann zu Vertex AI migrieren können, ohne den Code neu schreiben zu müssen.

Weitere Informationen zu den Unterschieden zwischen der Gemini Developer API und Gemini in Vertex AI finden Sie unter Von der Gemini Developer API zur Gemini API in Vertex AI migrieren.

Python

Das Google Gen AI SDK for Python ist auf PyPI und GitHub verfügbar:

Weitere Informationen finden Sie in der Python SDK-Referenz.

Installieren

pip install --upgrade google-genai

Umgebungsvariablen für die Verwendung des Gen AI SDK mit Vertex AI festlegen: 

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

Kurzanleitung

Wählen Sie eine der folgenden Optionen aus, je nachdem, ob Sie Vertex AI im Express-Modus verwenden oder nicht.

  • Vertex AI mit allen Google Cloud Funktionen und Diensten verwenden
from google import genai
from google.genai.types import HttpOptions

client = genai.Client(http_options=HttpOptions(api_version="v1"))
response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="How does AI work?",
)
print(response.text)
# Example response:
# Okay, let's break down how AI works. It's a broad field, so I'll focus on the ...
#
# Here's a simplified overview:
# ...
  • Vertex AI im Express-Modus verwenden
from google import genai

# TODO(developer): Update below line
API_KEY = "YOUR_API_KEY"

client = genai.Client(vertexai=True, api_key=API_KEY)

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="Explain bubble sort to me.",
)

print(response.text)
# Example response:
# Bubble Sort is a simple sorting algorithm that repeatedly steps through the list

Go

Das Google Gen AI SDK for Go ist auf go.dev und GitHub verfügbar:

Installieren

go get google.golang.org/genai

Umgebungsvariablen für die Verwendung des Gen AI SDK mit Vertex AI festlegen: 

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

Kurzanleitung

import (
	"context"
	"fmt"
	"io"

	"google.golang.org/genai"
)

// generateWithText shows how to generate text using a text prompt.
func generateWithText(w io.Writer) error {
	ctx := context.Background()

	client, err := genai.NewClient(ctx, &genai.ClientConfig{
		HTTPOptions: genai.HTTPOptions{APIVersion: "v1"},
	})
	if err != nil {
		return fmt.Errorf("failed to create genai client: %w", err)
	}

	resp, err := client.Models.GenerateContent(ctx,
		"gemini-2.5-flash",
		genai.Text("How does AI work?"),
		nil,
	)
	if err != nil {
		return fmt.Errorf("failed to generate content: %w", err)
	}

	respText := resp.Text()

	fmt.Fprintln(w, respText)
	// Example response:
	// That's a great question! Understanding how AI works can feel like ...
	// ...
	// **1. The Foundation: Data and Algorithms**
	// ...

	return nil
}

Node.js

Das Google Gen AI SDK für TypeScript und JavaScript ist auf npm und GitHub verfügbar:

Installieren

npm install @google/genai

Umgebungsvariablen für die Verwendung des Gen AI SDK mit Vertex AI festlegen: 

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

Kurzanleitung

/**
 * @license
 * Copyright 2025 Google LLC
 * SPDX-License-Identifier: Apache-2.0
 */
import {GoogleGenAI} from '@google/genai';

const GEMINI_API_KEY = process.env.GEMINI_API_KEY;
const GOOGLE_CLOUD_PROJECT = process.env.GOOGLE_CLOUD_PROJECT;
const GOOGLE_CLOUD_LOCATION = process.env.GOOGLE_CLOUD_LOCATION;
const GOOGLE_GENAI_USE_VERTEXAI = process.env.GOOGLE_GENAI_USE_VERTEXAI;

async function generateContentFromMLDev() {
  const ai = new GoogleGenAI({vertexai: false, apiKey: GEMINI_API_KEY});
  const response = await ai.models.generateContent({
    model: 'gemini-2.0-flash',
    contents: 'why is the sky blue?',
  });
  console.debug(response.text);
}

async function generateContentFromVertexAI() {
  const ai = new GoogleGenAI({
    vertexai: true,
    project: GOOGLE_CLOUD_PROJECT,
    location: GOOGLE_CLOUD_LOCATION,
  });
  const response = await ai.models.generateContent({
    model: 'gemini-2.0-flash',
    contents: 'why is the sky blue?',
  });
  console.debug(response.text);
}

async function main() {
  if (GOOGLE_GENAI_USE_VERTEXAI) {
    await generateContentFromVertexAI().catch((e) =>
      console.error('got error', e),
    );
  } else {
    await generateContentFromMLDev().catch((e) =>
      console.error('got error', e),
    );
  }
}

main();

Java

Das Google Gen AI SDK für Java ist auf Maven Central und GitHub verfügbar:

Maven-Installation

<dependencies>
  <dependency>
    <groupId>com.google.genai</groupId>
    <artifactId>google-genai</artifactId>
    <version>1.4.1</version>
  </dependency>
</dependencies>

Umgebungsvariablen für die Verwendung des Gen AI SDK mit Vertex AI festlegen: 

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

Kurzanleitung

/*
 * Copyright 2025 Google LLC
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

/**
 * Usage:
 *
 * <p>1a. If you are using Vertex AI, setup ADC to get credentials:
 * https://cloud.google.com/docs/authentication/provide-credentials-adc#google-idp
 *
 * <p>Then set Project, Location, and USE_VERTEXAI flag as environment variables:
 *
 * <p>export GOOGLE_CLOUD_PROJECT=YOUR_PROJECT
 *
 * <p>export GOOGLE_CLOUD_LOCATION=YOUR_LOCATION
 *
 * <p>export GOOGLE_GENAI_USE_VERTEXAI=true
 *
 * <p>1b. If you are using Gemini Developer API, set an API key environment variable. You can find a
 * list of available API keys here: https://aistudio.google.com/app/apikey
 *
 * <p>export GOOGLE_API_KEY=YOUR_API_KEY
 *
 * <p>2. Compile the java package and run the sample code.
 *
 * <p>mvn clean compile exec:java -Dexec.mainClass="com.google.genai.examples.GenerateContent"
 * -Dexec.args="YOUR_MODEL_ID"
 */
package com.google.genai.examples;

import com.google.genai.Client;
import com.google.genai.types.GenerateContentResponse;

/** An example of using the Unified Gen AI Java SDK to generate content. */
public final class GenerateContent {
  public static void main(String[] args) {
    final String modelId;
    if (args.length != 0) {
      modelId = args[0];
    } else {
      modelId = Constants.GEMINI_MODEL_NAME;
    }

    // Instantiate the client. The client by default uses the Gemini Developer API. It gets the API
    // key from the environment variable `GOOGLE_API_KEY`. Vertex AI API can be used by setting the
    // environment variables `GOOGLE_CLOUD_LOCATION` and `GOOGLE_CLOUD_PROJECT`, as well as setting
    // `GOOGLE_GENAI_USE_VERTEXAI` to "true".
    //
    // Note: Some services are only available in a specific API backend (Gemini or Vertex), you will
    // get a `UnsupportedOperationException` if you try to use a service that is not available in
    // the backend you are using.
    Client client = new Client();

    if (client.vertexAI()) {
      System.out.println("Using Vertex AI");
    } else {
      System.out.println("Using Gemini Developer API");
    }

    GenerateContentResponse response =
        client.models.generateContent(modelId, "What is your name?", null);

    // Gets the text string from the response by the quick accessor method `text()`.
    System.out.println("Unary response: " + response.text());

    // Gets the http headers from the response.
    response
        .sdkHttpResponse()
        .ifPresent(
            httpResponse ->
                System.out.println("Response headers: " + httpResponse.headers().orElse(null)));
  }

  private GenerateContent() {}
}