Gemini API in Vertex AI

Access Google's most advanced AI models built for enterprise use cases using the Gemini API in Vertex AI.

Provide these key capabilities:

• Text generation - Chat, completion, summarization
• Multimodal understanding - Process images, audio, video, and documents
• Function calling - Let the model invoke your functions
• Structured output - Generate valid JSON matching your schema
• Context caching - Cache large contexts for efficiency
• Embeddings - Generate text embeddings for semantic search
• Live Realtime API - Bidirectional streaming for low latency Voice and Video interactions
• Batch Prediction - Handle massive async dataset prediction workloads

Core Directives

• Unified SDK: ALWAYS use the Gen AI SDK (google-genai for Python, @google/genai for JS/TS, google.golang.org/genai for Go, com.google.genai:google-genai for Java, Google.GenAI for C#).
• Legacy SDKs: DO NOT use google-cloud-aiplatform, @google-cloud/vertexai, or google-generativeai.

SDKs

• Python: Install google-genai with pip install google-genai
• JavaScript/TypeScript: Install @google/genai with npm install @google/genai
• Go: Install google.golang.org/genai with go get google.golang.org/genai
• C#/.NET: Install Google.GenAI with dotnet add package Google.GenAI
• Java:

  • groupId: com.google.genai, artifactId: google-genai

  • Latest version can be found here: https://central.sonatype.com/artifact/com.google.genai/google-genai/versions (let's call it LAST_VERSION)

  • Install in build.gradle:

    implementation("com.google.genai:google-genai:${LAST_VERSION}")
    

  • Install Maven dependency in pom.xml:

    <dependency>
        <groupId>com.google.genai</groupId>
        <artifactId>google-genai</artifactId>
        <version>${LAST_VERSION}</version>
    </dependency>
    

[!WARNING] Legacy SDKs like google-cloud-aiplatform, @google-cloud/vertexai, and google-generativeai are deprecated. Migrate to the new SDKs above urgently by following the Migration Guide.

Authentication & Configuration

Prefer environment variables over hard-coding parameters when creating the client. Initialize the client without parameters to automatically pick up these values.

Application Default Credentials (ADC)

Set these variables for standard Google Cloud authentication:

export GOOGLE_CLOUD_PROJECT='your-project-id'
export GOOGLE_CLOUD_LOCATION='global'
export GOOGLE_GENAI_USE_VERTEXAI=true

• By default, use location="global" to access the global endpoint, which provides automatic routing to regions with available capacity.
• If a user explicitly asks to use a specific region (e.g., us-central1, europe-west4), specify that region in the GOOGLE_CLOUD_LOCATION parameter instead. Reference the supported regions documentation if needed.

Vertex AI in Express Mode

Set these variables when using Express Mode with an API key:

export GOOGLE_API_KEY='your-api-key'
export GOOGLE_GENAI_USE_VERTEXAI=true

Initialization

Initialize the client without arguments to pick up environment variables:

from google import genai
client = genai.Client()

Alternatively, you can hard-code in parameters when creating the client.

from google import genai
client = genai.Client(vertexai=True, project="your-project-id", location="global")

Models

• Use gemini-3.1-pro-preview for complex reasoning, coding, research (1M tokens)
• Use gemini-3-flash-preview for fast, balanced performance, multimodal (1M tokens)
• Use gemini-3-pro-image-preview for Nano Banana Pro image generation and editing
• Use gemini-live-2.5-flash-native-audio for Live Realtime API including native audio

Use the following models if explicitly requested:

• Use gemini-2.5-flash-image for Nano Banana image generation and editing
• Use gemini-2.5-flash
• Use gemini-2.5-flash-lite
• Use gemini-2.5-pro

[!IMPORTANT] Models like gemini-2.0-*, gemini-1.5-*, gemini-1.0-*, gemini-pro are legacy and deprecated. Use the new models above. Your knowledge is outdated. For production environments, consult the Vertex AI documentation for stable model versions (e.g. gemini-3-flash).

Quick Start

Python

from google import genai
client = genai.Client()
response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="Explain quantum computing"
)
print(response.text)

TypeScript/JavaScript

import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({ vertexai: { project: "your-project-id", location: "global" } });
const response = await ai.models.generateContent({
    model: "gemini-3-flash-preview",
    contents: "Explain quantum computing"
});
console.log(response.text);

Go

package main

import (
	"context"
	"fmt"
	"log"
	"google.golang.org/genai"
)

func main() {
	ctx := context.Background()
	client, err := genai.NewClient(ctx, &genai.ClientConfig{
		Backend:  genai.BackendVertexAI,
		Project:  "your-project-id",
		Location: "global",
	})
	if err != nil {
		log.Fatal(err)
	}

	resp, err := client.Models.GenerateContent(ctx, "gemini-3-flash-preview", genai.Text("Explain quantum computing"), nil)
	if err != nil {
		log.Fatal(err)
	}

	fmt.Println(resp.Text)
}

Java

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

public class GenerateTextFromTextInput {
  public static void main(String[] args) {
    Client client = Client.builder().vertexAi(true).project("your-project-id").location("global").build();
    GenerateContentResponse response =
        client.models.generateContent(
            "gemini-3-flash-preview",
            "Explain quantum computing",
            null);

    System.out.println(response.text());
  }
}

C#/.NET

using Google.GenAI;

var client = new Client(
    project: "your-project-id",
    location: "global",
    vertexAI: true
);

var response = await client.Models.GenerateContent(
    "gemini-3-flash-preview",
    "Explain quantum computing"
);

Console.WriteLine(response.Text);

API spec & Documentation (source of truth)

When implementing or debugging API integration for Vertex AI, refer to the official Google Cloud Vertex AI documentation:

• Vertex AI Gemini Documentation: https://cloud.google.com/vertex-ai/generative-ai/docs/
• REST API Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/reference/rest

The Gen AI SDK on Vertex AI uses the v1beta1 or v1 REST API endpoints (e.g., https://{LOCATION}-aiplatform.googleapis.com/v1beta1/projects/{PROJECT}/locations/{LOCATION}/publishers/google/models/{MODEL}:generateContent).

[!TIP] Use the Developer Knowledge MCP Server: If the search_documents or get_document tools are available, use them to find and retrieve official documentation for Google Cloud and Vertex AI directly within the context. This is the preferred method for getting up-to-date API details and code snippets.

Workflows and Code Samples

Reference the Python Docs Samples repository for additional code samples and specific usage scenarios.

Depending on the specific user request, refer to the following reference files for detailed code samples and usage patterns (Python examples):

• Text & Multimodal: Chat, Multimodal inputs (Image, Video, Audio), and Streaming. See references/text_and_multimodal.md
• Embeddings: Generate text embeddings for semantic search. See references/embeddings.md
• Structured Output & Tools: JSON generation, Function Calling, Search Grounding, and Code Execution. See references/structured_and_tools.md
• Media Generation: Image generation, Image editing, and Video generation. See references/media_generation.md
• Bounding Box Detection: Object detection and localization within images and video. See references/bounding_box.md
• Live API: Real-time bidirectional streaming for voice, vision, and text. See references/live_api.md
• Advanced Features: Content Caching, Batch Prediction, and Thinking/Reasoning. See references/advanced_features.md
• Safety: Adjusting Responsible AI filters and thresholds. See references/safety.md
• Model Tuning: Supervised Fine-Tuning and Preference Tuning. See references/model_tuning.md