Vertex AI API Dev

# 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`:

```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](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/start/gcp-auth): ```bash 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](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/learn/locations) if needed.

Vertex AI in Express Mode Set these variables when using [Express Mode](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/start/api-keys?usertype=expressmode) with an API key: ```bash export GOOGLE_API_KEY='your-api-key' export GOOGLE_GENAI_USE_VERTEXAI=true ```

Initialization Initialize the client without arguments to pick up environment variables: ```python from google import genai client = genai.Client() ```

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

```python 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 ```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 ```typescript 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 ```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 ```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 ```csharp 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