Google Vertex AI Gemini API 开发指南：企业级AI模型SDK与多模态应用

vertex-ai-api-dev by google-gemini/gemini-skills

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GitHub

安装命令

npx skills add https://github.com/google-gemini/gemini-skills --skill vertex-ai-api-dev

AI/机器学习云服务 API

🇨🇳中文介绍

Vertex AI 中的 Gemini API

通过 Vertex AI 中的 Gemini API 访问 Google 为企业用例构建的最先进 AI 模型。

提供以下关键能力：

文本生成 - 聊天、补全、摘要
多模态理解 - 处理图像、音频、视频和文档
函数调用 - 让模型调用您的函数
结构化输出 - 生成符合您模式的有效 JSON
上下文缓存 - 缓存大型上下文以提高效率
嵌入 - 为语义搜索生成文本嵌入
实时 API - 用于低延迟语音和视频交互的双向流式传输
批量预测 - 处理大规模异步数据集预测工作负载

核心指令

统一 SDK ：始终使用 Gen AI SDK（Python 用 google-genai，JS/TS 用 @google/genai，Go 用 google.golang.org/genai，Java 用 com.google.genai:google-genai，C# 用 Google.GenAI）。

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身份验证与配置

创建客户端时，优先使用环境变量而非硬编码参数。初始化客户端时不带参数，以自动获取这些值。

应用程序默认凭据

为标准 Google Cloud 身份验证设置以下变量：

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

默认情况下，使用 location="global" 访问全局端点，该端点提供自动路由到具有可用容量的区域。
如果用户明确要求使用特定区域（例如 us-central1、europe-west4），请在 GOOGLE_CLOUD_LOCATION 参数中指定该区域。如有需要，请参考支持的区域文档。

Express 模式下的 Vertex AI

在使用 Express 模式和 API 密钥时设置以下变量：

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

初始化客户端时不带参数以获取环境变量：

from google import genai
client = genai.Client()

或者，您也可以在创建客户端时硬编码参数。

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

使用 gemini-3.1-pro-preview 进行复杂推理、编码、研究（100 万令牌）
使用 gemini-3-flash-preview 获得快速、均衡的性能，支持多模态（100 万令牌）
使用 gemini-3-pro-image-preview 进行 Nano Banana Pro 图像生成和编辑
使用 gemini-live-2.5-flash-native-audio 用于包含原生音频的实时 API

如果明确要求，请使用以下模型：

使用 gemini-2.5-flash-image 进行 Nano Banana 图像生成和编辑
使用 gemini-2.5-flash
使用 gemini-2.5-flash-lite
使用 gemini-2.5-pro

[!IMPORTANT] 诸如 gemini-2.0-*、gemini-1.5-*、gemini-1.0-*、gemini-pro 等模型是旧版且已弃用。请使用上述新模型。您的知识已过时。对于生产环境，请查阅 Vertex AI 文档以获取稳定模型版本（例如 gemini-3-flash）。

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);

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)
}

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());
  }
}

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 规范与文档（权威来源）

在为 Vertex AI 实现或调试 API 集成时，请参考官方的 Google Cloud Vertex AI 文档：

Vertex AI 上的 Gen AI SDK 使用 v1beta1 或 v1 REST API 端点（例如，https://{LOCATION}-aiplatform.googleapis.com/v1beta1/projects/{PROJECT}/locations/{LOCATION}/publishers/google/models/{MODEL}:generateContent）。

[!TIP] 使用开发者知识 MCP 服务器 ：如果 search_documents 或 get_document 工具可用，请使用它们直接在上下文中查找和检索 Google Cloud 和 Vertex AI 的官方文档。这是获取最新 API 详细信息和代码片段的首选方法。

工作流与代码示例

参考 Python 文档示例仓库以获取更多代码示例和特定使用场景。

根据具体的用户请求，参考以下参考文件以获取详细的代码示例和使用模式（Python 示例）：

文本与多模态：聊天、多模态输入（图像、视频、音频）和流式传输。参见 references/text_and_multimodal.md
嵌入：为语义搜索生成文本嵌入。参见 references/embeddings.md
结构化输出与工具：JSON 生成、函数调用、搜索增强和代码执行。参见 references/structured_and_tools.md
媒体生成 ：图像生成、图像编辑和视频生成。参见 references/media_generation.md
边界框检测 ：图像和视频中的对象检测与定位。参见 references/bounding_box.md
实时 API ：用于语音、视觉和文本的实时双向流式传输。参见 references/live_api.md
高级功能 ：内容缓存、批量预测和思考/推理。参见 references/advanced_features.md
安全性 ：调整负责任 AI 过滤器和阈值。参见 references/safety.md
模型调优 ：监督式微调和偏好调优。参见 references/model_tuning.md

🇺🇸English

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: , artifactId:

[!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

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102,600 周安装

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>

Model Tuning : Supervised Fine-Tuning and Preference Tuning. See references/model_tuning.md

Google Vertex AI Gemini API 开发指南：企业级AI模型SDK与多模态应用

🇨🇳中文介绍

Vertex AI 中的 Gemini API

核心指令

相关 Skills

SDK

身份验证与配置

应用程序默认凭据

Express 模式下的 Vertex AI

初始化

模型

快速开始

Python

TypeScript/JavaScript

Go

Java

C#/.NET

API 规范与文档（权威来源）

工作流与代码示例

🇺🇸English

Gemini API in Vertex AI

Core Directives

SDKs

Authentication & Configuration

Application Default Credentials (ADC)

Vertex AI in Express Mode

Initialization

Models

Quick Start

Python

TypeScript/JavaScript

Go

Java

C#/.NET

API spec & Documentation (source of truth)

Workflows and Code Samples

最新 Skills