---
id: daily-gemini-api
name: "gemini-api"
url: https://skills.yangsir.net/skill/daily-gemini-api
author: google
domain: ai-llm-engineering
tags: ["google-gemini", "ai-api", "enterprise-ai", "llm"]
install_count: 8600
rating: 4.50 (7 reviews)
github: https://github.com/google/skills
---

# gemini-api

> 通过 Gemini API 访问 Google 企业级 AI 模型，支持多模态理解和生成

**Stats**: 8,600 installs · 4.5/5 (7 reviews)

## Before / After 对比

### 企业 AI 模型集成

**Before**:

需要自行搭建 AI 基础设施，处理模型部署、API 网关、认证授权等，从零搭建需要数周时间

**After**:

直接调用 Google 企业级 Gemini API，1-2 小时完成原型开发，无需管理底层基础设施

| Metric | Before | After | Change |
|---|---|---|---|
| 开发时间 | 120小时 | 2小时 | -98% |

## Readme

# gemini-api

IMPORTANT: Agent Platform (full name Gemini Enterprise Agent Platform) was previously named "Vertex AI" and many web resources use the legacy branding.

# Gemini API in Agent Platform

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

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](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](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/start/gcp-auth):

```
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.

### Agent Platform 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:

```
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)

IMPORTANT: Do not use `gemini-3-pro-preview`

- 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-3.1-flash-image-preview` for Nano Banana 2 image generation and editing

- Use `gemini-live-2.5-flash-native-audio` for Live Realtime API including native audio

Use the following models only if explicitly requested:

- `gemini-2.5-flash-image`

- `gemini-2.5-flash`

- `gemini-2.5-flash-lite`

- `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 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 Agent Platform, refer to the official Agent Platform documentation:

- **Agent Platform Documentation**: [https://docs.cloud.google.com/gemini-enterprise-agent-platform/overview](https://docs.cloud.google.com/gemini-enterprise-agent-platform/overview)

- **REST API Reference**: [https://docs.cloud.google.com/gemini-enterprise-agent-platform/reference/rest](https://docs.cloud.google.com/gemini-enterprise-agent-platform/reference/rest)

The Gen AI SDK on Agent Platform 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 Agent Platform 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](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/main/genai) 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](https://github.com/google/skills/blob/HEAD/skills/cloud/gemini-api/references/text_and_multimodal.md)

- **Embeddings**: Generate text embeddings for semantic search. See [references/embeddings.md](https://github.com/google/skills/blob/HEAD/skills/cloud/gemini-api/references/embeddings.md)

- **Structured Output & Tools**: JSON generation, Function Calling, Search Grounding, and Code Execution. See [references/structured_and_tools.md](https://github.com/google/skills/blob/HEAD/skills/cloud/gemini-api/references/structured_and_tools.md)

- **Media Generation**: Image generation, Image editing, and Video generation. See [references/media_generation.md](https://github.com/google/skills/blob/HEAD/skills/cloud/gemini-api/references/media_generation.md)

- **Bounding Box Detection**: Object detection and localization within images and video. See [references/bounding_box.md](https://github.com/google/skills/blob/HEAD/skills/cloud/gemini-api/references/bounding_box.md)

- **Live API**: Real-time bidirectional streaming for voice, vision, and text. See [references/live_api.md](https://github.com/google/skills/blob/HEAD/skills/cloud/gemini-api/references/live_api.md)

- **Advanced Features**: Content Caching, Batch Prediction, and Thinking/Reasoning. See [references/advanced_features.md](https://github.com/google/skills/blob/HEAD/skills/cloud/gemini-api/references/advanced_features.md)

- **Safety**: Adjusting Responsible AI filters and thresholds. See [references/safety.md](https://github.com/google/skills/blob/HEAD/skills/cloud/gemini-api/references/safety.md)

- **Model Tuning**: Supervised Fine-Tuning and Preference Tuning. See [references/model_tuning.md](https://github.com/google/skills/blob/HEAD/skills/cloud/gemini-api/references/model_tuning.md)

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---
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