Gemini 原生 API

我们的 Chat 服务同样支持原生 Google Gemini API 格式，允许您直接使用官方 Google GenAI 库。

此 API 专为偏好使用 Google 原生 SDK 而非 OpenAI 兼容格式的开发者设计。

🌟 核心特性

✅ 直接使用官方 Google GenAI SDK
✅ 完全兼容 Gemini API 格式
✅ 支持流式和非流式响应
✅ 通过我们的服务访问 Gemini 模型

📋 可用端点

端点	方法	描述
`/chat/gemini/{apiVersion}/models/{model}:generateContent`	POST	生成内容（非流式）
`/chat/gemini/{apiVersion}/models/{model}:streamGenerateContent`	POST	生成内容（流式 SSE）

Gemini 模型

我们会支持最新的 Gemini 模型。这一页只列 Gemini。快照可能不全，当前已启用的 Gemini 请调用 GET /chat/gemini/v1beta/models。其他厂家在列出模型里各自成组。

模型 ID
`gemini-2.5-flash`
`gemini-3.1-pro`
`gemini-3-pro`
`gemini-3-flash`
`gemini-2.5-pro`
`gemini-3.8-flash`
`gemini-3.7-flash`
`gemini-3.6-flash`
`gemini-3.5-flash`
`gemini-3.1-flash-lite`
`gemini-2.5-flash-lite`
`gemini-3.1-pro-preview`
`gemini-3-flash-preview`

💡 快速示例

Node.js
Python
Go
cURL

import { GoogleGenAI } from '@google/genai';

const API_KEY = 'your-api-key';
const API_VERSION = 'v1beta';
const BASE_URL = 'https://api.mountsea.ai/chat/gemini';

const client = new GoogleGenAI({
  apiKey: API_KEY,
  apiVersion: API_VERSION,
  httpOptions: {
    baseUrl: BASE_URL,
    headers: {
      "Authorization": `Bearer ${API_KEY}`,
    },
  },
});

// Generate content (non-streaming)
const response = await client.models.generateContent({
  model: 'gemini-3-flash',
  contents: [
    {
      role: 'user',
      parts: [{ text: 'Hello! Tell me a joke.' }]
    }
  ],
  config: {
    temperature: 1,
    maxOutputTokens: 1024,
  }
});

console.log(response.text);

流式响应：

const stream = await client.models.streamGenerateContent({
  model: 'gemini-3-flash',
  contents: [
    {
      role: 'user',
      parts: [{ text: 'Write a short story about a robot.' }]
    }
  ],
  config: {
    temperature: 0.8,
    maxOutputTokens: 2048,
  }
});

for await (const chunk of stream) {
  process.stdout.write(chunk.text || '');
}

安装：

npm install @google/genai

from google import genai

API_KEY = "your-api-key"
API_VERSION = "v1beta"
BASE_URL = "https://api.mountsea.ai/chat/gemini"

client = genai.Client(
    api_key=API_KEY,
    http_options={
        "base_url": BASE_URL,
        "headers": {
            "Authorization": f"Bearer {API_KEY}",
        },
    },
)

# Generate content (non-streaming)
response = client.models.generate_content(
    model="gemini-3-flash",
    contents=[
        {
            "role": "user",
            "parts": [{"text": "Hello! Tell me a joke."}]
        }
    ],
    config={
        "temperature": 1,
        "max_output_tokens": 1024,
    }
)

print(response.text)

流式响应：

stream = client.models.generate_content_stream(
    model="gemini-3-flash",
    contents=[
        {
            "role": "user",
            "parts": [{"text": "Write a short story about a robot."}]
        }
    ],
    config={
        "temperature": 0.8,
        "max_output_tokens": 2048,
    }
)

for chunk in stream:
    print(chunk.text, end="")

安装：

pip install google-genai

package main

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

func main() {
    ctx := context.Background()
    
    apiKey := "your-api-key"
    baseURL := "https://api.mountsea.ai/chat/gemini"
    
    client, _ := genai.NewClient(ctx, &genai.ClientConfig{
        APIKey:  apiKey,
        BaseURL: baseURL,
        Headers: map[string]string{
            "Authorization": "Bearer " + apiKey,
        },
    })
    defer client.Close()

    // Generate content (non-streaming)
    model := client.GenerativeModel("gemini-3-flash")
    model.Temperature = genai.Ptr(float32(1.0))
    model.MaxOutputTokens = genai.Ptr(int32(1024))

    resp, _ := model.GenerateContent(ctx, genai.Text("Hello! Tell me a joke."))
    
    for _, part := range resp.Candidates[0].Content.Parts {
        fmt.Println(part)
    }
}

流式响应：

iter := model.GenerateContentStream(ctx, genai.Text("Write a short story about a robot."))
for {
    resp, err := iter.Next()
    if err == iterator.Done {
        break
    }
    for _, part := range resp.Candidates[0].Content.Parts {
        fmt.Print(part)
    }
}

安装：

go get google.golang.org/genai

# Non-streaming
curl -X POST "https://api.mountsea.ai/chat/gemini/v1beta/models/gemini-3-flash:generateContent?key=your-api-key" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-api-key" \
  -d '{
    "contents": [
      {
        "role": "user",
        "parts": [{"text": "Hello! Tell me a joke."}]
      }
    ],
    "generationConfig": {
      "temperature": 1,
      "maxOutputTokens": 1024
    }
  }'

# Streaming (SSE)
curl -X POST "https://api.mountsea.ai/chat/gemini/v1beta/models/gemini-3-flash:streamGenerateContent?alt=sse&key=your-api-key" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-api-key" \
  -d '{
    "contents": [
      {
        "role": "user",
        "parts": [{"text": "Write a story"}]
      }
    ]
  }'

📤 响应格式

非流式响应

{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "text": "Why don't scientists trust atoms? Because they make up everything!"
          }
        ],
        "role": "model"
      },
      "finishReason": "STOP",
      "safetyRatings": [...]
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 10,
    "candidatesTokenCount": 15,
    "totalTokenCount": 25
  }
}

流式响应 (SSE)

data: {"candidates":[{"content":{"parts":[{"text":"Why"}],"role":"model"}}]}

data: {"candidates":[{"content":{"parts":[{"text":" don't"}],"role":"model"}}]}

data: {"candidates":[{"content":{"parts":[{"text":" scientists"}],"role":"model"}}]}

...

🔧 配置选项

config / generationConfig 对象支持以下参数：

参数	类型	描述
`temperature`	number	控制随机性（0-2）
`maxOutputTokens`	number	响应的最大 token 数
`topP`	number	Nucleus 采样参数
`topK`	number	Top-K 采样参数
`systemInstruction`	object	系统提示配置
`tools`	array	函数调用工具

使用系统指令

Node.js
Python

const response = await client.models.generateContent({
  model: 'gemini-3-flash',
  contents: [
    { role: 'user', parts: [{ text: 'What is the capital of France?' }] }
  ],
  config: {
    temperature: 0.7,
    maxOutputTokens: 1024,
    systemInstruction: {
      role: 'user',
      parts: [{ text: 'You are a helpful geography teacher. Answer concisely.' }]
    }
  }
});

response = client.models.generate_content(
    model="gemini-3-flash",
    contents=[
        {"role": "user", "parts": [{"text": "What is the capital of France?"}]}
    ],
    config={
        "temperature": 0.7,
        "max_output_tokens": 1024,
        "system_instruction": {
            "role": "user",
            "parts": [{"text": "You are a helpful geography teacher. Answer concisely."}]
        }
    }
)

📝 多轮对话

{
  "contents": [
    {
      "role": "user",
      "parts": [{ "text": "Hi, my name is Alice." }]
    },
    {
      "role": "model",
      "parts": [{ "text": "Hello Alice! Nice to meet you." }]
    },
    {
      "role": "user",
      "parts": [{ "text": "What's my name?" }]
    }
  ]
}

如需使用 OpenAI 兼容 API，请参阅 Chat Completions。

​Gemini 原生 API

​🌟 核心特性

​📋 可用端点

​Gemini 模型

​💡 快速示例

​📤 响应格式

​非流式响应

​流式响应 (SSE)

​🔧 配置选项

​使用系统指令

​📝 多轮对话

Gemini 原生 API

🌟 核心特性

📋 可用端点

Gemini 模型

💡 快速示例

📤 响应格式

非流式响应

流式响应 (SSE)

🔧 配置选项

使用系统指令

📝 多轮对话