Documentation Index
Fetch the complete documentation index at: https://docs.mountsea.ai/llms.txt
Use this file to discover all available pages before exploring further.
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） |
💡 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);
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 || '');
}
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)
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="")
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)
}
}
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)
}
}
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。