Chat Completions - MountSea API

聊天完成 (OpenAI 兼容)

curl --request POST \
  --url https://api.mountsea.ai/chat/chat/completions \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "gpt-5.1",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "Hello!"
    }
  ],
  "stream": false,
  "temperature": 1,
  "max_tokens": 1000,
  "max_completion_tokens": 1000,
  "top_p": 1,
  "frequency_penalty": 0,
  "presence_penalty": 0,
  "stop": {},
  "n": 1,
  "seed": 12345,
  "user": "<string>",
  "tools": [
    {}
  ],
  "tool_choice": {},
  "response_format": {},
  "logprobs": false,
  "top_logprobs": 5,
  "logit_bias": {},
  "parallel_tool_calls": true
}
'

POST

chat

completions

聊天完成 (OpenAI 兼容)

curl --request POST \
  --url https://api.mountsea.ai/chat/chat/completions \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "gpt-5.1",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "Hello!"
    }
  ],
  "stream": false,
  "temperature": 1,
  "max_tokens": 1000,
  "max_completion_tokens": 1000,
  "top_p": 1,
  "frequency_penalty": 0,
  "presence_penalty": 0,
  "stop": {},
  "n": 1,
  "seed": 12345,
  "user": "<string>",
  "tools": [
    {}
  ],
  "tool_choice": {},
  "response_format": {},
  "logprobs": false,
  "top_logprobs": 5,
  "logit_bias": {},
  "parallel_tool_calls": true
}
'

💡 Quick Examples

Python
Node.js
Java
Go

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://api.mountsea.ai/chat"
)

# Basic chat
response = client.chat.completions.create(
    model="gpt-5.1",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

Streaming:

stream = client.chat.completions.create(
    model="gpt-5.1",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

import OpenAI from 'openai';

const client = new OpenAI({
    apiKey: 'your-api-key',
    baseURL: 'https://api.mountsea.ai/chat'
});

// Basic chat
const response = await client.chat.completions.create({
    model: 'gpt-5.1',
    messages: [
        { role: 'system', content: 'You are a helpful assistant.' },
        { role: 'user', content: 'Hello!' }
    ]
});

console.log(response.choices[0].message.content);

Streaming:

const stream = await client.chat.completions.create({
    model: 'gpt-5.1',
    messages: [{ role: 'user', content: 'Tell me a story' }],
    stream: true
});

for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.*;

// Initialize client
OpenAIClient client = OpenAIOkHttpClient.builder()
    .apiKey("your-api-key")
    .baseUrl("https://api.mountsea.ai/chat")
    .build();

// Basic chat
ChatCompletionCreateParams params = ChatCompletionCreateParams.builder()
    .model("gpt-5.1")
    .addMessage(ChatCompletionMessageParam.ofSystem(
        SystemMessage.builder().content("You are a helpful assistant.").build()))
    .addMessage(ChatCompletionMessageParam.ofUser(
        UserMessage.builder().content("Hello!").build()))
    .build();

ChatCompletion response = client.chat().completions().create(params);
System.out.println(response.choices().get(0).message().content());

Streaming:

ChatCompletionCreateParams streamParams = ChatCompletionCreateParams.builder()
    .model("gpt-5.1")
    .addMessage(ChatCompletionMessageParam.ofUser(
        UserMessage.builder().content("Tell me a story").build()))
    .build();

client.chat().completions().createStreaming(streamParams)
    .forEach(chunk -> {
        String content = chunk.choices().get(0).delta().content().orElse("");
        System.out.print(content);
    });

Maven dependency:

<dependency>
    <groupId>com.openai</groupId>
    <artifactId>openai-java</artifactId>
    <version>0.8.0</version>
</dependency>

package main

import (
    "context"
    "fmt"
    "github.com/openai/openai-go"
    "github.com/openai/openai-go/option"
)

func main() {
    // Initialize client
    client := openai.NewClient(
        option.WithAPIKey("your-api-key"),
        option.WithBaseURL("https://api.mountsea.ai/chat"),
    )

    // Basic chat
    response, _ := client.Chat.Completions.New(context.TODO(),
        openai.ChatCompletionNewParams{
            Model: openai.String("gpt-5.1"),
            Messages: []openai.ChatCompletionMessageParamUnion{
                openai.SystemMessage("You are a helpful assistant."),
                openai.UserMessage("Hello!"),
            },
        },
    )

    fmt.Println(response.Choices[0].Message.Content)
}

Streaming:

stream := client.Chat.Completions.NewStreaming(context.TODO(),
    openai.ChatCompletionNewParams{
        Model: openai.String("gpt-5.1"),
        Messages: []openai.ChatCompletionMessageParamUnion{
            openai.UserMessage("Tell me a story"),
        },
    },
)

for stream.Next() {
    chunk := stream.Current()
    fmt.Print(chunk.Choices[0].Delta.Content)
}

Install:

go get github.com/openai/openai-go

📤 Response Format

Non-streaming Response

{
  "id": "chatcmpl-xxx",
  "object": "chat.completion",
  "created": 1234567890,
  "model": "gpt-5.1",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! How can I help you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 26,
    "completion_tokens": 10,
    "total_tokens": 36
  }
}

Streaming Response (SSE)

data: {"id":"chatcmpl-xxx","choices":[{"delta":{"role":"assistant"}}]}

data: {"id":"chatcmpl-xxx","choices":[{"delta":{"content":"Hello"}}]}

data: {"id":"chatcmpl-xxx","choices":[{"delta":{"content":"!"}}]}

data: {"id":"chatcmpl-xxx","choices":[{"delta":{},"finish_reason":"stop"}]}

data: [DONE]

🔧 API Reference

The interactive API form below is auto-generated from OpenAPI spec. All available models are shown in the model dropdown.

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json

model

enum<string>

required

模型名称

Available options:

gpt-5.1,

gpt-5.1-all,

gpt-5.1-thinking,

gpt-5.1-thinking-all,

gpt-5.2,

gpt-5.3,

gpt-5.3-codex,

gpt-5.4,

gemini-3-pro,

gemini-2.5-pro,

gemini-2.5-flash,

gemini-3-flash,

gemini-3.1-pro,

claude-4.5,

claude-opus-4-6,

claude-haiku-4-5-20251001,

claude-sonnet-4-6,

claude-opus-4-7

Example:

"gpt-5.1"

messages

object[]

required

消息列表

Example:

[
  {
    "role": "system",
    "content": "You are a helpful assistant."
  },
  { "role": "user", "content": "Hello!" }
]

stream

boolean

是否流式输出

Example:

false

temperature

number | null

温度 (0-2)

Example:

1

max_tokens

number | null

最大 token 数

Example:

1000

max_completion_tokens

number | null

最大补全 token 数 (新版参数)

Example:

1000

top_p

number | null

Top P

Example:

1

frequency_penalty

number | null

频率惩罚 (-2.0 到 2.0)

Example:

0

presence_penalty

number | null

存在惩罚 (-2.0 到 2.0)

Example:

0

stop

object

停止序列

number | null

生成数量

Example:

1

seed

number | null

随机种子

Example:

12345

user

string

用户标识

tools

object[]

工具列表 (Function Calling)

tool_choice

object

工具选择策略

response_format

object

响应格式

logprobs

boolean | null

是否返回 logprobs

Example:

false

top_logprobs

number | null

top logprobs 数量

Example:

5

logit_bias

object

logit 偏置

parallel_tool_calls

boolean

并行工具调用

Example:

true

Response

200

成功

Introduction Responses API

​💡 Quick Examples

​📤 Response Format

​Non-streaming Response

​Streaming Response (SSE)

​🔧 API Reference

Authorizations

Body

Response

💡 Quick Examples

📤 Response Format

Non-streaming Response

Streaming Response (SSE)

🔧 API Reference