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聊天 ​

对话补全接口，最常用的 AI 对话接口，兼容 OpenAI Chat Completions API。

创建聊天完成 POST ​

POST https://callxyq.xyz/v1/chat/completions

Authorization ​

参数位置说明
AuthorizationHeaderBearer sk-xxxx
Content-TypeHeaderapplication/json

请求参数 ​

参数类型必填默认值说明
modelstring✅-模型名称，如 claude-sonnet-4-20250514
messagesarray✅-对话消息列表
temperaturenumber否1.0采样温度，0~2
top_pnumber否1.0核采样参数，0~1
max_tokensinteger否4096最大生成 token 数
streamboolean否false是否流式输出
presence_penaltynumber否0存在惩罚，-2.0~2.0
frequency_penaltynumber否0频率惩罚，-2.0~2.0
stoparray/string否null停止序列
toolsarray否null工具/函数调用定义
tool_choicestring/object否"auto"工具选择策略

messages 结构 ​

支持的 role 类型：

  • system — 系统提示，设定 AI 角色和行为
  • user — 用户输入
  • assistant — 模型回复（多轮对话上下文）
  • tool — 工具调用返回结果

多模态内容（部分模型支持）：

json
[
  {"type": "text", "text": "请描述这张图片"},
  {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
]

示例请求 ​

cURL JavaScript Python Go Java C#

bash
curl -X POST "https://callxyq.xyz/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxx" \
  -d '{
    "model": "claude-sonnet-4-20250514",
    "messages": [
      {"role": "system", "content": "你是一个有帮助的AI助手。"},
      {"role": "user", "content": "你好，请介绍一下自己。"}
    ],
    "temperature": 0.7,
    "max_tokens": 1024,
    "stream": false
  }'
javascript
import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'sk-xxxx',
  baseURL: 'https://callxyq.xyz/v1',
});

const response = await client.chat.completions.create({
  model: 'claude-sonnet-4-20250514',
  messages: [
    { role: 'system', content: '你是一个有帮助的AI助手。' },
    { role: 'user', content: '你好，请介绍一下自己。' },
  ],
  temperature: 0.7,
  max_tokens: 1024,
});

console.log(response.choices[0].message.content);
python
from openai import OpenAI

client = OpenAI(
    api_key="sk-xxxx",
    base_url="https://callxyq.xyz/v1"
)

response = client.chat.completions.create(
    model="claude-sonnet-4-20250514",
    messages=[
        {"role": "system", "content": "你是一个有帮助的AI助手。"},
        {"role": "user", "content": "你好，请介绍一下自己。"}
    ],
    temperature=0.7,
    max_tokens=1024
)

print(response.choices[0].message.content)
go
package main

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

func main() {
    config := openai.DefaultConfig("sk-xxxx")
    config.BaseURL = "https://callxyq.xyz/v1"
    client := openai.NewClientWithConfig(config)

    resp, _ := client.CreateChatCompletion(
        context.Background(),
        openai.ChatCompletionRequest{
            Model: "claude-sonnet-4-20250514",
            Messages: []openai.ChatCompletionMessage{
                {Role: "system", Content: "你是一个有帮助的AI助手。"},
                {Role: "user", Content: "你好，请介绍一下自己。"},
            },
            Temperature: 0.7,
            MaxTokens:   1024,
        },
    )
    fmt.Println(resp.Choices[0].Message.Content)
}
java
import com.theokanning.openai.completion.chat.*;
import com.theokanning.openai.service.OpenAiService;
import java.util.List;

OpenAiService service = new OpenAiService("sk-xxxx",
    "https://callxyq.xyz/v1");

ChatCompletionRequest request = ChatCompletionRequest.builder()
    .model("claude-sonnet-4-20250514")
    .messages(List.of(
        new ChatMessage("system", "你是一个有帮助的AI助手。"),
        new ChatMessage("user", "你好，请介绍一下自己。")
    ))
    .temperature(0.7)
    .maxTokens(1024)
    .build();

ChatCompletionResult result = service.createChatCompletion(request);
System.out.println(result.getChoices().get(0).getMessage().getContent());
csharp
using OpenAI;
using OpenAI.Chat;

var client = new ChatClient(
    model: "claude-sonnet-4-20250514",
    apiKey: "sk-xxxx",
    options: new OpenAIClientOptions {
        Endpoint = new Uri("https://callxyq.xyz/v1")
    });

var response = await client.CompleteChatAsync(new List<ChatMessage>
{
    new SystemChatMessage("你是一个有帮助的AI助手。"),
    new UserChatMessage("你好，请介绍一下自己。")
});

Console.WriteLine(response.Value.Content[0].Text);

响应（非流式） ​

200 成功 401 未授权

json
{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1677858242,
  "model": "claude-sonnet-4-20250514",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "你好！我是一个AI助手，很高兴为你服务。"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 28,
    "completion_tokens": 18,
    "total_tokens": 46
  }
}
json
{
  "error": {
    "message": "Invalid API key",
    "type": "authentication_error",
    "code": "invalid_api_key"
  }
}

流式响应（stream=true） ​

采用 Server-Sent Events (SSE) 格式：

data: {"id":"chatcmpl-123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"role":"assistant","content":"你"},"finish_reason":null}]}

data: {"id":"chatcmpl-123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"好"},"finish_reason":null}]}

data: [DONE]

对话补全接口，兼容 OpenAI Chat Completions API。

创建聊天完成 POST ​

http
POST https://callxyq.xyz/v1/chat/completions

Authorization ​

  • Authorization: Bearer sk-xxxx
  • Content-Type: application/json

请求参数 ​

参数类型必填默认值说明
modelstring✅-模型名，如 claude-sonnet-4-20250514
messagesarray✅-对话消息列表
temperaturenumber否1.0采样温度（0~2）
top_pnumber否1.0核采样参数（0~1）
max_tokensinteger否4096最大生成 token 数
streamboolean否false是否流式输出
presence_penaltynumber否0存在惩罚（-2~2）
frequency_penaltynumber否0频率惩罚（-2~2）
stoparray/string否null停止序列
toolsarray否null工具/函数调用定义
tool_choicestring/object否auto工具选择策略

messages 结构 ​

支持 role：

  • system
  • user
  • assistant
  • tool

多模态（部分模型支持）示例：

json
[
  {"type": "text", "text": "请描述这张图片"},
  {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
]

示例请求 ​

bash
curl -X POST "https://callxyq.xyz/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxx" \
  -d '{
    "model": "claude-sonnet-4-20250514",
    "messages": [
      {"role": "system", "content": "你是一个有帮助的AI助手。"},
      {"role": "user", "content": "你好，请介绍一下自己。"}
    ],
    "temperature": 0.7,
    "max_tokens": 1024,
    "stream": false
  }'
javascript
import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'sk-xxxx',
  baseURL: 'https://callxyq.xyz/v1',
});

const response = await client.chat.completions.create({
  model: 'claude-sonnet-4-20250514',
  messages: [
    { role: 'system', content: '你是一个有帮助的AI助手。' },
    { role: 'user', content: '你好，请介绍一下自己。' },
  ],
  temperature: 0.7,
  max_tokens: 1024,
});

console.log(response.choices[0].message.content);
python
from openai import OpenAI

client = OpenAI(
    api_key="sk-xxxx",
    base_url="https://callxyq.xyz/v1"
)

response = client.chat.completions.create(
    model="claude-sonnet-4-20250514",
    messages=[
        {"role": "system", "content": "你是一个有帮助的AI助手。"},
        {"role": "user", "content": "你好，请介绍一下自己。"}
    ],
    temperature=0.7,
    max_tokens=1024
)

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

响应（非流式） ​

json
{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1677858242,
  "model": "claude-sonnet-4-20250514",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "你好！我是一个AI助手，很高兴为你服务。"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 28,
    "completion_tokens": 18,
    "total_tokens": 46
  }
}

错误示例：

json
{
  "error": {
    "message": "Invalid API key",
    "type": "authentication_error",
    "code": "invalid_api_key"
  }
}

流式响应（stream=true） ​

SSE 返回示例：

text
data: {"id":"chatcmpl-123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"role":"assistant","content":"你"},"finish_reason":null}]}

data: {"id":"chatcmpl-123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"好"},"finish_reason":null}]}

data: [DONE]

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