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OpenAI格式API

Constreet提供完全兼容OpenAI的API接口，支持所有GPT系列模型以及其他兼容OpenAI格式的模型。

接口地址​

https://api.constreet.cc/v1

认证方式​

在请求头中添加API密钥：

Authorization: Bearer YOUR_API_KEY

Chat Completions​

接口说明​

用于文本对话的核心接口，支持单轮和多轮对话。

请求地址​

POST /v1/chat/completions

重要说明​

GPT模型流式输出要求：

  • 所有GPT系列模型必须使用流式输出，因此最好直接用于Codex中
  • 请求中必须设置 "stream": true
  • 非流式请求将返回错误

其他模型（Claude、Gemini等）支持流式和非流式两种模式。

请求示例​

GPT模型（必须流式）​

curl https://api.constreet.cc/v1/chat/completions \  -H "Content-Type: application/json" \  -H "Authorization: Bearer YOUR_API_KEY" \  -d '{    "model": "gpt-5.2",    "messages": [      {        "role": "system",        "content": "你是一个有帮助的AI助手"      },      {        "role": "user",        "content": "用Python写一个快速排序"      }    ],    "stream": true,    "temperature": 0.7,    "max_tokens": 2000  }'

其他模型（可选流式）​

curl https://api.constreet.cc/v1/chat/completions \  -H "Content-Type: application/json" \  -H "Authorization: Bearer YOUR_API_KEY" \  -d '{    "model": "claude-sonnet-4-6",    "messages": [      {        "role": "user",        "content": "Hello!"      }    ],    "stream": false,    "temperature": 0.7  }'

请求参数​

Messages格式​

{  "messages": [    {      "role": "system",      "content": "系统提示词"    },    {      "role": "user",      "content": "用户消息"    },    {      "role": "assistant",      "content": "助手回复"    }  ]}

角色说明：

  • system - 系统提示词，设定AI的行为
  • user - 用户消息
  • assistant - AI的回复（用于多轮对话）

响应格式​

{  "id": "chatcmpl-123",  "object": "chat.completion",  "created": 1677652288,  "model": "gpt-5.2",  "choices": [    {      "index": 0,      "message": {        "role": "assistant",        "content": "这是AI的回复内容"      },      "finish_reason": "stop"    }  ],  "usage": {    "prompt_tokens": 56,    "completion_tokens": 31,    "total_tokens": 87  }}

流式输出​

GPT模型必须使用流式输出，其他模型可选。

GPT模型流式请求​

curl https://api.constreet.cc/v1/chat/completions \  -H "Content-Type: application/json" \  -H "Authorization: Bearer YOUR_API_KEY" \  -d '{    "model": "gpt-5.2",    "messages": [{"role": "user", "content": "你好"}],    "stream": true,    "stream_options": {      "include_usage": true    }  }'

流式响应格式（SSE）​

data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1677652288,"model":"gpt-5.2","choices":[{"index":0,"delta":{"role":"assistant","content":""},"finish_reason":null}]}data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1677652288,"model":"gpt-5.2","choices":[{"index":0,"delta":{"content":"你"},"finish_reason":null}]}data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1677652288,"model":"gpt-5.2","choices":[{"index":0,"delta":{"content":"好"},"finish_reason":null}]}data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1677652288,"model":"gpt-5.2","choices":[{"index":0,"delta":{},"finish_reason":"stop"}],"usage":{"prompt_tokens":9,"completion_tokens":2,"total_tokens":11}}data: [DONE]

非GPT模型（可选流式）​

curl https://api.constreet.cc/v1/chat/completions \  -H "Content-Type: application/json" \  -H "Authorization: Bearer YOUR_API_KEY" \  -d '{    "model": "claude-sonnet-4-6",    "messages": [{"role": "user", "content": "你好"}],    "stream": false  }'

支持的模型​

GPT系列（必须流式输出）​

Claude系列（OpenAI格式，可选流式）​

Gemini系列（OpenAI格式，可选流式）​

代码示例​

Python（GPT模型流式）​

from openai import OpenAIclient = OpenAI(    api_key="YOUR_API_KEY",    base_url="https://api.constreet.cc/v1")# GPT模型必须使用流式输出stream = client.chat.completions.create(    model="gpt-5.2",    messages=[        {"role": "system", "content": "你是一个编程助手"},        {"role": "user", "content": "写一个Python函数计算斐波那契数列"}    ],    stream=True,    stream_options={"include_usage": True})for chunk in stream:    if chunk.choices[0].delta.content is not None:        print(chunk.choices[0].delta.content, end="")        # 获取使用统计（最后一个chunk）    if hasattr(chunk, 'usage') and chunk.usage:        print(f"\n\n使用了 {chunk.usage.total_tokens} tokens")

Python（非GPT模型，可选流式）​

# 非流式请求（仅非GPT模型支持）response = client.chat.completions.create(    model="claude-sonnet-4-6",    messages=[        {"role": "user", "content": "写一个快速排序算法"}    ],    stream=False)print(response.choices[0].message.content)

Node.js（GPT模型流式）​

import OpenAI from 'openai';const client = new OpenAI({  apiKey: 'YOUR_API_KEY',  baseURL: 'https://api.constreet.cc/v1'});async function main() {  // GPT模型必须使用流式输出  const stream = await client.chat.completions.create({    model: 'gpt-5.2',    messages: [      { role: 'system', content: '你是一个编程助手' },      { role: 'user', content: '写一个快速排序算法' }    ],    stream: true,    stream_options: { include_usage: true }  });  for await (const chunk of stream) {    if (chunk.choices[0]?.delta?.content) {      process.stdout.write(chunk.choices[0].delta.content);    }        // 获取使用统计    if (chunk.usage) {      console.log(`\n\n使用了 ${chunk.usage.total_tokens} tokens`);    }  }}main();

Node.js（非GPT模型）​

// 非GPT模型可以使用非流式async function callClaude() {  const completion = await client.chat.completions.create({    model: 'claude-sonnet-4-6',    messages: [      { role: 'user', content: '写一个快速排序算法' }    ],    stream: false  });  console.log(completion.choices[0].message.content);}

cURL（GPT模型）​

# GPT模型必须流式curl https://api.constreet.cc/v1/chat/completions \  -H "Content-Type: application/json" \  -H "Authorization: Bearer YOUR_API_KEY" \  -d '{    "model": "gpt-5.2",    "messages": [      {"role": "user", "content": "Hello!"}    ],    "stream": true,    "stream_options": {      "include_usage": true    }  }'

cURL（非GPT模型）​

# 非GPT模型可以非流式curl https://api.constreet.cc/v1/chat/completions \  -H "Content-Type: application/json" \  -H "Authorization: Bearer YOUR_API_KEY" \  -d '{    "model": "claude-sonnet-4-6",    "messages": [      {"role": "user", "content": "Hello!"}    ],    "stream": false  }'

错误处理​

错误响应格式​

{  "error": {    "message": "错误描述",    "type": "invalid_request_error",    "code": "invalid_api_key"  }}

常见错误码​

错误处理示例​

from openai import OpenAI, APIErrorclient = OpenAI(    api_key="YOUR_API_KEY",    base_url="https://api.constreet.cc/v1")try:    # GPT模型必须流式    stream = client.chat.completions.create(        model="gpt-5.2",        messages=[{"role": "user", "content": "Hello"}],        stream=True    )        for chunk in stream:        if chunk.choices[0].delta.content:            print(chunk.choices[0].delta.content, end="")            except APIError as e:    print(f"API错误: {e.message}")    print(f"错误类型: {e.type}")    print(f"状态码: {e.status_code}")

最佳实践​

设置合理的超时​

client = OpenAI(    api_key="YOUR_API_KEY",    base_url="https://api.constreet.cc/v1",    timeout=30.0  # 30秒超时)

实现重试机制​

from tenacity import retry, stop_after_attempt, wait_exponential@retry(    stop=stop_after_attempt(3),    wait=wait_exponential(multiplier=1, min=4, max=10))def call_api():    return client.chat.completions.create(...)

使用流式输出（GPT模型必须）​

GPT模型必须使用流式输出，其他模型可选：

# GPT模型（必须流式）stream = client.chat.completions.create(    model="gpt-5.2",    messages=[{"role": "user", "content": "写一篇文章"}],    stream=True,    stream_options={"include_usage": True})for chunk in stream:    if chunk.choices[0].delta.content:        print(chunk.choices[0].delta.content, end='')# 非GPT模型（可选流式）response = client.chat.completions.create(    model="claude-sonnet-4-6",    messages=[{"role": "user", "content": "写一篇文章"}],    stream=False  # 可以设为False)print(response.choices[0].message.content)

控制Token消耗​

response = client.chat.completions.create(    model="gpt-5.2",    messages=[...],    max_tokens=500,  # 限制输出长度    temperature=0.3  # 降低随机性)# 查看token使用print(f"使用了 {response.usage.total_tokens} tokens")

速率限制​

Constreet实施以下速率限制：

超过限制会返回429错误，建议实现指数退避重试。

下一步​