OpenAI Responses接口兼容

Python

import os
from openai import OpenAI

client = OpenAI(
    # 若没有配置环境变量，请用百炼 API Key 将下行替换为：api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1",
)

response = client.responses.create(
    model="qwen3-max-2026-01-23",
    input="你好，请用一句话介绍你自己。"
)

# 获取模型回复
# print(response.model_dump_json())
print(response.output_text)

Node.js

import OpenAI from "openai";

const openai = new OpenAI({
    // 若没有配置环境变量，请用百炼 API Key 将下行替换为：apiKey: "sk-xxx"
    apiKey: process.env.DASHSCOPE_API_KEY,
    baseURL: "https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1"
});

async function main() {
    const response = await openai.responses.create({
        model: "qwen3-max-2026-01-23",
        input: "你好，请用一句话介绍你自己。"
    });

    // 获取模型回复
    console.log(response.output_text);
}

main();

curl

curl -X POST https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3-max-2026-01-23",
    "input": "你好，请用一句话介绍你自己。"
}'

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1",
)

# 第一轮对话
response1 = client.responses.create(
    model="qwen3-max-2026-01-23",
    input="我的名字是张三，请记住。"
)
print(f"第一轮回复: {response1.output_text}")

# 第二轮对话 - 使用 previous_response_id 关联上下文，响应id有效期为7天
response2 = client.responses.create(
    model="qwen3-max-2026-01-23",
    input="你还记得我的名字吗？",
    previous_response_id=response1.id
)
print(f"第二轮回复: {response2.output_text}")

Node.js

import OpenAI from "openai";

const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY,
    baseURL: "https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1"
});

async function main() {
    // 第一轮对话
    const response1 = await openai.responses.create({
        model: "qwen3-max-2026-01-23",
        input: "我的名字是张三，请记住。"
    });
    console.log(`第一轮回复: ${response1.output_text}`);

    // 第二轮对话 - 使用 previous_response_id 关联上下文，响应id有效期为7天
    const response2 = await openai.responses.create({
        model: "qwen3-max-2026-01-23",
        input: "你还记得我的名字吗？",
        previous_response_id: response1.id
    });
    console.log(`第二轮回复: ${response2.output_text}`);
}

main();

curl

# 第一轮对话
curl -X POST https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3-max-2026-01-23",
    "input": "我的名字是张三，请记住。"
}'

# 第二轮对话 - 使用上一轮返回的 id 作为 previous_response_id
curl -X POST https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3-max-2026-01-23",
    "input": "你还记得我的名字吗？",
    "previous_response_id": "第一轮返回的响应id"
}'

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1",
)

stream = client.responses.create(
    model="qwen3-max-2026-01-23",
    input="请简单介绍一下人工智能。",
    stream=True
)

print("开始接收流式输出:")
for event in stream:
    # print(event.model_dump_json())  # 取消注释以查看原始事件响应
    if event.type == 'response.output_text.delta':
        # 实时打印增量文本
        print(event.delta, end='', flush=True)
    elif event.type == 'response.completed':
        print("\n流式输出完成")
        print(f"总 Token 数: {event.response.usage.total_tokens}")

Node.js

import OpenAI from "openai";

const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY,
    baseURL: "https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1"
});

async function main() {
    const stream = await openai.responses.create({
        model: "qwen3-max-2026-01-23",
        input: "请简单介绍一下人工智能。",
        stream: true
    });

    console.log("开始接收流式输出:");
    for await (const event of stream) {
        // console.log(JSON.stringify(event));  // 取消注释以查看原始事件响应
        if (event.type === 'response.output_text.delta') {
            process.stdout.write(event.delta);
        } else if (event.type === 'response.completed') {
            console.log("\n流式输出完成");
            console.log(`总 Token 数: ${event.response.usage.total_tokens}`);
        }
    }
}

main();

curl

curl -X POST https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3-max-2026-01-23",
    "input": "请简单介绍一下人工智能。",
    "stream": true
}'

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1",
)

response = client.responses.create(
    model="qwen3-max-2026-01-23",
    input="帮我找一下阿里云官网，并提取首页的关键信息",
    # 建议同时开启内置工具以取得最佳效果
    tools=[
        {"type": "web_search"},
        {"type": "code_interpreter"},
        {"type": "web_extractor"}
    ],
    extra_body={"enable_thinking": True}
)

# 取消以下注释查看中间过程输出
# print(response.output)
print(response.output_text)

Node.js

import OpenAI from "openai";

const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY,
    baseURL: "https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1"
});

async function main() {
    const response = await openai.responses.create({
        model: "qwen3-max-2026-01-23",
        input: "帮我找一下阿里云官网，并提取首页的关键信息",
        tools: [
            { type: "web_search" },
            { type: "code_interpreter" },
            { type: "web_extractor" }
        ],
        enable_thinking: true
    });

    for (const item of response.output) {
        if (item.type === "reasoning") {
            console.log("模型正在思考...");
        } else if (item.type === "web_search_call") {
            console.log(`搜索查询: ${item.action.query}`);
        } else if (item.type === "web_extractor_call") {
            console.log("正在抽取网页内容...");
        } else if (item.type === "message") {
            console.log(`回复内容: ${item.content[0].text}`);
        }
    }
}

main();

curl

curl -X POST https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3-max-2026-01-23",
    "input": "帮我找一下阿里云官网，并提取首页的关键信息",
    "tools": [
        {
            "type": "web_search"
        },
        {
            "type": "code_interpreter"
        },
        {
            "type": "web_extractor"
        }
    ],
    "enable_thinking": true
}'

Python

# Chat Completions API
completion = client.chat.completions.create(
    model="qwen3-max-2026-01-23",
    messages=[
        {"role": "system", "content": "你是一个有帮助的助手。"},
        {"role": "user", "content": "你好！"}
    ]
)
print(completion.choices[0].message.content)

# Responses API - 可以使用相同的消息格式
response = client.responses.create(
    model="qwen3-max-2026-01-23",
    input=[
        {"role": "system", "content": "你是一个有帮助的助手。"},
        {"role": "user", "content": "你好！"}
    ]
)
print(response.output_text)

# Responses API - 或使用更简洁的格式
response = client.responses.create(
    model="qwen3-max-2026-01-23",
    input="你好！"
)
print(response.output_text)

Node.js

// Chat Completions API
const completion = await client.chat.completions.create({
    model: "qwen3-max-2026-01-23",
    messages: [
        { role: "system", content: "你是一个有帮助的助手。" },
        { role: "user", content: "你好！" }
    ]
});
console.log(completion.choices[0].message.content);

// Responses API - 可以使用相同的消息格式
const response = await client.responses.create({
    model: "qwen3-max-2026-01-23",
    input: [
        { role: "system", content: "你是一个有帮助的助手。" },
        { role: "user", content: "你好！" }
    ]
});
console.log(response.output_text);

// Responses API - 或使用更简洁的格式
const response2 = await client.responses.create({
    model: "qwen3-max-2026-01-23",
    input: "你好！"
});
console.log(response2.output_text);

curl

# Chat Completions API
curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3-max-2026-01-23",
    "messages": [
        {"role": "system", "content": "你是一个有帮助的助手。"},
        {"role": "user", "content": "你好！"}
    ]
}'

# Responses API - 使用更简洁的格式
curl -X POST https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3-max-2026-01-23",
    "input": "你好！"
}'

Python

# Chat Completions - 需要手动管理消息历史
messages = [
    {"role": "system", "content": "你是一个有帮助的助手。"},
    {"role": "user", "content": "法国的首都是哪里？"}
]
res1 = client.chat.completions.create(
    model="qwen3-max-2026-01-23",
    messages=messages
)

# 手动添加响应到历史
messages.append(res1.choices[0].message)
messages.append({"role": "user", "content": "它的人口是多少？"})

res2 = client.chat.completions.create(
    model="qwen3-max-2026-01-23",
    messages=messages
)

# Responses API - 使用 previous_response_id 自动关联
res1 = client.responses.create(
    model="qwen3-max-2026-01-23",
    input="法国的首都是哪里？"
)

# 只需传递上一轮的 ID
res2 = client.responses.create(
    model="qwen3-max-2026-01-23",
    input="它的人口是多少？",
    previous_response_id=res1.id
)

Node.js

// Chat Completions - 需要手动管理消息历史
let messages = [
    { role: "system", content: "你是一个有帮助的助手。" },
    { role: "user", content: "法国的首都是哪里？" }
];
const res1 = await client.chat.completions.create({
    model: "qwen3-max-2026-01-23",
    messages
});

// 手动添加响应到历史
messages = messages.concat([res1.choices[0].message]);
messages.push({ role: "user", content: "它的人口是多少？" });

const res2 = await client.chat.completions.create({
    model: "qwen3-max-2026-01-23",
    messages
});

// Responses API - 使用 previous_response_id 自动关联
const res1 = await client.responses.create({
    model: "qwen3-max-2026-01-23",
    input: "法国的首都是哪里？"
});

// 只需传递上一轮的 ID
const res2 = await client.responses.create({
    model: "qwen3-max-2026-01-23",
    input: "它的人口是多少？",
    previous_response_id: res1.id
});

Python

# Chat Completions - 需要自己实现工具函数
def web_search(query):
    # 需要自己实现网络搜索逻辑
    import requests
    r = requests.get(f"https://api.example.com/search?q={query}")
    return r.json().get("results", [])

completion = client.chat.completions.create(
    model="qwen3-max-2026-01-23",
    messages=[{"role": "user", "content": "法国现任总统是谁？"}],
    functions=[{
        "name": "web_search",
        "description": "搜索网络信息",
        "parameters": {
            "type": "object",
            "properties": {"query": {"type": "string"}},
            "required": ["query"]
        }
    }]
)

# Responses API - 直接使用内置工具
response = client.responses.create(
    model="qwen3-max-2026-01-23",
    input="法国现任总统是谁？",
    tools=[{"type": "web_search"}]  # 直接启用网络搜索
)
print(response.output_text)

Node.js

// Chat Completions - 需要自己实现工具函数
async function web_search(query) {
    const fetch = (await import('node-fetch')).default;
    const res = await fetch(`https://api.example.com/search?q=${query}`);
    const data = await res.json();
    return data.results;
}

const completion = await client.chat.completions.create({
    model: "qwen3-max-2026-01-23",
    messages: [{ role: "user", content: "法国现任总统是谁？" }],
    functions: [{
        name: "web_search",
        description: "搜索网络信息",
        parameters: {
            type: "object",
            properties: { query: { type: "string" } },
            required: ["query"]
        }
    }]
});

// Responses API - 直接使用内置工具
const response = await client.responses.create({
    model: "qwen3-max-2026-01-23",
    input: "法国现任总统是谁？",
    tools: [{ type: "web_search" }]  // 直接启用网络搜索
});
console.log(response.output_text);

curl

# Chat Completions - 需要自己实现工具
# 这里展示调用外部搜索 API 的示例
curl https://api.example.com/search \
  -G \
  --data-urlencode "q=法国现任总统" \
  --data-urlencode "key=$SEARCH_API_KEY"

# Responses API - 直接使用内置工具
curl -X POST https://dashscope.aliyuncs.com/api/v2/apps/protocols/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3-max-2026-01-23",
    "input": "法国现任总统是谁？",
    "tools": [{"type": "web_search"}]
}'