首次调用通义千问API_大模型服务平台百炼(Model Studio)-阿里云帮助中心

```
# 用您的阿里云百炼API Key代替YOUR_DASHSCOPE_API_KEY
echo "export DASHSCOPE_API_KEY='YOUR_DASHSCOPE_API_KEY'" >> ~/.bashrc
```
```
nano ~/.bashrc
```
```
# 用您的阿里云百炼API Key代替YOUR_DASHSCOPE_API_KEY
export DASHSCOPE_API_KEY="YOUR_DASHSCOPE_API_KEY"
```
```
source ~/.bashrc
```
```
echo $DASHSCOPE_API_KEY
```

# 用您的阿里云百炼API Key代替YOUR_DASHSCOPE_API_KEY
export DASHSCOPE_API_KEY="YOUR_DASHSCOPE_API_KEY"

```
echo $DASHSCOPE_API_KEY
```

```
echo $SHELL
```
1. ```
# 用您的阿里云百炼API Key代替YOUR_DASHSCOPE_API_KEY
echo "export DASHSCOPE_API_KEY='YOUR_DASHSCOPE_API_KEY'" >> ~/.zshrc
```
2. ```
source ~/.zshrc
```
3. ```
echo $DASHSCOPE_API_KEY
```
1. ```
# 用您的阿里云百炼API Key代替YOUR_DASHSCOPE_API_KEY
echo "export DASHSCOPE_API_KEY='YOUR_DASHSCOPE_API_KEY'" >> ~/.bash_profile
```
2. ```
source ~/.bash_profile
```
3. ```
echo $DASHSCOPE_API_KEY
```

# 用您的阿里云百炼API Key代替YOUR_DASHSCOPE_API_KEY
export DASHSCOPE_API_KEY="YOUR_DASHSCOPE_API_KEY"

```
echo $DASHSCOPE_API_KEY
```

- ```
echo %DASHSCOPE_API_KEY%
```
- ```
echo $env:DASHSCOPE_API_KEY
```

# 用您的阿里云百炼API Key代替YOUR_DASHSCOPE_API_KEY
setx DASHSCOPE_API_KEY "YOUR_DASHSCOPE_API_KEY"

```
echo %DASHSCOPE_API_KEY%
```

# 用您的阿里云百炼API Key代替YOUR_DASHSCOPE_API_KEY
set DASHSCOPE_API_KEY=YOUR_DASHSCOPE_API_KEY

echo %DASHSCOPE_API_KEY%

# 用您的阿里云百炼API Key代替YOUR_DASHSCOPE_API_KEY
[Environment]::SetEnvironmentVariable("DASHSCOPE_API_KEY", "YOUR_DASHSCOPE_API_KEY", [EnvironmentVariableTarget]::User)

```
echo $env:DASHSCOPE_API_KEY
```

# 用您的阿里云百炼API Key代替YOUR_DASHSCOPE_API_KEY
$env:DASHSCOPE_API_KEY = "YOUR_DASHSCOPE_API_KEY"

echo $env:DASHSCOPE_API_KEY

python -V
pip --version

- ```
/usr/bin/python
/usr/bin/pip
```
- ```
/usr/bin/which: no python in (/root/.local/bin:/root/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin)
/usr/bin/which: no pip in (/root/.local/bin:/root/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin)
```
```
/usr/bin/python3
/usr/bin/pip3
```

```
# 如果运行失败，您可以将python替换成python3再运行
python -m venv .venv
```
```
.venv\Scripts\activate
```
```
source .venv/bin/activate
```

# 如果运行失败，您可以将pip替换成pip3再运行
pip install -U openai

# 如果运行失败，您可以将pip替换成pip3再运行
pip install -U dashscope

如果在安装 SDK 过程中出现 WARNING: You are using pip version xxx; however, version xxx is available. 提示，此为 pip 工具版本更新通知，与 SDK 安装无关，请直接忽略即可。

import os
from openai import OpenAI
    client = OpenAI(
        # 若没有配置环境变量，请用阿里云百炼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/compatible-mode/v1",
    completion = client.chat.completions.create(
        model="qwen-plus",  # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
        messages=[
            {'role': 'system', 'content': 'You are a helpful assistant.'},
            {'role': 'user', 'content': '你是谁？'}
    print(completion.choices[0].message.content)
except Exception as e:
    print(f"错误信息：{e}")
    print("请参考文档：https://help.aliyun.com/zh/model-studio/developer-reference/error-code")

```
我是阿里云开发的一款超大规模语言模型，我叫通义千问。
```

import os
from dashscope import Generation
messages = [
    {'role': 'system', 'content': 'You are a helpful assistant.'},
    {'role': 'user', 'content': '你是谁？'}
response = Generation.call(
    # 若没有配置环境变量，请用阿里云百炼API Key将下行替换为：api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"), 
    model="qwen-plus",   # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
    messages=messages,
    result_format="message"
if response.status_code == 200:
    print(response.output.choices[0].message.content)
else:
    print(f"HTTP返回码：{response.status_code}")
    print(f"错误码：{response.code}")
    print(f"错误信息：{response.message}")
    print("请参考文档：https://help.aliyun.com/zh/model-studio/developer-reference/error-code")

本示例使用的运行命令需在 Python 文件所在目录执行，如果想要在任意位置执行，请在文件名前指定具体文件路径。
```
我是来自阿里云的大规模语言模型，我叫通义千问。
```

node -v
npm -v

npm install --save openai
yarn add openai

如果安装失败，您可以通过配置镜像源的方法来完成安装，如：

npm config set registry https://registry.npmmirror.com/

配置镜像源后，您可以重新运行安装 SDK 的命令。

import OpenAI from "openai";
try {
    const openai = new OpenAI(
            // 若没有配置环境变量，请用阿里云百炼API Key将下行替换为：apiKey: "sk-xxx",
            apiKey: process.env.DASHSCOPE_API_KEY,
            baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1"
    const completion = await openai.chat.completions.create({
        model: "qwen-plus",  //模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
        messages: [
            { role: "system", content: "You are a helpful assistant." },
            { role: "user", content: "你是谁？" }
    console.log(completion.choices[0].message.content);
} catch (error) {
    console.log(`错误信息：${error}`);
    console.log("请参考文档：https://help.aliyun.com/zh/model-studio/developer-reference/error-code");
}

```
node hello_qwen.mjs
```
```
我是来自阿里云的语言模型，我叫通义千问。
```

java -version
# （可选）如果使用maven管理和构建java项目，还需确保maven已正确安装到您的开发环境中
mvn --version

<dependency>
    <groupId>com.alibaba</groupId>
    <artifactId>dashscope-sdk-java</artifactId>
    <!-- 请将 'the-latest-version' 替换为最新版本号：https://mvnrepository.com/artifact/com.alibaba/dashscope-sdk-java -->
    <version>the-latest-version</version>
</dependency>

dependencies {
    // 请将 'the-latest-version' 替换为最新版本号：https://mvnrepository.com/artifact/com.alibaba/dashscope-sdk-java
    implementation group: 'com.alibaba', name: 'dashscope-sdk-java', version: 'the-latest-version'
}

```
./gradlew build --refresh-dependencies
```

import java.util.Arrays;
import java.lang.System;
import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
public class Main {
    public static GenerationResult callWithMessage() throws ApiException, NoApiKeyException, InputRequiredException {
        Generation gen = new Generation();
        Message systemMsg = Message.builder()
                .role(Role.SYSTEM.getValue())
                .content("You are a helpful assistant.")
                .build();
        Message userMsg = Message.builder()
                .role(Role.USER.getValue())
                .content("你是谁？")
                .build();
        GenerationParam param = GenerationParam.builder()
                // 若没有配置环境变量，请用阿里云百炼API Key将下行替换为：.apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                // 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
                .model("qwen-plus")
                .messages(Arrays.asList(systemMsg, userMsg))
                .resultFormat(GenerationParam.ResultFormat.MESSAGE)
                .build();
        return gen.call(param);
    public static void main(String[] args) {
        try {
            GenerationResult result = callWithMessage();
            System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent());
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            System.err.println("错误信息："+e.getMessage());
            System.out.println("请参考文档：https://help.aliyun.com/zh/model-studio/developer-reference/error-code");
        System.exit(0);
}

我是阿里云开发的一款超大规模语言模型，我叫通义千问。

若没有配置环境变量，请用阿里云百炼 API Key 将：-H "Authorization: Bearer $DASHSCOPE_API_KEY" \ 换为：-H "Authorization: Bearer sk-xxx" \ 。

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\": \"qwen-plus\",
    \"messages\": [
            \"role\": \"system\",
            \"content\": \"You are a helpful assistant.\"
            \"role\": \"user\",
            \"content\": \"你是谁？\"
}"

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": "qwen-plus",
    "messages": [
            "role": "system",
            "content": "You are a helpful assistant."
            "role": "user", 
            "content": "你是谁？"
}'

{
    "choices": [
            "message": {
                "role": "assistant",
                "content": "我是来自阿里云的大规模语言模型，我叫通义千问。"
            "finish_reason": "stop",
            "index": 0,
            "logprobs": null
    "object": "chat.completion",
    "usage": {
        "prompt_tokens": 22,
        "completion_tokens": 16,
        "total_tokens": 38
    "created": 1728353155,
    "system_fingerprint": null,
    "model": "qwen-plus",
    "id": "chatcmpl-39799876-eda8-9527-9e14-2214d641cf9a"
}

curl -X POST "https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation" ^
-H "Authorization: Bearer %DASHSCOPE_API_KEY%" ^
-H "Content-Type: application/json" ^
-d "{
  \"model\": \"qwen-plus\",
  \"input\": {
    \"messages\": [
        \"role\": \"system\",
        \"content\": \"You are a helpful assistant.\"
        \"role\": \"user\",
        \"content\": \"你是谁？\"
  \"parameters\": {
    \"result_format\": \"message\"
}"

curl -X POST https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen-plus",
    "input":{
        "messages":[      
                "role": "system",
                "content": "You are a helpful assistant."
                "role": "user",
                "content": "你是谁？"
    "parameters": {
        "result_format":"message"
}'

{
    "output": {
        "choices": [
                "finish_reason": "stop",
                "message": {
                    "role": "assistant",
                    "content": "我是来自阿里云的大规模语言模型，我叫通义千问。"
    "usage": {
        "total_tokens": 38,
        "output_tokens": 16,
        "input_tokens": 22
    "request_id": "87f776d7-3c82-9d39-b238-d1ad38c9b6a9"
}

查看更多模型	示例代码以 qwen-plus 模型为例，阿里云百炼还支持其他通义千问模型与 DeepSeek、Llama 等第三方模型，支持的模型以及对应的 API 参考文档请参见模型列表与价格。
了解进阶用法	示例代码仅完成了简单问答，如果您想了解通义千问 API 的更多用法，如流式输出、结构化输出、工具调用等，请参见概述目录。
在线体验大模型	如果您想像通义官网一样，通过对话框与大模型互动，请访问模型体验。通义官网将通义千问 API 与联网搜索、网页解析等工具进行了集成，与直接调用通义千问 API 效果略有差异。
0 代码进行大模型微调	通常来说，对大模型微调需要有人工智能知识背景与工程能力，阿里云百炼提供了 0 代码对大模型进行微调的功能，您仅需提供数据集即可。详情请参见在控制台进行模型调优。

首次调用通义千问API

账号设置

配置 API Key 到环境变量

Linux 系统

添加永久性环境变量

添加临时性环境变量

macOS 系统

添加永久性环境变量

Zsh

Bash

添加临时性环境变量

Windows 系统

系统属性

CMD

添加永久性环境变量

添加临时性环境变量

PowerShell

添加永久性环境变量

添加临时性环境变量

选择开发语言

Python

步骤 1：配置 Python 环境

检查您的 Python 版本

常见问题

Windows 系统

Linux、macOS 系统

配置虚拟环境（可选）

安装 OpenAI Python SDK 或 DashScope Python SDK

安装 OpenAI Python SDK

安装 DashScope Python SDK

步骤 2：调用大模型 API

OpenAI Python SDK

DashScope Python SDK

Node.js

步骤 1：配置 Node.js 环境

检查 Node.js 安装状态

安装模型调用 SDK

步骤 2：调用大模型 API

Java

步骤 1：配置 Java 环境

检查您的 Java 版本

安装模型调用 SDK

XML

Gradle

步骤 2：调用大模型 API

curl

OpenAI 兼容-HTTP

DashScope-HTTP

其它语言

API 参考

常见问题

免费额度 用完后如何购买 Token？

调用大模型 API 后报错 Model.AccessDenied ，如何处理？

如何接入 Chatbox 、 Cherry Studio 、 Cline 或 Dify ？

Chatbox

Cherry Studio

Cline

Dify

下一步