JSON Mode

执行信息抽取或结构化数据生成任务时,大模型可能返回多余文本(如 ```json)导致下游解析失败。开启结构化输出可以确保大模型输出标准格式的 JSON 字符串。

支持的模型

  • 通义千问Max 系列:qwen-max、qwen-max-latest、qwen-max-2024-09-19 及之后的快照模型

  • 通义千问Plus 系列(非思考模式):qwen-plus、qwen-plus-latest、qwen-plus-2024-09-19及之后的快照模型

  • 通义千问Flash 系列(非思考模式):qwen-flash、qwen-flash-2025-07-28及之后的快照模型

  • 通义千问Coder 系列:qwen3-coder-plus、qwen3-coder-plus-2025-07-22、qwen3-coder-flash、qwen3-coder-flash-2025-07-28

  • 通义千问VL 系列:qwen-vl-max、qwen-vl-plus(不包括最新版与快照版模型)

  • 通义千问Turbo 系列(非思考模式):qwen-turbo、qwen-turbo-latest、qwen-turbo-2024-09-19及之后的快照模型

  • Qwen 开源系列

    • Qwen3(非思考模式)

    • Qwen3-Coder

    • Qwen2.5 系列的文本模型(不含mathcoder模型)

重要

思考模式的模型暂不支持结构化输出功能。

模型的上下文、价格、快照版本等信息请参见模型列表与价格

如何使用

前提条件

您需要已获取API Key配置API Key到环境变量。如果通过OpenAI SDKDashScope SDK进行调用,还需要安装SDK

开启方法

开启结构化输出功能需要以下三点:

  • 选择模型:支持的模型中选择。

    • 文本处理:推荐使用通义千问Plus 系列或通义千问Flash 系列模型。通义千问Plus 系列模型效果、速度、成本均衡;通义千问Flash 系列模型成本低,速度快,性价比较高。

    • 图片、视频数据处理:请选择qwen-vl-maxqwen-vl-plus模型。qwen-vl-max模型能力强,qwen-vl-plus模型在效果、成本上比较均衡。

  • 设置参数:设置请求参数response_format{"type": "json_object"}以启用结构化输出功能。

  • 提示词指引:System Message 或 User Message 中需要包含 "JSON" 关键词(不区分大小写),否则会报错:'messages' must contain the word 'json' in some form, to use 'response_format' of type 'json_object'.

快速开始

以从个人简介中抽取信息的简单场景为例,介绍快速使用结构化输出的方法。

OpenAI兼容

Python

from openai import OpenAI
import os

client = OpenAI(
    # 如果没有配置环境变量,请用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-flash",
    messages=[
        {
            "role": "system",
            "content": "请抽取用户的姓名与年龄信息,以JSON格式返回"
        },
        {
            "role": "user",
            "content": "大家好,我叫刘五,今年34岁,邮箱是liuwu@example.com,平时喜欢打篮球和旅游", 
        },
    ],
    response_format={"type": "json_object"}
)

json_string = completion.choices[0].message.content
print(json_string)

返回结果

{
  "姓名": "刘五",
  "年龄": 34
}

Node.js

import OpenAI from "openai";

const openai = new OpenAI({
    // 如果没有配置环境变量,请用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-flash",
    messages: [
        {
            role: "system",
            content: "请抽取用户的姓名与年龄信息,以JSON格式返回"
        },
        {
            role: "user",
            content: "大家好,我叫刘五,今年34岁,邮箱是liuwu@example.com,平时喜欢打篮球和旅游"
        }
    ],
    response_format: {
        type: "json_object"
    }
});

const jsonString = completion.choices[0].message.content;
console.log(jsonString);

返回结果

{
  "姓名": "刘五",
  "年龄": 34
}

curl

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": "你需要提取出name(名字,为string类型)、age(年龄,为string类型)与email(邮箱,为string类型),请输出JSON 字符串,不要输出其它无关内容。\n示例:\nQ:我叫张三,今年25岁,邮箱是zhangsan@example.com\nA:{\"name\":\"张三\",\"age\":\"25岁\",\"email\":\"zhangsan@example.com\"}\nQ:我叫李四,今年30岁,我的邮箱是lisi@example.com\nA:{\"name\":\"李四\",\"age\":\"30岁\",\"email\":\"lisi@example.com\"}\nQ:我叫王五,我的邮箱是wangwu@example.com,今年40岁\nA:{\"name\":\"王五\",\"age\":\"40岁\",\"email\":\"wangwu@example.com\""
        },
        {
            "role": "user", 
            "content": "大家好,我叫刘五,今年34岁,邮箱是liuwu@example.com"
        }
    ],
    "response_format": {
        "type": "json_object"
    }
}'

返回结果

{
    "choices": [
        {
            "message": {
                "role": "assistant",
                "content": "{\"name\":\"刘五\",\"age\":\"34岁\",\"email\":\"liuwu@example.com\"}"
            },
            "finish_reason": "stop",
            "index": 0,
            "logprobs": null
        }
    ],
    "object": "chat.completion",
    "usage": {
        "prompt_tokens": 207,
        "completion_tokens": 20,
        "total_tokens": 227,
        "prompt_tokens_details": {
            "cached_tokens": 0
        }
    },
    "created": 1756455080,
    "system_fingerprint": null,
    "model": "qwen-plus",
    "id": "chatcmpl-624b665b-fb93-99e7-9ebd-bb6d86d314d2"
}

DashScope

Python

import os
import dashscope

messages=[
    {
        "role": "system",
        "content": "请抽取用户的姓名与年龄信息,以JSON格式返回"
    },
    {
        "role": "user",
        "content": "大家好,我叫刘五,今年34岁,邮箱是liuwu@example.com,平时喜欢打篮球和旅游", 
    },
]
response = dashscope.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-flash", 
    messages=messages,
    result_format='message',
    response_format={'type': 'json_object'}
    )
json_string = response.output.choices[0].message.content
print(json_string)

返回结果

{
  "姓名": "刘五",
  "年龄": 34
}

Java

// DashScope Java SDK 版本需要不低于 2.18.4

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;
import com.alibaba.dashscope.common.ResponseFormat;

public class Main {
    public static GenerationResult callWithMessage() throws ApiException, NoApiKeyException, InputRequiredException {
        Generation gen = new Generation();
        Message systemMsg = Message.builder()
                .role(Role.SYSTEM.getValue())
                .content("请抽取用户的姓名与年龄信息,以JSON格式返回")
                .build();
        Message userMsg = Message.builder()
                .role(Role.USER.getValue())
                .content("大家好,我叫刘五,今年34岁,邮箱是liuwu@example.com,平时喜欢打篮球和旅游")
                .build();
        ResponseFormat jsonMode = ResponseFormat.builder().type("json_object").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"))
                .model("qwen-flash")
                .messages(Arrays.asList(systemMsg, userMsg))
                .resultFormat(GenerationParam.ResultFormat.MESSAGE)
                .responseFormat(jsonMode)
                .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) {
            // 使用日志框架记录异常信息
            System.err.println("An error occurred while calling the generation service: " + e.getMessage());
        }
        System.exit(0);
    }
}

返回结果

{
  "姓名": "刘五",
  "年龄": 34
}

curl

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-flash",
    "input": {
        "messages": [
            {
                "role": "system",
                "content": "请抽取用户的姓名与年龄信息,以JSON格式返回"
            },
            {
                "role": "user", 
                "content": "大家好,我叫刘五,今年34岁,邮箱是liuwu@example.com,平时喜欢打篮球和旅游"
            }
        ]
    },
    "parameters": {
        "result_format": "message",
        "response_format": {
            "type": "json_object"
        }
    }
}'

返回结果

{
    "output": {
        "choices": [
            {
                "finish_reason": "stop",
                "message": {
                    "role": "assistant",
                    "content": "{\n  \"姓名\": \"刘五\",\n  \"年龄\": 34\n}"
                }
            }
        ]
    },
    "usage": {
        "total_tokens": 72,
        "output_tokens": 18,
        "input_tokens": 54,
        "cached_tokens": 0
    },
    "request_id": "xxx-xxx-xxx-xxx-xxx"
}

图片、视频数据处理

除了文本信息,qwen-vl-maxqwen-vl-plus模型还支持针对图像、视频数据进行结构化输出,实现视觉信息抽取、定位、事件监测等功能。

OpenAI兼容

Python

import os
from openai import OpenAI

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

completion = client.chat.completions.create(
    model="qwen-vl-max",
    messages=[
        {
            "role": "system",
            "content": [{"type": "text", "text": "You are a helpful assistant."}],
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "http://duguang-labelling.oss-cn-shanghai.aliyuncs.com/demo_ocr/receipt_zh_demo.jpg"
                    },
                },
                {"type": "text", "text": "提取图中ticket(包括 travel_date、trains、seat_num、arrival_site、price)和 invoice 的信息(包括 invoice_code 和 invoice_number ),请输出包含 ticket 和 invoice 数组的JSON"},
            ],
        },
    ],
    response_format={"type": "json_object"}
)
json_string = completion.choices[0].message.content
print(json_string)

返回结果

{
  "ticket": [
    {
      "travel_date": "2013-06-29",
      "trains": "流水",
      "seat_num": "371",
      "arrival_site": "开发区",
      "price": "8.00"
    }
  ],
  "invoice": [
    {
      "invoice_code": "221021325353",
      "invoice_number": "10283819"
    }
  ]
}

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/compatible-mode/v1"
});

async function main() {
  const response = await openai.chat.completions.create({
    model: "qwen-vl-max",
    messages: [{
        role: "system",
        content: [{
          type: "text",
          text: "You are a helpful assistant."
        }]
      },
      {
        role: "user",
        content: [{
            type: "image_url",
            image_url: {
              "url": "http://duguang-labelling.oss-cn-shanghai.aliyuncs.com/demo_ocr/receipt_zh_demo.jpg"
            }
          },
          {
            type: "text",
            text: "提取图中ticket(包括 travel_date、trains、seat_num、arrival_site、price)和 invoice 的信息(数组类型,包括 invoice_code 和 invoice_number ),请输出包含 ticket 和 invoice 数组的JSON"
          }
        ]
      }
    ],
    response_format: {type: "json_object"}
  });
  const jsonString = completion.choices[0].message.content
  console.log(response.choices[0].message.content);
}

main()

返回结果

{
  "ticket": [
    {
      "travel_date": "2013-06-29",
      "trains": "流水",
      "seat_num": "371",
      "arrival_site": "开发区",
      "price": "8.00"
    }
  ],
  "invoice": [
    {
      "invoice_code": "221021325353",
      "invoice_number": "10283819"
    }
  ]
}

curl

curl --location 'https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
  "model": "qwen-vl-max",
  "messages": [
  {"role":"system",
  "content":[
    {"type": "text", "text": "You are a helpful assistant."}]},
  {
    "role": "user",
    "content": [
      {"type": "image_url", "image_url": {"url": "http://duguang-labelling.oss-cn-shanghai.aliyuncs.com/demo_ocr/receipt_zh_demo.jpg"}},
      {"type": "text", "text": "提取图中ticket(包括 travel_date、trains、seat_num、arrival_site、price)和 invoice 的信息(数组类型,包括 invoice_code 和 invoice_number ),请输出包含 ticket 和 invoice 数组的JSON"}
    ]
  }],
  "response_format":{"type": "json_object"}
}'

返回结果

{
  "choices": [{
    "message": {
      "content": "{\n  \"ticket\": [\n    {\n      \"travel_date\": \"2013-06-29\",\n      \"trains\": \"流水\",\n      \"seat_num\": \"371\",\n      \"arrival_site\": \"开发区\",\n      \"price\": \"8.00\"\n    }\n  ],\n  \"invoice\": [\n    {\n      \"invoice_code\": \"221021325353\",\n      \"invoice_number\": \"10283819\"\n    }\n  ]\n}",
      "role": "assistant"
    },
    "finish_reason": "stop",
    "index": 0,
    "logprobs": null
  }],
  "object": "chat.completion",
  "usage": {
    "prompt_tokens": 486,
    "completion_tokens": 112,
    "total_tokens": 598,
    "prompt_tokens_details": {
      "cached_tokens": 0
    }
  },
  "created": 1755767481,
  "system_fingerprint": null,
  "model": "qwen-vl-max",
  "id": "chatcmpl-33249829-e9f3-9cbc-93e4-0536b3d7d713"
}

DashScope

Python

import os
import dashscope
messages = [
{
    "role": "system",
    "content": [
    {"text": "You are a helpful assistant."}]
},
{
    "role": "user",
    "content": [
    {"image": "http://duguang-labelling.oss-cn-shanghai.aliyuncs.com/demo_ocr/receipt_zh_demo.jpg"},
    {"text": "提取图中ticket(包括 travel_date、trains、seat_num、arrival_site、price)和 invoice 的信息(数组类型,包括 invoice_code 和 invoice_number ),请输出包含 ticket 和 invoice 数组的JSON"}]
}]
response = dashscope.MultiModalConversation.call(
    #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'),
    model = 'qwen-vl-max',
    messages = messages,
    response_format={'type': 'json_object'}
)
json_string = response.output.choices[0].message.content[0]["text"]
print(json_string)

返回结果

{
  "ticket": [
    {
      "travel_date": "2013-06-29",
      "trains": "流水",
      "seat_num": "371",
      "arrival_site": "开发区",
      "price": "8.00"
    }
  ],
  "invoice": [
    {
      "invoice_code": "221021325353",
      "invoice_number": "10283819"
    }
  ]
}

Java

// DashScope Java SDK 版本需要不低于 2.21.4

import java.util.Arrays;
import java.util.Collections;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.common.ResponseFormat;

public class Main {
    public static void simpleMultiModalConversationCall()
            throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        MultiModalMessage systemMessage = MultiModalMessage.builder().role(Role.SYSTEM.getValue())
                .content(Arrays.asList(
                        Collections.singletonMap("text", "You are a helpful assistant."))).build();
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(
                        Collections.singletonMap("image", "http://duguang-labelling.oss-cn-shanghai.aliyuncs.com/demo_ocr/receipt_zh_demo.jpg"),
                        Collections.singletonMap("text", "提取图中ticket(包括 travel_date、trains、seat_num、arrival_site、price)和 invoice 的信息(数组类型,包括 invoice_code 和 invoice_number ),请输出包含 ticket 和 invoice 数组的JSON"))).build();
        ResponseFormat jsonMode = ResponseFormat.builder().type("json_object").build();
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // 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(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen-vl-max")
                .messages(Arrays.asList(systemMessage, userMessage))
                .responseFormat(jsonMode)
                .build();
        MultiModalConversationResult result = conv.call(param);
        System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
    }
    public static void main(String[] args) {
        try {
            simpleMultiModalConversationCall();
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

返回结果

{
  "ticket": [
    {
      "travel_date": "2013-06-29",
      "trains": "流水",
      "seat_num": "371",
      "arrival_site": "开发区",
      "price": "8.00"
    }
  ],
  "invoice": [
    {
      "invoice_code": "221021325353",
      "invoice_number": "10283819"
    }
  ]
}

curl

curl -X POST https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
    "model": "qwen-vl-max",
    "input":{
        "messages":[
            {"role": "system",
         "content": [
           {"text": "You are a helpful assistant."}]},
            {
             "role": "user",
             "content": [
               {"image": "http://duguang-labelling.oss-cn-shanghai.aliyuncs.com/demo_ocr/receipt_zh_demo.jpg"},
               {"text": "提取图中ticket(包括 travel_date、trains、seat_num、arrival_site、price)和 invoice 的信息(数组类型,包括 invoice_code 和 invoice_number ),请输出包含 ticket 和 invoice 数组的JSON"}
                ]
            }
        ],
    "parameters": {
        "response_format": {"type": "json_object"}
    }
    }
}'

返回结果

{
  "output": {
    "choices": [{
      "message": {
        "content": [{
          "text": "```json\n{\n  \"ticket\": [\n    {\n      \"travel_date\": \"2013-06-29\",\n      \"trains\": \"流水\",\n      \"seat_num\": \"371\",\n      \"arrival_site\": \"开发区\",\n      \"price\": \"8.00\"\n    }\n  ],\n  \"invoice\": [\n    {\n      \"invoice_code\": \"221021325353\",\n      \"invoice_number\": \"10283819\"\n    }\n  ]\n}\n```"
        }],
        "role": "assistant"
      },
      "finish_reason": "stop"
    }]
  },
  "usage": {
    "input_tokens_details": {
      "image_tokens": 418,
      "text_tokens": 65
    },
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "total_tokens": 599,
    "output_tokens": 116,
    "input_tokens": 483,
    "output_tokens_details": {
      "text_tokens": 116
    },
    "image_tokens": 418
  },
  "request_id": "9261e910-b810-91e9-88cf-9f7e0eb3750c"
}

优化提示词

模糊的提示词(如“返回用户信息”)会使模型生成非预期结果。建议在提示词中准确描述预期 Schema,包括字段类型、必需性、格式要求(如日期格式),并提供示例。

OpenAI兼容

Python

from openai import OpenAI
import os
import json
import textwrap  # 用于处理多行字符串的缩进,提高代码可读性

# 预定义示例响应,用于向模型展示期望的输出格式
# 示例1:包含所有字段的完整响应
example1_response = json.dumps(
    {
        "info": {"name": "张三", "age": "25岁", "email": "zhangsan@example.com"},
        "hobby": ["唱歌"]
    },
    ensure_ascii=False
)
# 示例2:包含多个hobby的响应
example2_response = json.dumps(
    {
        "info": {"name": "李四", "age": "30岁", "email": "lisi@example.com"},
        "hobby": ["跳舞", "游泳"]
    },
    ensure_ascii=False
)
# 示例3:不包含hobby字段的响应(hobby非必需)
example3_response = json.dumps(
    {
        "info": {"name": "赵六", "age": "28岁", "email": "zhaoliu@example.com"}
    },
    ensure_ascii=False
)
# 示例4:另一个不包含hobby字段的响应
example4_response = json.dumps(
    {
        "info": {"name": "孙七", "age": "35岁", "email": "sunqi@example.com"}
    },
    ensure_ascii=False
)

# 初始化OpenAI客户端
client = OpenAI(
    # 若没有配置环境变量,请将下行替换为:api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

# dedent的作用是去除每行开头的公共缩进,使字符串在代码中可以美观地缩进,但在运行时不会包含这些额外的空格
system_prompt = textwrap.dedent(f"""\
    请从用户输入中提取个人信息并按照指定的JSON Schema格式输出:

    【输出格式要求】
    输出必须严格遵循以下JSON结构:
    {{
      "info": {{
        "name": "字符串类型,必需字段,用户姓名",
        "age": "字符串类型,必需字段,格式为'数字+岁',例如'25岁'",
        "email": "字符串类型,必需字段,标准邮箱格式,例如'user@example.com'"
      }},
      "hobby": ["字符串数组类型,非必需字段,包含用户的所有爱好,如未提及则完全不输出此字段"]
    }}

    【字段提取规则】
    1. name: 从文本中识别用户姓名,必需提取
    2. age: 识别年龄信息,转换为"数字+岁"格式,必需提取
    3. email: 识别邮箱地址,保持原始格式,必需提取
    4. hobby: 识别用户爱好,以字符串数组形式输出,如未提及爱好信息则完全省略hobby字段

    【参考示例】
    示例1(包含爱好):
    Q:我叫张三,今年25岁,邮箱是zhangsan@example.com,爱好是唱歌
    A:{example1_response}

    示例2(包含多个爱好):
    Q:我叫李四,今年30岁,邮箱是lisi@example.com,平时喜欢跳舞和游泳
    A:{example2_response}

    示例3(不包含爱好):
    Q:我叫赵六,今年28岁,我的邮箱是zhaoliu@example.com
    A:{example3_response}

    示例4(不包含爱好):
    Q:我是孙七,35岁,邮箱sunqi@example.com
    A:{example4_response}

    请严格按照上述格式和规则提取信息并输出JSON。如果用户未提及爱好,则不要在输出中包含hobby字段。\
""")

# 调用大模型API进行信息提取
completion = client.chat.completions.create(
    model="qwen-plus",
    messages=[
        {
            "role": "system",
            "content": system_prompt
        },
        {
            "role": "user",
            "content": "大家好,我叫刘五,今年34岁,邮箱是liuwu@example.com,平时喜欢打篮球和旅游", 
        },
    ],
    response_format={"type": "json_object"},  # 指定返回JSON格式
)

# 提取并打印模型生成的JSON结果
json_string = completion.choices[0].message.content
print(json_string)

返回结果

{
  "info": {
    "name": "刘五",
    "age": "34岁",
    "email": "liuwu@example.com"
  },
  "hobby": ["打篮球", "旅游"]  
}

Node.js

import OpenAI from "openai";

// 预定义示例响应(用于向模型展示期望的输出格式)
// 示例1:包含所有字段的完整响应
const example1Response = JSON.stringify({
    info: { name: "张三", age: "25岁", email: "zhangsan@example.com" },
    hobby: ["唱歌"]
}, null, 2);

// 示例2:包含多个hobby的响应
const example2Response = JSON.stringify({
    info: { name: "李四", age: "30岁", email: "lisi@example.com" },
    hobby: ["跳舞", "游泳"]
}, null, 2);

// 示例3:不包含hobby字段的响应(hobby非必需)
const example3Response = JSON.stringify({
    info: { name: "赵六", age: "28岁", email: "zhaoliu@example.com" }
}, null, 2);

// 示例4:另一个不包含hobby字段的响应
const example4Response = JSON.stringify({
    info: { name: "孙七", age: "35岁", email: "sunqi@example.com" }
}, null, 2);

// 初始化OpenAI客户端配置
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"
});

// 创建聊天完成请求,使用结构化提示词来提高输出准确性
const completion = await openai.chat.completions.create({
    model: "qwen-plus",
    messages: [
        {
            role: "system",
            content: `请从用户输入中提取个人信息并按照指定的JSON Schema格式输出:

【输出格式要求】
输出必须严格遵循以下JSON结构:
{
  "info": {
    "name": "字符串类型,必需字段,用户姓名",
    "age": "字符串类型,必需字段,格式为'数字+岁',例如'25岁'",
    "email": "字符串类型,必需字段,标准邮箱格式,例如'user@example.com'"
  },
  "hobby": ["字符串数组类型,非必需字段,包含用户的所有爱好,如未提及则完全不输出此字段"]
}

【字段提取规则】
1. name: 从文本中识别用户姓名,必需提取
2. age: 识别年龄信息,转换为"数字+岁"格式,必需提取
3. email: 识别邮箱地址,保持原始格式,必需提取
4. hobby: 识别用户爱好,以字符串数组形式输出,如未提及爱好信息则完全省略hobby字段

【参考示例】
示例1(包含爱好):
Q:我叫张三,今年25岁,邮箱是zhangsan@example.com,爱好是唱歌
A:${example1Response}

示例2(包含多个爱好):
Q:我叫李四,今年30岁,邮箱是lisi@example.com,平时喜欢跳舞和游泳
A:${example2Response}

示例3(不包含爱好):
Q:我叫赵六,今年28岁,我的邮箱是zhaoliu@example.com
A:${example3Response}

示例4(不包含爱好):
Q:我是孙七,35岁,邮箱sunqi@example.com
A:${example4Response}

请严格按照上述格式和规则提取信息并输出JSON。如果用户未提及爱好,则不要在输出中包含hobby字段。`
        },
        {
            role: "user",
            content: "大家好,我叫刘五,今年34岁,邮箱是liuwu@example.com,平时喜欢打篮球和旅游"
        }
    ],
    response_format: {
        type: "json_object"
    }
});

// 提取并打印模型生成的JSON结果
const jsonString = completion.choices[0].message.content;
console.log(jsonString);

返回结果

{
  "info": {
    "name": "刘五",
    "age": "34岁",
    "email": "liuwu@example.com"
  },
  "hobby": [
    "打篮球",
    "旅游"
  ]
}

DashScope

Python

import os
import json
import dashscope

# 预定义示例响应(用于向模型展示期望的输出格式)
example1_response = json.dumps(
    {
        "info": {"name": "张三", "age": "25岁", "email": "zhangsan@example.com"},
        "hobby": ["唱歌"]
    },
    ensure_ascii=False
)
example2_response = json.dumps(
    {
        "info": {"name": "李四", "age": "30岁", "email": "lisi@example.com"},
        "hobby": ["跳舞", "游泳"]
    },
    ensure_ascii=False
)
example3_response = json.dumps(
    {
        "info": {"name": "王五", "age": "40岁", "email": "wangwu@example.com"},
        "hobby": ["Rap", "篮球"]
    },
    ensure_ascii=False
)

messages=[
        {
            "role": "system",
            "content": f"""请从用户输入中提取个人信息并按照指定的JSON Schema格式输出:

【输出格式要求】
输出必须严格遵循以下JSON结构:
{{
  "info": {{
    "name": "字符串类型,必需字段,用户姓名",
    "age": "字符串类型,必需字段,格式为'数字+岁',例如'25岁'",
    "email": "字符串类型,必需字段,标准邮箱格式,例如'user@example.com'"
  }},
  "hobby": ["字符串数组类型,非必需字段,包含用户的所有爱好,如未提及则完全不输出此字段"]
}}

【字段提取规则】
1. name: 从文本中识别用户姓名,必需提取
2. age: 识别年龄信息,转换为"数字+岁"格式,必需提取
3. email: 识别邮箱地址,保持原始格式,必需提取
4. hobby: 识别用户爱好,以字符串数组形式输出,如未提及爱好信息则完全省略hobby字段

【参考示例】
示例1(包含爱好):
Q:我叫张三,今年25岁,邮箱是zhangsan@example.com,爱好是唱歌
A:{example1_response}

示例2(包含多个爱好):
Q:我叫李四,今年30岁,邮箱是lisi@example.com,平时喜欢跳舞和游泳
A:{example2_response}

示例3(包含多个爱好):
Q:我的邮箱是wangwu@example.com,今年40岁,名字是王五,会Rap和打篮球
A:{example3_response}

请严格按照上述格式和规则提取信息并输出JSON。如果用户未提及爱好,则不要在输出中包含hobby字段。"""
        },
        {
            "role": "user",
            "content": "大家好,我叫刘五,今年34岁,邮箱是liuwu@example.com,平时喜欢打篮球和旅游", 
        },
    ]
response = dashscope.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", 
    messages=messages,
    result_format='message',
    response_format={'type': 'json_object'}
    )
json_string = response.output.choices[0].message.content
print(json_string)

返回结果

{
  "info": {
    "name": "刘五",
    "age": "34岁",
    "email": "liuwu@example.com"
  },
  "hobby": [
    "打篮球",
    "旅游"
  ]
}

Java

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;
import com.alibaba.dashscope.common.ResponseFormat;

public class Main {
    public static GenerationResult callWithMessage() throws ApiException, NoApiKeyException, InputRequiredException {
        Generation gen = new Generation();
        Message systemMsg = Message.builder()
                .role(Role.SYSTEM.getValue())
                .content("""
                请从用户输入中提取个人信息并按照指定的JSON Schema格式输出:

【输出格式要求】
输出必须严格遵循以下JSON结构:
{
  "info": {
    "name": "字符串类型,必需字段,用户姓名",
    "age": "字符串类型,必需字段,格式为'数字+岁',例如'25岁'",
    "email": "字符串类型,必需字段,标准邮箱格式,例如'user@example.com'"
  },
  "hobby": ["字符串数组类型,非必需字段,包含用户的所有爱好,如未提及则完全不输出此字段"]
}

【字段提取规则】
1. name: 从文本中识别用户姓名,必需提取
2. age: 识别年龄信息,转换为"数字+岁"格式,必需提取
3. email: 识别邮箱地址,保持原始格式,必需提取
4. hobby: 识别用户爱好,以字符串数组形式输出,如未提及爱好信息则完全省略hobby字段

【参考示例】
示例1(包含爱好):
Q:我叫张三,今年25岁,邮箱是zhangsan@example.com,爱好是唱歌
A:{"info":{"name":"张三","age":"25岁","email":"zhangsan@example.com"},"hobby":["唱歌"]}

示例2(包含多个爱好):
Q:我叫李四,今年30岁,邮箱是lisi@example.com,平时喜欢跳舞和游泳
A:{"info":{"name":"李四","age":"30岁","email":"lisi@example.com"},"hobby":["跳舞","游泳"]}

示例3(不包含爱好):
Q:我叫王五,我的邮箱是wangwu@example.com,今年40岁
A:{"info":{"name":"王五","age":"40岁","email":"wangwu@example.com"}}""")
                .build();
        Message userMsg = Message.builder()
                .role(Role.USER.getValue())
                .content("大家好,我叫刘五,今年34岁,邮箱是liuwu@example.com,平时喜欢打篮球和旅游")
                .build();
        ResponseFormat jsonMode = ResponseFormat.builder().type("json_object").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"))
                .model("qwen-plus")
                .messages(Arrays.asList(systemMsg, userMsg))
                .resultFormat(GenerationParam.ResultFormat.MESSAGE)
                .responseFormat(jsonMode)
                .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) {
            // 使用日志框架记录异常信息
            System.err.println("An error occurred while calling the generation service: " + e.getMessage());
        }
        System.exit(0);
    }
}

返回结果

{
  "info": {
    "name": "刘五",
    "age": "34岁",
    "email": "liuwu@example.com"
  },
  "hobby": [
    "打篮球",
    "旅游"
  ]
}

应用于生产环境

  • 有效性校验

    当前暂不支持根据给定的 JSON Schema 生成 JSON 字符串。传递给下游业务前,建议使用工具对其进行有效性校验,如 jsonschema (Python)、Ajv (JavaScript)、Everit (Java)等确保其符合指定的 JSON Schema 要求,避免因字段缺失、类型错误或格式不规范导致下游系统解析失败、数据丢失或业务逻辑中断。失败时可通过重试、大模型改写等策略进行修复。

  • 禁用 max_tokens

    请勿在开启结构化输出时指定 max_tokens(控制模型输出 Token 数的参数,默认值为模型最大输出 Token 数),否则返回的 JSON 字符串可能不完整,导致下游业务解析失败。

常见问题

Q: 是否支持根据给定的 JSON Schema 生成数据?

A:通义千问 API 支持根据提示词生成有效的 JSON 字符串,但暂时无法根据提供的 JSON Schema 生成。建议在提示词中通过自然语言明确描述所需 JSON 的键值结构和数据类型,并提供标准数据样例,可帮助大模型生成符合预期 Schema 的数据。

错误码

如果模型调用失败并返回报错信息,请参见错误信息进行解决。