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How to make Qwen generate a JSON string -
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Structured output (JSON mode) makes the model return a valid JSON string that your code can parse directly, without extra text like json that breaks downstream parsing.
Usage
To enable structured output, set response_format in your request with two requirements:
Set the
response_formatparameter to{"type": "json_object"}in the request body.Include the word "JSON" (case-insensitive) in your system message or user message. Without it, the API returns:
'messages' must contain the word 'json' in some form, to use 'response_format' of type 'json_object'.
Supported models
Qwen
Kimi
GLM
- Text generation models
Qwen-Max (non-thinking mode): Qwen3.6-Max series, Qwen3-Max series, Qwen-Max series
Qwen-Plus (non-thinking mode): Qwen3.7-Plus series, Qwen3.6-Plus series, Qwen3.5-Plus series, Qwen-Plus series
Qwen-Flash (non-thinking mode): Qwen3.6-Flash series, Qwen3.5-Flash series, Qwen-Flash series
Qwen-Turbo (non-thinking mode): Qwen-Turbo series
Qwen-Coder: Qwen3-Coder series
Qwen-Long: Qwen-Long series
Qwen3.6 open-source series (non-thinking mode)
Qwen3.5 open-source series (non-thinking mode)
Qwen3 open-source series (non-thinking mode)
Qwen3-Coder open-source series
Qwen2.5 open-source series (excluding math and coder models)
- Multimodal models
Qwen-VL (non-thinking mode): Qwen3-VL-Plus series, Qwen3-VL-Flash series, Qwen-VL-Max series (excluding the latest and snapshot versions), Qwen-VL-Plus series (excluding the latest and snapshot versions)
Qwen-Omni: Qwen3.5-Omni-Plus series, Qwen3.5-Omni-Flash series
Qwen3-VL open-source series (non-thinking mode)
Note
Models in thinking mode do not currently support structured output.
kimi-k2-thinking
Non-thinking mode: glm-5.1, glm-5, glm-4.7, glm-4.6
Getting started
This example extracts structured information from a personal profile.
Obtain an API key and export the API key as an environment variable. If you use the OpenAI SDK or DashScope SDK to make calls, install the SDK.
OpenAI compatible
DashScope
Python
Node.js
curl
python
from openai import OpenAI
import os
client = OpenAI(
# API keys differ by region. If you haven't configured an environment variable, replace the next line with: api_key="sk-xxx"
api_key=os.getenv("DASHSCOPE_API_KEY"),
# If you use Beijing region models, replace base_url with: https://dashscope.aliyuncs.com/compatible-mode/v1
base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
model="qwen-flash",
messages=[\
{\
"role": "system",\
"content": "Extract the user's name and age, and return them in JSON format"\
},\
{\
"role": "user",\
"content": "Hi everyone, my name is Alex Brown, I'm 34 years old, my email is alexbrown@example.com, and I enjoy playing basketball and traveling",\
},\
],
response_format={"type": "json_object"}
)
json_string = completion.choices[0].message.content
print(json_string)Response
plaintext
{
"Name": "Alex Brown",
"Age": 34
}nodejs
import OpenAI from "openai";
const openai = new OpenAI({
// If you haven't configured an environment variable, replace the next line with: apiKey: "sk-xxx"
apiKey: process.env.DASHSCOPE_API_KEY,
// For Beijing region models, replace baseURL with: https://dashscope.aliyuncs.com/compatible-mode/v1
baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
});
const completion = await openai.chat.completions.create({
model: "qwen-flash",
messages: [\
{\
role: "system",\
content: "Extract the user's name and age, and return them in JSON format"\
},\
{\
role: "user",\
content: "Hi everyone, my name is Alex Brown, I'm 34 years old, my email is alexbrown@example.com, and I enjoy playing basketball and traveling"\
}\
],
response_format: {
type: "json_object"
}
});
const jsonString = completion.choices[0].message.content;
console.log(jsonString);Response
plaintext
{
"name": "Alex Brown",
"age": 34
}curl
# ======= Important =======
# API keys differ by region. To obtain an API key, visit: https://www.alibabacloud.com/help/en/model-studio/get-api-key
# If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
# === Delete this comment before execution ===
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.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 need to extract the name (string), age (string), and email (string). Output the result as a JSON string. Do not include any other irrelevant content.\nExamples:\nQ: My name is Alice, I am 25 years old, and my email is alice@example.com\nA: {\"name\":\"Alice\",\"age\":\"25 years old\",\"email\":\"alice@example.com\"}\nQ: My name is Bob, I am 30 years old, and my email is bob@example.com\nA: {\"name\":\"Bob\",\"age\":\"30 years old\",\"email\":\"bob@example.com\"}\nQ: My name is Charlie, my email is charlie@example.com, and I am 40 years old\nA: {\"name\":\"Charlie\",\"age\":\"40 years old\",\"email\":\"charlie@example.com\"}"\
},\
{\
"role": "user",\
"content": "Hello everyone, my name is Alex Brown, I am 34 years old, and my email is alexbrown@example.com"\
}\
],
"response_format": {
"type": "json_object"
}
}'Response
json
{
"choices": [\
{\
"message": {\
"role": "assistant",\
"content": "{\"name\":\"Alex Brown\",\"age\":\"34 years old\"}"\
},\
"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"
}Python
Java
curl
python
import os
import dashscope
# For Beijing region models, replace the URL with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'
messages=[\
{\
"role": "system",\
"content": "Extract the user's name and age, and return them in JSON format"\
},\
{\
"role": "user",\
"content": "Hi everyone, my name is Alex Brown, I'm 34 years old, my email is alexbrown@example.com, and I enjoy playing basketball and traveling",\
},\
]
response = dashscope.Generation.call(
# If you haven't configured an environment variable, replace the next line with: api_key="sk-xxx" (Alibaba Cloud Model Studio API key),
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)Response
plaintext
{
"name": "Alex Brown",
"age": 34
}DashScope Java SDK version must be 2.18.4 or higher.
java
// DashScope Java SDK version must be 2.18.4 or higher
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;
import com.alibaba.dashscope.protocol.Protocol;
public class Main {
public static GenerationResult callWithMessage() throws ApiException, NoApiKeyException, InputRequiredException {
// For Beijing region models, replace the URL with: https://dashscope.aliyuncs.com/api/v1
Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1");
Message systemMsg = Message.builder()
.role(Role.SYSTEM.getValue())
.content("Extract the user's name and age, and return them in JSON format")
.build();
Message userMsg = Message.builder()
.role(Role.USER.getValue())
.content("Hi everyone, my name is Alex Brown, I'm 34 years old, my email is alexbrown@example.com, and I enjoy playing basketball and traveling")
.build();
ResponseFormat jsonMode = ResponseFormat.builder().type("json_object").build();
GenerationParam param = GenerationParam.builder()
// If you haven't configured an environment variable, replace the next line with: .apiKey("sk-xxx") (Alibaba Cloud Model Studio API key)
.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) {
// Log the exception using a logging framework
System.err.println("An error occurred while calling the generation service: " + e.getMessage());
}
}
}Response
plaintext
{
"name": "Alex Brown",
"age": 34
}curl
# ======= Important notes =======
# For Beijing region models, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation
# API keys differ by region. Get an API key: https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Delete this comment before running ===
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.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": "Extract the user'\''s name and age, and return them in JSON format"\
},\
{\
"role": "user",\
"content": "Hi everyone, my name is Alex Brown, I'\''m 34 years old, my email is alexbrown@example.com, and I enjoy playing basketball and traveling"\
}\
]
},
"parameters": {
"result_format": "message",
"response_format": {
"type": "json_object"
}
}
}'Response
json
{
"name": "Alex Brown",
"age": 34
}Image and video data processing
Multimodal models also support structured output for images and videos. Use JSON mode to extract structured data from visual content, such as field values from receipts, object locations in images, or events in video.
For image and video file limits, see Image and video understanding .
OpenAI compatible
DashScope
Python
Node.js
curl
python
import os
from openai import OpenAI
client = OpenAI(
# API keys differ by region. Get an API key: https://www.alibabacloud.com/help/en/model-studio/get-api-key
api_key=os.getenv("DASHSCOPE_API_KEY"),
# For Beijing region models, replace base_url with: https://dashscope.aliyuncs.com/compatible-mode/v1
base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
)
completion = client.chat.completions.create(
model="qwen3-vl-plus",
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": "Extract ticket (array type, including travel_date, trains, seat_num, arrival_site, price) and invoice information (array type, including invoice_code and invoice_number) from the image. Output a JSON containing both ticket and invoice arrays"},\
],\
},\
],
response_format={"type": "json_object"}
)
json_string = completion.choices[0].message.content
print(json_string)Response
plaintext
{
"ticket": [\
{\
"travel_date": "2013-06-29",\
"trains": "stream",\
"seat_num": "371",\
"arrival_site": "Development Zone",\
"price": "8.00"\
}\
],
"invoice": [\
{\
"invoice_code": "221021325353",\
"invoice_number": "10283819"\
}\
]
}nodejs
import OpenAI from "openai";
const openai = new OpenAI({
// API keys differ by region. Get an API key: https://www.alibabacloud.com/help/en/model-studio/get-api-key
// If you haven't configured an environment variable, replace the next line with: apiKey: "sk-xxx" (Model Studio API key)
apiKey: process.env.DASHSCOPE_API_KEY,
// For Beijing region models, replace base_url with https://dashscope.aliyuncs.com/compatible-mode/v1
baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
});
async function main() {
const response = await openai.chat.completions.create({
model: "qwen3-vl-plus",
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: "Extract ticket (array type, including travel_date, trains, seat_num, arrival_site, price) and invoice information (array type, including invoice_code and invoice_number) from the image. Output a JSON containing both ticket and invoice arrays"\
}\
]\
}\
],
response_format: {type: "json_object"}
});
console.log(response.choices[0].message.content);
}
main()Response
plaintext
{
"ticket": [\
{\
"travel_date": "2013-06-29",\
"trains": "stream",\
"seat_num": "371",\
"arrival_site": "Development Zone",\
"price": "8.00"\
}\
],
"invoice": [\
{\
"invoice_code": "221021325353",\
"invoice_number": "10283819"\
}\
]
}curl
# ======= Important notes =======
# For Beijing region models, replace base_url with: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
# API keys differ by region. Get an API key: https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Delete this comment before running ===
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
"model": "qwen3-vl-plus",
"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": "Extract ticket (array type, including travel_date, trains, seat_num, arrival_site, price) and invoice information (array type, including invoice_code and invoice_number) from the image. Output a JSON containing both ticket and invoice arrays"}\
]\
}],
"response_format":{"type": "json_object"}
}'Response
json
{
"ticket": [\
{\
"travel_date": "2013-06-29",\
"trains": "stream",\
"seat_num": "371",\
"arrival_site": "Development Zone",\
"price": "8.00"\
}\
],
"invoice": [\
{\
"invoice_code": "221021325353",\
"invoice_number": "10283819"\
}\
]
}Python
Java
curl
python
import os
import dashscope
# For Beijing region models, replace the URL with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'
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": "Extract ticket (array type, including travel_date, trains, seat_num, arrival_site, price) and invoice information (array type, including invoice_code and invoice_number) from the image. Output a JSON containing both ticket and invoice arrays"}]\
}]
response = dashscope.MultiModalConversation.call(
# If you haven't configured an environment variable, replace the next line with: api_key ="sk-xxx" (Model Studio API key)
api_key = os.getenv('DASHSCOPE_API_KEY'),
model = 'qwen3-vl-plus',
messages = messages,
response_format={'type': 'json_object'}
)
json_string = response.output.choices[0].message.content[0]["text"]
print(json_string)Response
plaintext
{
"ticket": [\
{\
"travel_date": "2013-06-29",\
"trains": "Liushui",\
"seat_num": "371",\
"arrival_site": "Development Zone",\
"price": "8.00"\
}\
],
"invoice": [\
{\
"invoice_code": "221021325353",\
"invoice_number": "10283819"\
}\
]
}java
// DashScope Java SDK version must be 2.21.4 or higher
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;
import com.alibaba.dashscope.utils.Constants;
public class Main {
// For Beijing region models, replace the URL with: https://dashscope.aliyuncs.com/api/v1
static {
Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1";
}
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", "Extract ticket (array type, including travel_date, trains, seat_num, arrival_site, price) and invoice information (array type, including invoice_code and invoice_number) from the image. Output a JSON containing both ticket and invoice arrays"))).build();
ResponseFormat jsonMode = ResponseFormat.builder().type("json_object").build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
// If you haven't configured an environment variable, replace the next line with: .apiKey("sk-xxx") (Model Studio API key)
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
.model("qwen3-vl-plus")
.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());
}
}
}Response
plaintext
{
"ticket": [\
{\
"travel_date": "2013-06-29",\
"trains": "stream",\
"seat_num": "371",\
"arrival_site": "Development Zone",\
"price": "8.00"\
}\
],
"invoice": [\
{\
"invoice_code": "221021325353",\
"invoice_number": "10283819"\
}\
]
}curl
# ======= Important notes =======
# For Beijing region models, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
# API keys differ by region. Get an API key: https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Delete this comment before running ===
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "qwen3-vl-plus",
"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": "Extract ticket (array type, including travel_date, trains, seat_num, arrival_site, price) and invoice information (array type, including invoice_code and invoice_number) from the image. Output a JSON containing both ticket and invoice arrays"\
}\
]\
}\
]
},
"parameters": {
"response_format": {
"type": "json_object"
}
}
}'Response
json
{
"output": {
"choices": [\
{\
"message": {\
"content": [\
{\
"text": "{\n \"ticket\": [\n {\n \"travel_date\": \"2013-06-29\",\n \"trains\": \"train number\",\n \"seat_num\": \"371\",\n \"arrival_site\": \"Development Zone\",\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"\
}\
]
},
"usage": {
"total_tokens": 598,
"input_tokens_details": {
"image_tokens": 418,
"text_tokens": 68
},
"output_tokens": 112,
"input_tokens": 486,
"output_tokens_details": {
"text_tokens": 112
},
"image_tokens": 418
},
"request_id": "b129dce1-0d5d-4772-b8b5-bd3a1d5cde63"
}Optimize prompts
Ambiguous prompts like "return user information" lead to unpredictable output structures. For reliable results, describe the expected schema in your prompt: specify field names, types, required vs. optional status, format constraints (such as date format), and include examples.
OpenAI compatible
DashScope
Python
Node.js
python
from openai import OpenAI
import os
import json
import textwrap # Handles indentation for multi-line strings to improve code readability
# Predefined example responses to show the model the expected output format
# Example 1: Complete response with all fields
example1_response = json.dumps(
{
"info": {"name": "Alice", "age": "25 years old", "email": "alice@example.com"},
"hobby": ["singing"]
},
ensure_ascii=False
)
# Example 2: Response with multiple hobbies
example2_response = json.dumps(
{
"info": {"name": "Bob", "age": "30 years old", "email": "bob@example.com"},
"hobby": ["dancing", "swimming"]
},
ensure_ascii=False
)
# Example 3: Response without hobby field (hobby is optional)
example3_response = json.dumps(
{
"info": {"name": "Dave", "age": "28 years old", "email": "dave@example.com"}
},
ensure_ascii=False
)
# Example 4: Another response without hobby field
example4_response = json.dumps(
{
"info": {"name": "Sun Qi", "age": "35 years old", "email": "sunqi@example.com"}
},
ensure_ascii=False
)
# Initialize the OpenAI client
client = OpenAI(
# If you haven't configured an environment variable, replace the next line with: api_key="sk-xxx"
# API keys differ by region. Get an API key: https://www.alibabacloud.com/help/en/model-studio/get-api-key
api_key=os.getenv("DASHSCOPE_API_KEY"),
# This is the Beijing region base_url. If you use Singapore region models, replace base_url with: https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
# dedent removes common leading whitespace from each line, allowing the string to be indented nicely in code without including extra spaces at runtime
system_prompt = textwrap.dedent(f"""\
Extract personal information from the user input and output it in the specified JSON Schema format:
[Output format requirements]
The output must strictly follow this JSON structure:
{{
"info": {{
"name": "string type, required field, user's name",
"age": "string type, required field, format 'number years old', e.g., '25 years old'",
"email": "string type, required field, standard email format, e.g., 'user@example.com'"
}},
"hobby": ["string array type, optional field, contains all user hobbies; omit entirely if not mentioned"]
}}
[Field extraction rules]
1. name: Identify the user's name from the text, must extract
2. age: Identify age information, convert to 'number years old' format, must extract
3. email: Identify email address, keep original format, must extract
4. hobby: Identify user hobbies, output as string array; omit hobby field entirely if hobbies are not mentioned
[Reference examples]
Example 1 (with hobby):
Q: My name is Alice, I'm 25 years old, my email is alice@example.com, and my hobby is singing
A: {example1_response}
Example 2 (with multiple hobbies):
Q: My name is Bob, I'm 30 years old, my email is bob@example.com, and I enjoy dancing and swimming
A: {example2_response}
Example 3 (without hobby):
Q: My name is Dave, I'm 28 years old, and my email is dave@example.com
A: {example3_response}
Example 4 (without hobby):
Q: I'm Sun Qi, 35 years old, and my email is sunqi@example.com
A: {example4_response}
Extract information and output JSON strictly according to the above format and rules. Do not include the hobby field if the user doesn't mention hobbies.\
""")
# Call the model API for information extraction
completion = client.chat.completions.create(
model="qwen-plus",
messages=[\
{\
"role": "system",\
"content": system_prompt\
},\
{\
"role": "user",\
"content": "Hi everyone, my name is Alex Brown, I'm 34 years old, my email is alexbrown@example.com, and I enjoy playing basketball and traveling",\
},\
],
response_format={"type": "json_object"}, # Specify JSON format return
)
# Extract and print the model-generated JSON result
json_string = completion.choices[0].message.content
print(json_string)Response
plaintext
{
"info": {
"name": "Alex Brown",
"age": "34 years old",
"email": "alexbrown@example.com"
},
"hobby": ["Basketball", "Traveling"]
}nodejs
import OpenAI from "openai";
// Predefined example responses (to show the model the expected output format)
// Example 1: Complete response with all fields
const example1Response = JSON.stringify({
info: { name: "Alice", age: "25 years old", email: "alice@example.com" },
hobby: ["singing"]
}, null, 2);
// Example 2: Response with multiple hobbies
const example2Response = JSON.stringify({
info: { name: "Bob", age: "30 years old", email: "bob@example.com" },
hobby: ["dancing", "swimming"]
}, null, 2);
// Example 3: Response without hobby field (hobby is optional)
const example3Response = JSON.stringify({
info: { name: "Dave", age: "28 years old", email: "dave@example.com" }
}, null, 2);
// Example 4: Another response without hobby field
const example4Response = JSON.stringify({
info: { name: "Sun Qi", age: "35 years old", email: "sunqi@example.com" }
}, null, 2);
// Initialize OpenAI client configuration
const openai = new OpenAI({
// If you haven't configured an environment variable, replace the next line with: apiKey: "sk-xxx" (Alibaba Cloud Model Studio API key),
// API keys differ by region. Get an API key: https://www.alibabacloud.com/help/en/model-studio/get-api-key
apiKey: process.env.DASHSCOPE_API_KEY,
// This is the Beijing region base_url. If you use Singapore region models, replace base_url with: https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1
baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1"
});
// Create chat completion request using structured prompts to improve output accuracy
const completion = await openai.chat.completions.create({
model: "qwen-plus",
messages: [\
{\
role: "system",\
content: `Extract personal information from the user input and output it in the specified JSON Schema format:\
\
[Output format requirements]\
The output must strictly follow this JSON structure:\
{\
"info": {\
"name": "string type, required field, user's name",\
"age": "string type, required field, format 'number years old', e.g., '25 years old'",\
"email": "string type, required field, standard email format, e.g., 'user@example.com'"\
},\
"hobby": ["string array type, optional field, contains all user hobbies; omit entirely if not mentioned"]\
}\
\
[Field extraction rules]\
1. name: Identify the user's name from the text, must extract\
2. age: Identify age information, convert to 'number years old' format, must extract\
3. email: Identify email address, keep original format, must extract\
4. hobby: Identify user hobbies, output as string array; omit hobby field entirely if hobbies are not mentioned\
\
[Reference examples]\
Example 1 (with hobby):\
Q: My name is Alice, I'm 25 years old, my email is alice@example.com, and my hobby is singing\
A: ${example1Response}\
\
Example 2 (with multiple hobbies):\
Q: My name is Bob, I'm 30 years old, my email is bob@example.com, and I enjoy dancing and swimming\
A: ${example2Response}\
\
Example 3 (without hobby):\
Q: My name is Dave, I'm 28 years old, and my email is dave@example.com\
A: ${example3Response}\
\
Example 4 (without hobby):\
Q: I'm Sun Qi, 35 years old, and my email is sunqi@example.com\
A: ${example4Response}\
\
Extract information and output JSON strictly according to the above format and rules. Do not include the hobby field if the user doesn't mention hobbies.`\
},\
{\
role: "user",\
content: "Hi everyone, my name is Alex Brown, I'm 34 years old, my email is alexbrown@example.com, and I enjoy playing basketball and traveling"\
}\
],
response_format: {
type: "json_object"
}
});
// Extract and print the model-generated JSON result
const jsonString = completion.choices[0].message.content;
console.log(jsonString);Response
plaintext
{
"info": {
"name": "Alex Brown",
"age": "34 years old",
"email": "alexbrown@example.com"
},
"hobby": [\
"playing basketball",\
"traveling"\
]
}Python
Java
python
import os
import json
import dashscope
# If you use Singapore region models, uncomment the following line
# dashscope.base_http_api_url = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1"
# Predefined example responses (to show the model the expected output format)
example1_response = json.dumps(
{
"info": {"name": "Alice", "age": "25 years old", "email": "alice@example.com"},
"hobby": ["singing"]
},
ensure_ascii=False
)
example2_response = json.dumps(
{
"info": {"name": "Bob", "age": "30 years old", "email": "bob@example.com"},
"hobby": ["dancing", "swimming"]
},
ensure_ascii=False
)
example3_response = json.dumps(
{
"info": {"name": "Charlie", "age": "40 years old", "email": "charlie@example.com"},
"hobby": ["Rap", "basketball"]
},
ensure_ascii=False
)
messages=[\
{\
"role": "system",\
"content": f"""Extract personal information from the user input and output it in the specified JSON Schema format:\
\
[Output format requirements]\
The output must strictly follow this JSON structure:\
{{\
"info": {{\
"name": "string type, required field, user's name",\
"age": "string type, required field, format 'number years old', e.g., '25 years old'",\
"email": "string type, required field, standard email format, e.g., 'user@example.com'"\
}},\
"hobby": ["string array type, optional field, contains all user hobbies; omit entirely if not mentioned"]\
}}\
\
[Field extraction rules]\
1. name: Identify the user's name from the text, must extract\
2. age: Identify age information, convert to 'number years old' format, must extract\
3. email: Identify email address, keep original format, must extract\
4. hobby: Identify user hobbies, output as string array; omit hobby field entirely if hobbies are not mentioned\
\
[Reference examples]\
Example 1 (with hobby):\
Q: My name is Alice, I'm 25 years old, my email is alice@example.com, and my hobby is singing\
A: {example1_response}\
\
Example 2 (with multiple hobbies):\
Q: My name is Bob, I'm 30 years old, my email is bob@example.com, and I enjoy dancing and swimming\
A: {example2_response}\
\
Example 3 (with multiple hobbies):\
Q: My email is charlie@example.com, I'm 40 years old, my name is Charlie, and I can Rap and play basketball\
A: {example3_response}\
\
Extract information and output JSON strictly according to the above format and rules. Do not include the hobby field if the user doesn't mention hobbies."""\
},\
{\
"role": "user",\
"content": "Hi everyone, my name is Alex Brown, I'm 34 years old, my email is alexbrown@example.com, and I enjoy playing basketball and traveling",\
},\
]
response = dashscope.Generation.call(
# If you haven't configured an environment variable, replace the next line with: api_key="sk-xxx" (Alibaba Cloud Model Studio API key),
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)Response
plaintext
{
"info": {
"name": "Alex Brown",
"age": "34 years old",
"email": "alexbrown@example.com"
},
"hobby": [\
"playing basketball",\
"traveling"\
]
}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;
import com.alibaba.dashscope.protocol.Protocol;
public class Main {
public static GenerationResult callWithMessage() throws ApiException, NoApiKeyException, InputRequiredException {
// If you use a model in the Beijing region, you must replace the URL with: https://dashscope.aliyuncs.com/api/v1
Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1");
Message systemMsg = Message.builder()
.role(Role.SYSTEM.getValue())
.content("""
Extract personal information from the user input and output it in the specified JSON Schema format:
[Output Format Requirements]
The output must strictly follow the JSON structure below:
{
"info": {
"name": "String type, required field, user's name",
"age": "String type, required field, in the format of 'Number years old', for example, '25 years old'",
"email": "String type, required field, standard email format, for example, 'user@example.com'"
},
"hobby": ["String array type, optional field, contains all of the user's hobbies. If no hobbies are mentioned, do not include this field in the output."]
}
[Field Extraction Rules]
1. name: Identify the user's name from the text. This is a required field.
2. age: Identify the age information and transform it into the 'Number years old' format. This is a required field.
3. email: Identify the email address and keep its original format. This is a required field.
4. hobby: Identify the user's hobbies and output them as a string array. If no hobbies are mentioned, completely omit the hobby field.
[Examples]
Example 1 (with a hobby):
Q: My name is Alice, I am 25 years old, my email is alice@example.com, and my hobby is singing.
A: {"info":{"name":"Alice","age":"25 years old","email":"alice@example.com"},"hobby":["singing"]}
Example 2 (with multiple hobbies):
Q: My name is Bob, I am 30 years old, my email is bob@example.com, and I like dancing and swimming.
A: {"info":{"name":"Bob","age":"30 years old","email":"bob@example.com"},"hobby":["dancing","swimming"]}
Example 3 (without hobbies):
Q: My name is Charlie, my email is charlie@example.com, and I am 40 years old.
A: {"info":{"name":"Charlie","age":"40 years old","email":"charlie@example.com"}}""")
.build();
Message userMsg = Message.builder()
.role(Role.USER.getValue())
.content("Hello everyone, my name is Alex Brown, I am 34 years old, my email is alexbrown@example.com, and I enjoy playing basketball and traveling.")
.build();
ResponseFormat jsonMode = ResponseFormat.builder().type("json_object").build();
GenerationParam param = GenerationParam.builder()
// If you use a model in the Beijing region, you need to use an API key for the Beijing region. Obtain the key from: https://bailian.console.alibabacloud.com/?tab=model#/api-key
// If you have not configured environment variables, replace the following line with your Alibaba Cloud Model Studio API key: .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) {
// Use a logging framework to record the exception.
System.err.println("An error occurred while calling the generation service: " + e.getMessage());
}
}
}Response
plaintext
{
"info": {
"name": "Alex Brown",
"age": "34 years old",
"email": "alexbrown@example.com"
},
"hobby": [\
"Playing basketball",\
"Traveling"\
]
}Going live
- Validate before passing downstream
Always validate the JSON output before passing it to downstream services. Use a library such as jsonschema (Python), Ajv (JavaScript), or Everit (Java) to check for missing fields, type errors, or format issues. If validation fails, retry the request or use a second model to fix the output.
- Do not set max_tokens
Do not set max_tokens when structured output is enabled. This parameter caps the number of output tokens and defaults to the model's maximum. Setting it may truncate the JSON string mid-output, producing invalid JSON that fails to parse.
FAQ
Q: How does Qwen's thinking mode model produce structured output?
Qwen's thinking mode models do not support structured output directly. To get a valid JSON string from a thinking mode model, use a two-step approach: first call the thinking model to get high-quality output, then pass any malformed JSON through a model that supports JSON mode to fix it.
- Get output from the thinking mode model
Call the thinking mode model. The result may not be valid JSON.
Do not set the
response_formatparameter to{"type": "json_object"}when enabling thinking mode, or you will encounter an error.
python
completion = client.chat.completions.create(
model="qwen-plus",
messages=[\
{"role": "system", "content": system_prompt},\
{\
"role": "user",\
"content": "Hi everyone, my name is Alex Brown, I'm 34 years old, my email is alexbrown@example.com, and I enjoy playing basketball and traveling",\
},\
],
# Enable thinking mode; do not set response_format parameter to {"type": "json_object"}, or you will get an error
extra_body={"enable_thinking": True},
# Streaming output is required in thinking mode
stream=True
)
# Extract and print the model-generated JSON result
json_string = ""
for chunk in completion:
if chunk.choices[0].delta.content is not None:
json_string += chunk.choices[0].delta.content- Validate and fix the output
Try to parse the json_string from the previous step:
If the model returned valid JSON, parse and use it directly.
If the model returned invalid JSON, call a model that supports structured output (a fast, low-cost model such as qwen-flash in non-thinking mode works well) to fix the format.
python
import json
from openai import OpenAI
import os
# Initialize the OpenAI client (if the client variable isn't defined in the previous code block, uncomment the lines below)
# client = OpenAI(
# api_key=os.getenv("DASHSCOPE_API_KEY"),
# base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
# )
try:
json_object_from_thinking_model = json.loads(json_string)
print("Generated standard JSON string")
except json.JSONDecodeError:
print("Did not generate standard JSON string; fixing with a model that supports structured output")
completion = client.chat.completions.create(
model="qwen-flash",
messages=[\
{\
"role": "system",\
"content": "You are a JSON format expert. Fix the user's JSON string to standard format",\
},\
{\
"role": "user",\
"content": json_string,\
},\
],
response_format={"type": "json_object"},
)
json_object_from_thinking_model = json.loads(completion.choices[0].message.content)Error codes
If the model call fails and returns an error message, see Error codes for resolution.
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