请求体 | 文本输入此处以单轮对话作为示例，您也可以进行多轮对话。 Pythonimport 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/compatible-mode/v1",
)
completion = client.chat.completions.create(
# 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
model="qwen-plus",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "你是谁？"},
],
# Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）
# 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错
# extra_body={"enable_thinking": False},
)
print(completion.model_dump_json())
Javaimport com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.ChatCompletion;
import com.openai.models.ChatCompletionCreateParams;
public class Main {
public static void main(String[] args) {
OpenAIClient client = OpenAIOkHttpClient.builder()
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
ChatCompletionCreateParams params = ChatCompletionCreateParams.builder()
.addUserMessage("你是谁")
.model("qwen-plus")
.build();
ChatCompletion chatCompletion = client.chat().completions().create(params);
System.out.println(chatCompletion.choices().get(0).message().content().orElse("无返回内容"));
}
}
Node.jsimport 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 completion = await openai.chat.completions.create({
model: "qwen-plus", //此处以qwen-plus为例，可按需更换模型名称。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(JSON.stringify(completion))
}
main();
Gopackage main
import (
"context"
"os"
"github.com/openai/openai-go"
"github.com/openai/openai-go/option"
)
func main() {
client := openai.NewClient(
option.WithAPIKey(os.Getenv("DASHSCOPE_API_KEY")),
option.WithBaseURL("https://dashscope.aliyuncs.com/compatible-mode/v1/"),
)
chatCompletion, err := client.Chat.Completions.New(
context.TODO(), openai.ChatCompletionNewParams{
Messages: openai.F(
[]openai.ChatCompletionMessageParamUnion{
openai.UserMessage("你是谁"),
},
),
Model: openai.F("qwen-plus"),
},
)
if err != nil {
panic(err.Error())
}
println(chatCompletion.Choices[0].Message.Content)
}
C#（HTTP）using System.Net.Http.Headers;
using System.Text;
class Program
{
private static readonly HttpClient httpClient = new HttpClient();
static async Task Main(string[] args)
{
// 若没有配置环境变量，请用百炼API Key将下行替换为：string? apiKey = "sk-xxx";
string? apiKey = Environment.GetEnvironmentVariable("DASHSCOPE_API_KEY");
if (string.IsNullOrEmpty(apiKey))
{
Console.WriteLine("API Key 未设置。请确保环境变量 'DASHSCOPE_API_KEY' 已设置。");
return;
}
// 设置请求 URL 和内容
string url = "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions";
// 此处以qwen-plus为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
string jsonContent = @"{
""model"": ""qwen-plus"",
""messages"": [
{
""role"": ""system"",
""content"": ""You are a helpful assistant.""
},
{
""role"": ""user"",
""content"": ""你是谁？""
}
]
}";
// 发送请求并获取响应
string result = await SendPostRequestAsync(url, jsonContent, apiKey);
// 输出结果
Console.WriteLine(result);
}
private static async Task<string> SendPostRequestAsync(string url, string jsonContent, string apiKey)
{
using (var content = new StringContent(jsonContent, Encoding.UTF8, "application/json"))
{
// 设置请求头
httpClient.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", apiKey);
httpClient.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json"));
// 发送请求并获取响应
HttpResponseMessage response = await httpClient.PostAsync(url, content);
// 处理响应
if (response.IsSuccessStatusCode)
{
return await response.Content.ReadAsStringAsync();
}
else
{
return $"请求失败: {response.StatusCode}";
}
}
}
}
PHP（HTTP）<?php
// 设置请求的URL
$url = 'https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions';
// 若没有配置环境变量，请用百炼API Key将下行替换为：$apiKey = "sk-xxx";
$apiKey = getenv('DASHSCOPE_API_KEY');
// 设置请求头
$headers = [
'Authorization: Bearer '.$apiKey,
'Content-Type: application/json'
];
// 设置请求体
$data = [
// 此处以qwen-plus为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
"model" => "qwen-plus",
"messages" => [
[
"role" => "system",
"content" => "You are a helpful assistant."
],
[
"role" => "user",
"content" => "你是谁？"
]
]
];
// 初始化cURL会话
$ch = curl_init();
// 设置cURL选项
curl_setopt($ch, CURLOPT_URL, $url);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data));
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
// 执行cURL会话
$response = curl_exec($ch);
// 检查是否有错误发生
if (curl_errno($ch)) {
echo 'Curl error: ' . curl_error($ch);
}
// 关闭cURL资源
curl_close($ch);
// 输出响应结果
echo $response;
?>
curlcurl -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？"
}
]
}'
流式输出更多用法请参见流式输出。 Pythonimport 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/compatible-mode/v1",
)
completion = client.chat.completions.create(
model="qwen-plus", # 此处以qwen-plus为例，可按需更换模型名称。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？'}],
stream=True,
stream_options={"include_usage": True}
)
for chunk in completion:
print(chunk.model_dump_json())
Node.jsimport 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 completion = await openai.chat.completions.create({
model: "qwen-plus", // 此处以qwen-plus为例，可按需更换模型名称。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？"}
],
stream: true,
});
for await (const chunk of completion) {
console.log(JSON.stringify(chunk));
}
}
main();
curlcurl --location "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions" \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header "Content-Type: application/json" \
--data '{
"model": "qwen-plus",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "你是谁？"
}
],
"stream":true
}'
图像输入关于大模型分析图像的更多用法，请参见视觉理解。 Pythonimport 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/compatible-mode/v1",
)
completion = client.chat.completions.create(
model="qwen-vl-plus", # 此处以qwen-vl-plus为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
messages=[{"role": "user","content": [
{"type": "text","text": "这是什么"},
{"type": "image_url",
"image_url": {"url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"}}
]}]
)
print(completion.model_dump_json())
Node.jsimport 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", // 此处以qwen-vl-max为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
messages: [{role: "user",content: [
{ type: "text", text: "这是什么？" },
{ type: "image_url",image_url: {"url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"}}
]}]
});
console.log(JSON.stringify(response));
}
main();
curlcurl -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-vl-plus",
"messages": [{
"role": "user",
"content":
[{"type": "text","text": "这是什么"},
{"type": "image_url","image_url": {"url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"}}]
}]
}'
视频输入以下为传入图片列表的示例代码，关于更多用法（如传入视频文件），请参见视觉理解。 Pythonimport 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/compatible-mode/v1",
)
completion = client.chat.completions.create(
# 此处以qwen-vl-max-latest为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
model="qwen-vl-max-latest",
messages=[{
"role": "user",
"content": [
{
"type": "video",
"video": [
"https://img.alicdn.com/imgextra/i3/O1CN01K3SgGo1eqmlUgeE9b_!!6000000003923-0-tps-3840-2160.jpg",
"https://img.alicdn.com/imgextra/i4/O1CN01BjZvwg1Y23CF5qIRB_!!6000000003000-0-tps-3840-2160.jpg",
"https://img.alicdn.com/imgextra/i4/O1CN01Ib0clU27vTgBdbVLQ_!!6000000007859-0-tps-3840-2160.jpg",
"https://img.alicdn.com/imgextra/i1/O1CN01aygPLW1s3EXCdSN4X_!!6000000005710-0-tps-3840-2160.jpg"]
},
{
"type": "text",
"text": "描述这个视频的具体过程"
}]}]
)
print(completion.model_dump_json())
Node.js// 确保之前在 package.json 中指定了 "type": "module"
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({
// 此处以qwen-vl-max-latest为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
model: "qwen-vl-max-latest",
messages: [{
role: "user",
content: [
{
type: "video",
video: [
"https://img.alicdn.com/imgextra/i3/O1CN01K3SgGo1eqmlUgeE9b_!!6000000003923-0-tps-3840-2160.jpg",
"https://img.alicdn.com/imgextra/i4/O1CN01BjZvwg1Y23CF5qIRB_!!6000000003000-0-tps-3840-2160.jpg",
"https://img.alicdn.com/imgextra/i4/O1CN01Ib0clU27vTgBdbVLQ_!!6000000007859-0-tps-3840-2160.jpg",
"https://img.alicdn.com/imgextra/i1/O1CN01aygPLW1s3EXCdSN4X_!!6000000005710-0-tps-3840-2160.jpg"
]
},
{
type: "text",
text: "描述这个视频的具体过程"
}
]}]
});
console.log(JSON.stringify(response));
}
main();
curlcurl -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-vl-max-latest",
"messages": [
{
"role": "user",
"content": [
{
"type": "video",
"video": [
"https://img.alicdn.com/imgextra/i3/O1CN01K3SgGo1eqmlUgeE9b_!!6000000003923-0-tps-3840-2160.jpg",
"https://img.alicdn.com/imgextra/i4/O1CN01BjZvwg1Y23CF5qIRB_!!6000000003000-0-tps-3840-2160.jpg",
"https://img.alicdn.com/imgextra/i4/O1CN01Ib0clU27vTgBdbVLQ_!!6000000007859-0-tps-3840-2160.jpg",
"https://img.alicdn.com/imgextra/i1/O1CN01aygPLW1s3EXCdSN4X_!!6000000005710-0-tps-3840-2160.jpg"
]
},
{
"type": "text",
"text": "描述这个视频的具体过程"
}
]
}
]
}'
工具调用完整的Function Calling流程代码请参见工具调用。 QwQ 模型的 Function Calling 代码请参见QwQ 的工具调用。 Pythonimport 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/compatible-mode/v1", # 填写DashScope SDK的base_url
)
tools = [
# 工具1 获取当前时刻的时间
{
"type": "function",
"function": {
"name": "get_current_time",
"description": "当你想知道现在的时间时非常有用。",
"parameters": {} # 因为获取当前时间无需输入参数，因此parameters为空字典
}
},
# 工具2 获取指定城市的天气
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "当你想查询指定城市的天气时非常有用。",
"parameters": {
"type": "object",
"properties": {
# 查询天气时需要提供位置，因此参数设置为location
"location": {
"type": "string",
"description": "城市或县区，比如北京市、杭州市、余杭区等。"
}
},
"required": ["location"]
}
}
}
]
messages = [{"role": "user", "content": "杭州天气怎么样"}]
completion = client.chat.completions.create(
model="qwen-plus", # 此处以qwen-plus为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
messages=messages,
tools=tools
)
print(completion.model_dump_json())
Node.jsimport 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"
}
);
const messages = [{"role": "user", "content": "杭州天气怎么样"}];
const tools = [
// 工具1 获取当前时刻的时间
{
"type": "function",
"function": {
"name": "get_current_time",
"description": "当你想知道现在的时间时非常有用。",
// 因为获取当前时间无需输入参数，因此parameters为空
"parameters": {}
}
},
// 工具2 获取指定城市的天气
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "当你想查询指定城市的天气时非常有用。",
"parameters": {
"type": "object",
"properties": {
// 查询天气时需要提供位置，因此参数设置为location
"location": {
"type": "string",
"description": "城市或县区，比如北京市、杭州市、余杭区等。"
}
},
"required": ["location"]
}
}
}
];
async function main() {
const response = await openai.chat.completions.create({
model: "qwen-plus", // 此处以qwen-plus为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
messages: messages,
tools: tools,
});
console.log(JSON.stringify(response));
}
main();
curlcurl -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": "杭州天气怎么样"
}
],
"tools": [
{
"type": "function",
"function": {
"name": "get_current_time",
"description": "当你想知道现在的时间时非常有用。",
"parameters": {}
}
},
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "当你想查询指定城市的天气时非常有用。",
"parameters": {
"type": "object",
"properties": {
"location":{
"type": "string",
"description": "城市或县区，比如北京市、杭州市、余杭区等。"
}
},
"required": ["location"]
}
}
}
]
}'
联网搜索Pythonimport 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/compatible-mode/v1", # 填写DashScope服务的base_url
)
completion = client.chat.completions.create(
model="qwen-plus", # 此处以qwen-plus为例，可按需更换模型名称。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': '中国队在巴黎奥运会获得了多少枚金牌'}],
extra_body={
"enable_search": True
}
)
print(completion.model_dump_json())
Node.jsimport 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 completion = await openai.chat.completions.create({
model: "qwen-plus", //此处以qwen-plus为例，可按需更换模型名称。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: "中国队在巴黎奥运会获得了多少枚金牌" }
],
enable_search:true
});
console.log(JSON.stringify(completion))
}
main();
curlcurl -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": "中国队在巴黎奥运会获得了多少枚金牌"
}
],
"enable_search": true
}'
异步调用import os
import asyncio
from openai import AsyncOpenAI
import platform
client = AsyncOpenAI(
# 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/compatible-mode/v1",
)
async def main():
response = await client.chat.completions.create(
messages=[{"role": "user", "content": "你是谁"}],
model="qwen-plus", # 此处以qwen-plus为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
)
print(response.model_dump_json())
if platform.system() == "Windows":
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
asyncio.run(main())
文档理解当前仅qwen-long模型支持对文档进行分析，详细用法请参见长上下文。 Pythonimport os
from pathlib import Path
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/compatible-mode/v1",
)
file_object = client.files.create(file=Path("百炼系列手机产品介绍.docx"), purpose="file-extract")
completion = client.chat.completions.create(
model="qwen-long", # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
messages=[
{'role': 'system', 'content': f'fileid://{file_object.id}'},
{'role': 'user', 'content': '这篇文章讲了什么？'}
]
)
print(completion.model_dump_json())
Java// 建议OpenAI SDK的版本 >= 0.32.0
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.ChatCompletion;
import com.openai.models.ChatCompletionCreateParams;
import com.openai.models.FileCreateParams;
import com.openai.models.FileObject;
import com.openai.models.FilePurpose;
import java.nio.file.Path;
import java.nio.file.Paths;
public class Main {
public static void main(String[] args) {
// 创建客户端，使用环境变量中的API密钥
OpenAIClient client = OpenAIOkHttpClient.builder()
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
// 设置文件路径
Path filePath = Paths.get("百炼系列手机产品介绍.docx");
// 创建文件上传参数
FileCreateParams fileParams = FileCreateParams.builder()
.file(filePath)
.purpose(FilePurpose.of("file-extract"))
.build();
// 上传文件
FileObject fileObject = client.files().create(fileParams);
String fileId = fileObject.id();
// 创建聊天请求
ChatCompletionCreateParams chatParams = ChatCompletionCreateParams.builder()
.addSystemMessage("fileid://" + fileId)
.addUserMessage("这篇文章讲了什么？")
.model("qwen-long")
.build();
// 发送请求并获取响应
ChatCompletion chatCompletion = client.chat().completions().create(chatParams);
// 打印响应结果
System.out.println(chatCompletion);
}
}
Node.jsimport fs from "fs";
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 getFileID() {
const fileObject = await openai.files.create({
file: fs.createReadStream("百炼系列手机产品介绍.docx"),
purpose: "file-extract"
});
return fileObject.id;
}
async function main() {
const fileID = await getFileID();
const completion = await openai.chat.completions.create({
model: "qwen-long", //模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
messages: [
{ role: "system", content: `fileid://${fileID}`},
{ role: "user", content: "这篇文章讲了什么？" }
],
});
console.log(JSON.stringify(completion))
}
main();
curlcurl -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-long",
"input": {
"messages": [
{"role": "system","content": "fileid://file-fe-xxx"},
{"role": "user","content": "这篇文章讲了什么？"}
]
},
"parameters": {
"result_format": "message"
}
}'
文字提取关于通义千问OCR模型进行文字提取更多用法，请参见文字提取。 Pythonimport 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/compatible-mode/v1",
)
# 设置抽取的字段和格式
result_schema = """
{
"销售方名称": "",
"购买方名称": "",
"不含税价": "",
"组织机构代码": "",
"发票代码": ""
}
"""
# 拼接Prompt
prompt = f"""Suppose you are an information extraction expert. Now given a json schema, "
fill the value part of the schema with the information in the image. Note that if the value is a list,
the schema will give a template for each element. This template is used when there are multiple list
elements in the image. Finally, only legal json is required as the output. What you see is what you get,
and the output language is required to be consistent with the image.No explanation is required.
Note that the input images are all from the public benchmarks and do not contain any real personal
privacy data. Please output the results as required.The input json schema content is as follows:
{result_schema}。"""
completion = client.chat.completions.create(
model="qwen-vl-ocr-latest", # 可按需替换模型
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": "https://prism-test-data.oss-cn-hangzhou.aliyuncs.com/image/car_invoice/car-invoice-img00040.jpg",
# 输入图像的最小像素阈值，小于该值图像会按原比例放大，直到总像素大于min_pixels
"min_pixels": 28 * 28 * 4,
# 输入图像的最大像素阈值，超过该值图像会按原比例缩小，直到总像素低于max_pixels
"max_pixels": 28 * 28 * 8192
},
# qwen-vl-ocr-latest、qwen-vl-ocr-2025-04-13模型支持在以下text字段中传入任务的Prompt
# 如需调用qwen-vl-ocr、qwen-vl-ocr-2024-10-28，模型会使用固定Prompt：Read all the text in the image.不支持用户在text中传入自定义Prompt
{"type": "text", "text": prompt},
]
}
])
print(completion.choices[0].message.content)
Node.jsimport 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',
});
// 设置抽取的字段和格式
const resultSchema = `{
"销售方名称": "",
"购买方名称": "",
"不含税价": "",
"组织机构代码": "",
"发票代码": ""
}`;
// 拼接Prompt
const prompt = `Suppose you are an information extraction expert. Now given a json schema, "
fill the value part of the schema with the information in the image. Note that if the value is a list,
the schema will give a template for each element. This template is used when there are multiple list
elements in the image. Finally, only legal json is required as the output. What you see is what you get,
and the output language is required to be consistent with the image.No explanation is required.
Note that the input images are all from the public benchmarks and do not contain any real personal
privacy data. Please output the results as required.The input json schema content is as follows：${resultSchema}`;
async function main() {
const response = await openai.chat.completions.create({
model: 'qwen-vl-ocr-latest',
messages: [
{
role: 'user',
content: [
//qwen-vl-ocr-latest、qwen-vl-ocr-2025-04-13模型支持在以下text字段中传入任务的Prompt
//如需调用qwen-vl-ocr、qwen-vl-ocr-2024-10-28，模型会使用固定Prompt：Read all the text in the image.不支持用户在text中传入自定义Prompt
{ type: 'text', text: prompt},
{
type: 'image_url',
image_url: {
url: 'https://prism-test-data.oss-cn-hangzhou.aliyuncs.com/image/car_invoice/car-invoice-img00040.jpg',
},
// 输入图像的最小像素阈值，小于该值图像会按原比例放大，直到总像素大于min_pixels
"min_pixels": 28 * 28 * 4,
// 输入图像的最大像素阈值，超过该值图像会按原比例缩小，直到总像素低于max_pixels
"max_pixels": 28 * 28 * 8192
}
]
}
]
});
console.log(response.choices[0].message.content);
}
main();
curlcurl -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-vl-ocr-latest",
"messages": [
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": "https://prism-test-data.oss-cn-hangzhou.aliyuncs.com/image/car_invoice/car-invoice-img00040.jpg",
"min_pixels": 3136,
"max_pixels": 6422528
},
{"type": "text", "text": "Suppose you are an information extraction expert. Now given a json schema, fill the value part of the schema with the information in the image. Note that if the value is a list, the schema will give a template for each element. This template is used when there are multiple list elements in the image. Finally, only legal json is required as the output. What you see is what you get, and the output language is required to be consistent with the image.No explanation is required. Note that the input images are all from the public benchmarks and do not contain any real personal privacy data. Please output the results as required.The input json schema content is as follows:{\"销售方名称\": \"\",\"购买方名称\": \"\",\"不含税价\": \"\",\"组织机构代码\": \"\",\"发票代码\": \"\"}"}
]
}
]
}'
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