请求体 | 文本输入 流式输出 图像输入 视频输入 工具调用 联网搜索 异步调用 文档理解 文字提取 此处以单轮对话作为示例，您也可以进行多轮对话。 Python Java Node.js Go C#（HTTP） PHP（HTTP） curl
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-plus",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "你是谁？"},
],
)
print(completion.model_dump_json())
import 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"));
}
}
import OpenAI from "openai";
const openai = new OpenAI(
{
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",
messages: [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "你是谁？" }
],
});
console.log(JSON.stringify(completion))
}
main();
package 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)
}
using System.Net.Http.Headers;
using System.Text;
class Program
{
private static readonly HttpClient httpClient = new HttpClient();
static async Task Main(string[] args)
{
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;
}
string url = "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions";
string jsonContent = @"{
""model"": ""qwen-plus"",
""messages"": [
{
""role"": ""system"",
""content"": ""You are a helpful assistant.""
},
{
""role"": ""user"",
""content"": ""你是谁？""
}
]
}";
string result = await SendPostRequestAsync(url, jsonContent, apiKey);
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"))
{
httpClient.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", apiKey);
httpClient.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json"));
HttpResponseMessage response = await httpClient.PostAsync(url, content);
if (response.IsSuccessStatusCode)
{
return await response.Content.ReadAsStringAsync();
}
else
{
return $"请求失败: {response.StatusCode}";
}
}
}
}
<?php
$url = 'https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions';
$apiKey = getenv('DASHSCOPE_API_KEY');
$headers = [
'Authorization: Bearer '.$apiKey,
'Content-Type: application/json'
];
$data = [
"model" => "qwen-plus",
"messages" => [
[
"role" => "system",
"content" => "You are a helpful assistant."
],
[
"role" => "user",
"content" => "你是谁？"
]
]
];
$ch = curl_init();
curl_setopt($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);
$response = curl_exec($ch);
if (curl_errno($ch)) {
echo 'Curl error: ' . curl_error($ch);
}
curl_close($ch);
echo $response;
?>
curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-plus",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "你是谁？"
}
]
}'
更多用法请参见流式输出。
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-plus",
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())
import OpenAI from "openai";
const openai = new OpenAI(
{
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",
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();
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-plus",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "你是谁？"
}
],
"stream":true
}'
关于大模型分析图像的更多用法，请参见视觉理解。
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-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"}}
]}]
)
print(completion.model_dump_json())
import OpenAI from "openai";
const openai = new OpenAI(
{
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: "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();
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-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"}}]
}]
}'
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。
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-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())
import OpenAI from "openai";
const openai = new OpenAI({
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-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();
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-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"
}
]
}
]
}'
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。
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("DASHSCOPE_API_KEY"),
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
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"]
}
}
}
]
messages = [{"role": "user", "content": "杭州天气怎么样"}]
completion = client.chat.completions.create(
model="qwen-plus",
messages=messages,
tools=tools
)
print(completion.model_dump_json())
import OpenAI from "openai";
const openai = new OpenAI(
{
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 = [
{
"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"]
}
}
}
];
async function main() {
const response = await openai.chat.completions.create({
model: "qwen-plus",
messages: messages,
tools: tools,
});
console.log(JSON.stringify(response));
}
main();
curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-plus",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "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"]
}
}
}
]
}'
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-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'}],
extra_body={
"enable_search": True
}
)
print(completion.model_dump_json())
import OpenAI from "openai";
const openai = new OpenAI(
{
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",
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();
curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-plus",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "中国队在巴黎奥运会获得了多少枚金牌"
}
],
"enable_search": true
}'
import os
import asyncio
from openai import AsyncOpenAI
import platform
client = AsyncOpenAI(
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",
)
print(response.model_dump_json())
if platform.system() == "Windows":
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
asyncio.run(main())
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。
import os
from pathlib import Path
from openai import OpenAI
client = OpenAI(
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",
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())
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) {
OpenAIClient client = OpenAIOkHttpClient.builder()
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
.build();
Path filePath = Paths.get("百炼系列手机产品介绍.docx");
FileCreateParams fileParams = FileCreateParams.builder()
.file(filePath)
.purpose(FilePurpose.of("file-extract"))
.build();
FileObject fileObject = client.files().create(fileParams);
String fileId = fileObject.id();
ChatCompletionCreateParams chatParams = ChatCompletionCreateParams.builder()
.addSystemMessage("fileid://" + fileId)
.addUserMessage("这篇文章讲了什么？")
.model("qwen-long")
.build();
ChatCompletion chatCompletion = client.chat().completions().create(chatParams);
System.out.println(chatCompletion);
}
}
import fs from "fs";
import OpenAI from "openai";
const openai = new OpenAI(
{
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",
messages: [
{ role: "system", content: `fileid://${fileID}`},
{ role: "user", content: "这篇文章讲了什么？" }
],
});
console.log(JSON.stringify(completion))
}
main();
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-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"
}
}'
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。
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("DASHSCOPE_API_KEY"),
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
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": ""
}
"""
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",
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": 28 * 28 * 4,
"max_pixels": 28 * 28 * 8192
},
{"type": "text", "text": prompt},
]
}
])
print(completion.choices[0].message.content)
import OpenAI from 'openai';
const openai = new OpenAI({
apiKey: process.env.DASHSCOPE_API_KEY,
baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1',
});
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": ""
}`;
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: [
{ 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',
},
"min_pixels": 28 * 28 * 4,
"max_pixels": 28 * 28 * 8192
}
]
}
]
});
console.log(response.choices[0].message.content);
}
main();
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-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\": \"\"}"}
]
}
]
}'
|
