通义千问VL
支持的领域 / 任务:aigc
通义千问VL是阿里云研发的大规模视觉语言模型(Large Vision Language Model, LVLM),可以以图像、文本、检测框作为输入,并以文本和检测框作为输出,支持中文多模态对话及多图对话,并具有更好的性能,是首个支持中文开放域的通用定位模型和首个开源448分辨率的大规模视觉语言模型。
通义千问VL模型主要有以下特点:
强大的性能:在四大类多模态任务的标准英文测评中(Zero-shot Captioning/VQA/DocVQA/Grounding)上,均取得同等通用模型大小下最好效果;
多语言对话模型:天然支持英文、中文等多语言对话,端到端支持图片里中英双语的长文本识别;
多图交错对话:支持多图输入和比较,指定图片问答,多图文学创作等;
首个支持中文开放域定位的通用模型:通过中文开放域语言表达进行检测框标注;
细粒度识别和理解:相比于目前其它开源LVLM使用的224分辨率,Qwen-VL是首个开源的448分辨率的LVLM模型。更高分辨率可以提升细粒度的文字识别、文档问答和检测框标注。
用户以文本和url形式的图片形式输入包含多轮对话历史和当前指令的信息序列(messages),到返回模型生成的回复作为输出。在这一过程中,文本将被转换为语言模型可以处理的token序列。Token是模型用来表示自然语言文本的基本单位,可以直观的理解为“字”或“词”。对于中文文本来说,1个token通常对应一个汉字;对于英文文本来说,1个token通常对应3至4个字母或1个单词。例如,中文文本“你好,我是通义千问”会被转换成序列['你', '好', ',', '我', '是', '通', '义', '千', '问'],而英文文本"Nice to meet you."则会被转换成['Nice', ' to', ' meet', ' you', '.'];图片将按照图片尺寸、图片大小、分辨率不同会转换成不同长度地token序列。
由于模型调用的计算量与token序列长度相关,输入或输出token数量越多,模型的计算时间越长,我们将根据模型输入和输出的token数量计费。可以从API返回结果的 usage 字段中了解到您每次调用时使用的token数量。
模型概览
模型名 | 模型简介 |
qwen-vl-v1 | 以 Qwen-7B 语言模型初始化,添加图像模型,图像输入分辨率为448的预训练模型。 |
qwen-vl-chat-v1 | 通义千问VL支持灵活的交互方式,包括多图、多轮问答、创作等能力的模型。 |
SDK使用
前提条件
已开通服务并获得API-KEY:开通DashScope并创建API-KEY。
已安装最新版SDK:安装DashScope SDK。
问答示例
以下示例展示了调用通义千问VL模型对用户指令进行响应的代码。
需要使用您的API-KEY替换示例中的 YOUR_DASHSCOPE_API_KEY,代码才能正常运行。
API-KEY设置
export DASHSCOPE_API_KEY=YOUR_DASHSCOPE_API_KEY
简单示例
from http import HTTPStatus
import dashscope
def simple_multimodal_conversation_call():
"""Simple single round multimodal conversation call.
"""
messages = [
{
"role": "user",
"content": [
{"image": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"},
{"text": "这是什么?"}
]
}
]
response = dashscope.MultiModalConversation.call(model=dashscope.MultiModalConversation.Models.qwen_vl_chat_v1,
messages=messages)
# The response status_code is HTTPStatus.OK indicate success,
# otherwise indicate request is failed, you can get error code
# and message from code and message.
if response.status_code == HTTPStatus.OK:
print(response)
else:
print(response.code) # The error code.
print(response.message) # The error message.
if __name__ == '__main__':
simple_multimodal_conversation_call()
// Copyright (c) Alibaba, Inc. and its affiliates.
import java.util.Arrays;
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.aigc.multimodalconversation.MultiModalConversationMessage;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalMessageItemImage;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalMessageItemText;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
public class Main {
public static void simpleMultiModalConversationCall() throws ApiException, NoApiKeyException, UploadFileException {
MultiModalConversation conv = new MultiModalConversation();
MultiModalMessageItemImage userImage = new MultiModalMessageItemImage(
"https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg");
MultiModalMessageItemText userText = new MultiModalMessageItemText("这是什么?");
MultiModalConversationMessage userMessage =
MultiModalConversationMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(userImage, userText)).build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
.model(MultiModalConversation.Models.QWEN_VL_CHAT_V1)
.message(userMessage).build();
MultiModalConversationResult result = conv.call(param);
System.out.print(result);
}
public static void main(String[] args) {
try {
simpleMultiModalConversationCall();
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}
本地文件
您可以通过本地文件测试接口调用,示例代码如下。
from dashscope import MultiModalConversation
def call_with_local_file():
"""Sample of use local file.
linux&mac file schema: file:///home/images/test.png
windows file schema: file://D:/images/abc.png
"""
local_file_path1 = 'file://The_local_absolute_file_path1'
local_file_path2 = 'file://The_local_absolute_file_path2'
messages = [{
'role': 'system',
'content': [{
'text': 'You are a helpful assistant.'
}]
}, {
'role':
'user',
'content': [
{
'image': local_file_path1
},
{
'image': local_file_path2
},
{
'text': '图片里有什么东西?'
},
]
}]
response = MultiModalConversation.call(model=MultiModalConversation.Models.qwen_vl_chat_v1, messages=messages)
print(response)
if __name__ == '__main__':
call_with_local_file()
// Copyright (c) Alibaba, Inc. and its affiliates.
import java.util.Arrays;
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.aigc.multimodalconversation.MultiModalConversationMessage;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalMessageItemImage;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalMessageItemText;
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 io.reactivex.Flowable;
public class Main {
/*
* sample of use local file
* Windows file format: file:///D:/test/images/test.png
* Linux & Mac format: file://The_absolute_local_path
*
*/
public static void callWithLocalFile()
throws ApiException, NoApiKeyException, UploadFileException {
String localFilePath1 = "file:///home/xxx/images/test.png";
String localFilePath2 = "file:///home/xxx/images/test2.png";
MultiModalConversation conv = new MultiModalConversation();
MultiModalMessageItemImage userImage1 = new MultiModalMessageItemImage(
localFilePath1);
MultiModalMessageItemImage userImage2 = new MultiModalMessageItemImage(
localFilePath2);
MultiModalMessageItemText userText = new MultiModalMessageItemText("这是什么?");
MultiModalConversationMessage userMessage = MultiModalConversationMessage.builder()
.role(Role.USER.getValue()).content(Arrays.asList(userImage1, userImage2, userText)).build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
.model(MultiModalConversation.Models.QWEN_VL_CHAT_V1)
.message(userMessage)
.build();
Flowable<MultiModalConversationResult> result = conv.streamCall(param);
result.blockingForEach(item -> {
System.out.println(item);
});
}
public static void main(String[] args) {
try {
callWithLocalFile();
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}
多轮对话
from http import HTTPStatus
from dashscope import MultiModalConversation
def conversation_call():
"""Sample of multiple rounds of conversation.
"""
messages = [
{
"role": "user",
"content": [
{"image": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"},
{"text": "这是什么?"},
]
}
]
response = MultiModalConversation.call(model=MultiModalConversation.Models.qwen_vl_chat_v1,
messages=messages)
# The response status_code is HTTPStatus.OK indicate success,
# otherwise indicate request is failed, you can get error code
# and message from code and message.
if response.status_code == HTTPStatus.OK:
print(response)
else:
print(response.code) # The error code.
print(response.message) # The error message.
messages.append({'role': response.output.choices[0].message.role,
'content': [{'text': response.output.choices[0].message.content}]})
messages.append({"role": "user",
"content": [
{"text": "她们在干什么?", }
]})
response = MultiModalConversation.call(model=MultiModalConversation.Models.qwen_vl_chat_v1,
messages=messages)
# The response status_code is HTTPStatus.OK indicate success,
# otherwise indicate request is failed, you can get error code
# and message from code and message.
if response.status_code == HTTPStatus.OK:
print(response)
else:
print(response.code) # The error code.
print(response.message) # The error message.
if __name__ == '__main__':
conversation_call()
// Copyright (c) Alibaba, Inc. and its affiliates.
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationMessage;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalMessageItemImage;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalMessageItemText;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
public class Main {
public static void multiRoundConversationCall() throws ApiException, NoApiKeyException, UploadFileException {
MultiModalConversation conv = new MultiModalConversation();
MultiModalMessageItemText systemText = new MultiModalMessageItemText("You are a helpful assistant.");
MultiModalConversationMessage systemMessage = MultiModalConversationMessage.builder()
.role(Role.SYSTEM.getValue()).content(Arrays.asList(systemText)).build();
MultiModalMessageItemImage userImage = new MultiModalMessageItemImage(
"https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg");
MultiModalMessageItemText userText = new MultiModalMessageItemText("这是什么?");
MultiModalConversationMessage userMessage =
MultiModalConversationMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(userImage, userText)).build();
List<MultiModalConversationMessage> messages = new ArrayList<>();
messages.add(systemMessage);
messages.add(userMessage);
MultiModalConversationParam param = MultiModalConversationParam.builder()
.model(MultiModalConversation.Models.QWEN_VL_CHAT_V1)
.messages(messages)
.build();
MultiModalConversationResult result = conv.call(param);
System.out.println(result);
MultiModalMessageItemText assistentText = new MultiModalMessageItemText(
result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text").toString());
MultiModalConversationMessage assistentMessage = MultiModalConversationMessage.builder()
.role(Role.ASSISTANT.getValue()).content(Arrays.asList(assistentText)).build();
messages.add(assistentMessage);
userText = new MultiModalMessageItemText("他们在干什么?");
messages.add(MultiModalConversationMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(userText)).build());
param.setMessages(new ArrayList<Object>(messages));
result = conv.call(param);
System.out.print(result);
}
public static void main(String[] args) {
try {
multiRoundConversationCall();
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}
流式输出
from dashscope import MultiModalConversation
def simple_multimodal_conversation_call():
"""Simple single round multimodal conversation call.
"""
messages = [
{
"role": "user",
"content": [
{"image": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"},
{"text": "这是什么?"}
]
}
]
responses = MultiModalConversation.call(model=MultiModalConversation.Models.qwen_vl_chat_v1,
messages=messages,
stream=True)
for response in responses:
print(response)
if __name__ == '__main__':
simple_multimodal_conversation_call()
// Copyright (c) Alibaba, Inc. and its affiliates.
import java.util.Arrays;
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.aigc.multimodalconversation.MultiModalConversationMessage;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalMessageItemImage;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalMessageItemText;
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 io.reactivex.Flowable;
public class Main {
public static void simpleMultiModalConversationStreamCall() throws ApiException, NoApiKeyException, UploadFileException {
MultiModalConversation conv = new MultiModalConversation();
MultiModalMessageItemImage userImage = new MultiModalMessageItemImage(
"https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg");
MultiModalMessageItemText userText = new MultiModalMessageItemText("这是什么?");
MultiModalConversationMessage userMessage =
MultiModalConversationMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(userImage, userText)).build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
.model(MultiModalConversation.Models.QWEN_VL_CHAT_V1)
.message(userMessage).build();
Flowable<MultiModalConversationResult> result = conv.streamCall(param);
result.blockingForEach(item->{
System.out.println(item);
});
}
public static void main(String[] args) {
try {
simpleMultiModalConversationStreamCall();
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}
参数配置
参数 | 类型 | 默认值 | 说明 |
model | string | - | 指定用于对话的通义千问模型名,qwen-vl-v1或qwen-vl-chat-v1。 |
messages | list[dict] | - | 用户输入的历史对话信息和指令组成的完整输入信息。 参考示例,目前对于图片,支持图片url和本地路径 本地路径格式: python下:
file://D:/images/abc.png java下:
⚠️windows下python,java区别 |
top_p (可选) | float | 0.8 | 生成过程中核采样方法概率阈值,例如,取值为0.8时,仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为(0,1.0),取值越大,生成的随机性越高;取值越低,生成的确定性越高。 |
stream (可选) | bool | False | 是否使用流式输出。当以stream模式输出结果时,接口返回结果为generator,需要通过迭代获取结果,每个输出为当前生成的整个序列,最后一次输出为最终全部生成结果。 |
top_k | float | 100.0 | 生成时,采样候选集的大小。例如,取值为50时,仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大,生成的随机性越高;取值越小,生成的确定性越高。注意:如果top_k的值大于100,top_k将采用默认值100 |
seed | int | 1234 | 生成时,随机数的种子,用于控制模型生成的随机性。如果使用相同的种子,每次运行生成的结果都将相同;当需要复现模型的生成结果时,可以使用相同的种子。seed参数支持无符号64位整数类型。默认值 1234 |
返回结果
返回结果示例
{
"status_code": 200,
"request_id": "c34fdf33-d5a6-9902-8784-e76bdf60ab0c",
"code": "",
"message": "",
"output": {
"text": null,
"finish_reason": null,
"choices": [
{
"finish_reason": null,
"message": {
"role": "assistant",
"content": "图片里有绵羊"
}
}
]
},
"usage": {
"input_tokens": 0,
"output_tokens": 5
}
}
返回参数说明
返回参数 | 类型 | 说明 | |
status_code | int | 200(HTTPStatus.OK)表示请求成功,否则表示请求失败,可以通过code获取错误码,通过message字段获取错误详细信息。 | |
request_Id | string | 系统生成的标志本次调用的id。 | |
code | string | 表示请求失败,表示错误码,成功忽略。 | |
output | dict | 调用结果信息,对于通义VL模型,包含输出message。 | |
output.choices | List | 模型为输入生成的完成选项列表。 | "choices":[ { "message":{ "role":"assistant", "content":"我建议你去颐和园" } }] |
output.choices[x].message.role | String | 信息来源。 | |
output.choices[x].message.content | String | 本次请求的算法输出内容。 | |
usage | dict | 计量信息,表示本次请求计量数据。 | |
usage.input_tokens | int | 用户输入文本转换成Token后的长度。 | |
usage.output_tokens | int | 模型生成回复转换为Token后的长度。 |
HTTP调用接口
功能描述
通义VL模型同时支持 HTTP 调用来完成客户的响应,目前提供普通 HTTP 和 HTTP SSE 两种协议,您可根据自己的需求自行选择。
前提条件
已开通服务并获得API-KEY:开通DashScope并创建API-KEY。
提交接口调用
POST https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
入参描述
传参方式 | 字段 | 类型 | 必选 | 描述 | 示例值 |
Header | Content-Type | String | 是 | 请求类型:application/json 或者text/event-stream(开启 SSE 响应) | application/json |
Authorization | String | 是 | API-Key,例如:Bearer d1**2a | Bearer d1**2a | |
X-DashScope-SSE | String | 否 | 跟Accept: text/event-stream 二选一即可启用SSE响应 | enable | |
Body | model | String | 是 | 指明需要调用的模型 | qwen-vl-chat-v1 |
input.messages[list] | Array | 是 | 多模态场景下的输入信息 | "messages":[ { "role": "user", "content": [ {"image": "http://XXXX"}, {"text": "这个图片是哪里?"} ] }] | |
input.message[x].role | String | 是 | 信息来源 | ||
input.message[x].content[list] | Array | 是 | contents 列表中包含本次输入的内容列表,每一个列表可以分别是图像(image)或者文本(text) | ||
input.message[x].contents[list].image | String | 至少包含一项 | 本次输入的图像内容的 url 链接;图像格式目前支持:bmp, jpg, jpeg, png 和 tiff。 | ||
input.message[x].contents[list].text | String | 本次输入的文本内容;支持 utf-8 编码的中文、英文输入。 | |||
parameters.top_p | Float | 否 | 生成时,核采样方法的概率阈值。例如,取值为0.8时,仅保留累计概率之和大于等于0.8的概率分布中的token,作为随机采样的候选集。取值范围为(0,1.0),取值越大,生成的随机性越高;取值越低,生成的随机性越低。默认值 0.8。注意,取值不要大于等于1 | 0.8 | |
parameters.top_k | Integer | 否 | 生成时,采样候选集的大小。例如,取值为50时,仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大,生成的随机性越高;取值越小,生成的确定性越高。注意:如果top_k的值大于100,top_k将采用默认值100 | 50 | |
parameters.seed | Integer | 否 | 生成时,随机数的种子,用于控制模型生成的随机性。如果使用相同的种子,每次运行生成的结果都将相同;当需要复现模型的生成结果时,可以使用相同的种子。seed参数支持无符号64位整数类型。默认值 1234 | 65535 |
出参描述
字段 | 类型 | 描述 | 示例值 |
output.choices[list] | Array | 模型为输入生成的完成选项列表。 | "choices":[ { "message":{ "role":"assistant", "content":"我建议你去颐和园" } }] |
output.choices[x].message.role | String | 信息来源。 | |
output.choices[x].message.content | String | 本次请求的算法输出内容。 | |
output.finish_reason | String | 有三种情况:正在生成时为null,生成结束时如果由于停止token导致则为stop,生成结束时如果因为生成长度过长导致则为length。 | stop |
usage.output_tokens | Integer | 本次请求算法输出内容的 token 数目。 | 380 |
usage.input_tokens | Integer | 本次请求输入内容的 token 数目。在打开了搜索的情况下,输入的 token 数目因为还需要添加搜索相关内容支持,所以会超出客户在请求中的输入。 | 633 |
request_id | String | 本次请求的系统唯一码 | 7574ee8f-38a3-4b1e-9280-11c33ab46e51 |
请求示例(SSE 关闭)
以下示例展示通过CURL命令来调用通义千问-VL模型的脚本(SSE 关闭)。
需要使用您的API-KEY替换示例中的 your-dashscope-api-key ,代码才能正常运行。
curl --location 'https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation' \
--header 'Authorization: Bearer <your-dashscope-api-key>' \
--header 'Content-Type: application/json' \
--data '{
"model": "qwen-vl-chat-v1",
"input":{
"messages":[
{
"role": "system",
"content": [
{"text": "You are a helpful assistant."}
]
},
{
"role": "user",
"content": [
{"image": "http://XXXX"},
{"text": "这个图片是哪里?"}
]
}
]
},
"parameters": {
}
}'
响应示例(SSE关闭)
{
"output":{
"choices": [
"message": {
"role": "assistant",
"content": "这个图片描述的是 ... ... ..."
}
]
"finish_reason":"stop"
},
"usage":{
"output_tokens":380,
"input_tokens":633
},
"request_id":"d89c06fb-46a1-47b6-acb9-bfb17f814969"
}
请求示例(SSE开启)
以下示例展示通过CURL命令来调用通义千问-VL模型的脚本(SSE 开启)。
需要使用您的API-KEY替换示例中的 your-dashscope-api-key ,代码才能正常运行。
curl --location 'https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation' \
--header 'Authorization: Bearer <your-dashscope-api-key>' \
--header 'Content-Type: application/json' \
--header 'X-DashScope-SSE: enable' \
--data '{
"model": "qwen-vl-chat-v1",
"input":{
"messages":[
{
"role": "system",
"content": [
{"text": "You are a helpful assistant."}
]
},
{
"role": "user",
"content": [
{"image": "http://XXXX"},
{"text": "这个图片是哪里?"}
]
}
]
},
"parameters": {
}
}'
响应示例(SSE开启)
id:1
event:result
data:{"output":{"finish_reason":"null","choices":["message":{"role":"assistant","content":"这个"}]},"usage":{"output_tokens":3,"input_tokens":85},"request_id":"1117fb64-5dd9-9df0-a5ca-d7ee0e97032d"}
id:2
event:result
data:{"output":{"finish_reason":"null","choices":["message":{"role":"assistant","content":"这个图片描述"}]},"usage":{"output_tokens":11,"input_tokens":85},"request_id":"1117fb64-5dd9-9df0-a5ca-d7ee0e97032d"}
... ... ... ...
... ... ... ...
id:8
event:result
data:{"output":{"finish_reason":"stop","choices":["message":{"role":"assistant","content":"这个图片描述的是一个公园里的 ... ... "}]},"usage":{"output_tokens":51,"input_tokens":85},"request_id":"1117fb64-5dd9-9df0-a5ca-d7ee0e97032d"}
异常响应示例
在访问请求出错的情况下,输出的结果中会通过 code 和 message 指明出错原因。
{
"code":"InvalidApiKey",
"message":"Invalid API-key provided.",
"request_id":"fb53c4ec-1c12-4fc4-a580-cdb7c3261fc1"
}
状态码说明
DashScope灵积模型服务通用状态码请查阅:返回状态码说明
- 本页导读 (0)