OpenAI接口兼容

更新时间: 2024-06-11 14:53:51

DashScope提供了与OpenAI兼容的使用方式。如果您之前使用OpenAI SDK或者其他OpenAI兼容接口(例如langchain_openai SDK),以及HTTP方式调用OpenAI的服务,只需在原有框架下调整API-KEY、base_url、model等参数,就可以直接使用DashScope模型服务。

兼容OpenAI需要信息

Base_URL

base_url表示模型服务的网络访问点或地址。通过该地址,您可以访问服务提供的功能或数据。在Web服务或API的使用中,base_url通常对应于服务的具体操作或资源的URL。当您使用OpenAI兼容接口来使用DashScope模型服务时,需要配置base_url。

  • 当您通过OpenAI SDK或其他OpenAI兼容的SDK调用时,需要配置的base_url如下:

    https://dashscope.aliyuncs.com/compatible-mode/v1
  • 当您通过HTTP请求调用时,需要配置的完整访问endpoint如下:

    POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

灵积API-KEY

您需要开通灵积模型服务并获得API-KEY,详情请参考:开通DashScope并创建API-KEY

支持的模型列表

当前OpenAI兼容接口支持的通义千问系列模型如下表所示。

模型分类

模型名称

通义千问

qwen-turbo

qwen-plus

qwen-max

qwen-max-0428

qwen-max-0403

qwen-max-0107

qwen-max-longcontext

通义千问VL系列

qwen-vl-plus

qwen-vl-max

通义千问开源系列

qwen2-57b-a14b-instruct

qwen2-72b-instruct

qwen2-7b-instruct

qwen2-1.5b-instruct

qwen2-0.5b-instruct

qwen1.5-110b-chat

qwen1.5-72b-chat

qwen1.5-32b-chat

qwen1.5-14b-chat

qwen1.5-7b-chat

qwen1.5-1.8b-chat

qwen1.5-0.5b-chat

codeqwen1.5-7b-chat

qwen-72b-chat

qwen-14b-chat

qwen-7b-chat

qwen-1.8b-longcontext-chat

qwen-1.8b-chat

通过OpenAI SDK调用

前提条件

  • 请确保您的计算机上安装了Python环境。

  • 请安装最新版OpenAI SDK。

    # 如果下述命令报错,请将pip替换为pip3
    pip install -U openai
  • 已开通灵积模型服务并获得API-KEY:开通DashScope并创建API-KEY

  • 我们推荐您将API-KEY配置到环境变量中以降低API-KEY的泄漏风险,配置方法可参考通过环境变量配置API-KEY。您也可以在代码中配置API-KEY,但是泄漏风险会提高

  • 请选择您需要使用的模型:支持的模型列表

使用方式

您可以参考以下非流式输出与流式输出示例来使用OpenAI SDK访问DashScope服务上的千问模型。

非流式调用示例

from openai import OpenAI
import os

def get_response():
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"), # 如果您没有配置环境变量,请在此处用您的API Key进行替换
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope SDK的base_url
    )
    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())

if __name__ == '__main__':
    get_response()

运行代码可以获得以下结果:

{
    "id": "chatcmpl-xxx",
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "logprobs": null,
            "message": {
                "content": "我是来自阿里云的超大规模预训练模型,我叫通义千问。",
                "role": "assistant",
                "function_call": null,
                "tool_calls": null
            }
        }
    ],
    "created": 1716430652,
    "model": "qwen-plus",
    "object": "chat.completion",
    "system_fingerprint": null,
    "usage": {
        "completion_tokens": 18,
        "prompt_tokens": 22,
        "total_tokens": 40
    }
}

流式调用示例

from openai import OpenAI
import os

def get_response():
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"), # 如果您没有配置环境变量,请在此处用您的API Key进行替换
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope SDK的base_url
    )
    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
        )
    for chunk in completion:
        print(chunk.model_dump_json())

if __name__ == '__main__':
    get_response()

运行代码可以获得以下结果:

{"id":"chatcmpl-xxx","choices":[{"delta":{"content":"","function_call":null,"role":"assistant","tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1715934114,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":"我是","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1715934114,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":"来自","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1715934114,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":"阿里","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1715934114,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":"云的大规模语言模型","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1715934114,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":",我叫通义千问。","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1715934114,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":"","function_call":null,"role":null,"tool_calls":null},"finish_reason":"stop","index":0,"logprobs":null}],"created":1715934114,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}

VL模型流式调用示例

from openai import OpenAI
import os

def get_response():
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量,请在此处用您的API Key进行替换
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope SDK的base_url
    )
    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"
                  }
                }
              ]
            }
          ],
        top_p=0.8,
        stream=True
)
    for chunk in completion:
        print(chunk.model_dump_json())

if __name__ == '__main__':
    get_response()

运行代码可以获得以下结果:

{"id":"chatcmpl-xxx","choices":[{"delta":{"content":[{"text":"图"}],"function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1716774982,"model":"qwen-vl-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":[{"text":"中"}],"function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1716774982,"model":"qwen-vl-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":[{"text":"是一位"}],"function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1716774982,"model":"qwen-vl-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":[{"text":"女士和她的狗在"}],"function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1716774982,"model":"qwen-vl-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":[{"text":"沙滩上互动。女士穿着格子"}],"function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1716774982,"model":"qwen-vl-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":[{"text":"衬衫,坐在地上微笑着看着狗狗"}],"function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1716774982,"model":"qwen-vl-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":[{"text":",而狗狗也开心地抬起前"}],"function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1716774982,"model":"qwen-vl-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":[{"text":"爪似乎想要握手或触摸女士的手"}],"function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1716774982,"model":"qwen-vl-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":[{"text":"。远处是海洋和夕阳西下的"}],"function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1716774982,"model":"qwen-vl-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":[{"text":"背景。\n\n图片的氛围显得很温馨"}],"function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1716774982,"model":"qwen-vl-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":[{"text":",展现了人与宠物之间美好的友谊"}],"function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1716774982,"model":"qwen-vl-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":[{"text":"时光。"}],"function_call":null,"role":null,"tool_calls":null},"finish_reason":"stop","index":0,"logprobs":null}],"created":1716774982,"model":"qwen-vl-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}

输入参数配置

输入参数与OpenAI的接口参数对齐,当前已支持的参数如下:

参数

类型

默认值

说明

model

string

-

用户使用model参数指明对应的模型,可选的模型请见支持的模型列表

messages

array

-

用户与模型的对话历史。array中的每个元素形式为{"role":角色, "content": 内容}。角色当前可选值:system、user、assistant,其中,仅messages[0]中支持role为system,一般情况下,user和assistant需要交替出现,且messages中最后一个元素的role必须为user。

top_p(可选)

float

-

生成过程中的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。

temperature(可选)

float

-

用于控制模型回复的随机性和多样性。具体来说,temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值,使得更多的低概率词被选择,生成结果更加多样化;而较低的temperature值则会增强概率分布的峰值,使得高概率词更容易被选择,生成结果更加确定。

取值范围: [0, 2),不建议取值为0,无意义。

重要

qwen-vl相关模型目前不支持该参数。

presence_penalty

(可选)

float

-

用户控制模型生成时整个序列中的重复度。提高presence_penalty时可以降低模型生成的重复度,取值范围[-2.0, 2.0]。

重要

目前仅在千问商业模型和qwen1.5及以后的开源模型上支持该参数。

max_tokens(可选)

integer

-

指定模型可生成的最大token个数。根据模型不同有不同的上限限制,一般不超过2000。

重要

qwen-vl相关模型目前不支持该参数。

seed(可选)

integer

-

生成时使用的随机数种子,用于控制模型生成内容的随机性。seed支持无符号64位整数。

stream(可选)

boolean

False

用于控制是否使用流式输出。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,每次输出为当前生成的增量序列。

stop(可选)

string or array

None

stop参数用于实现内容生成过程的精确控制,在模型生成的内容即将包含指定的字符串或token_id时自动停止。stop可以为string类型或array类型。

  • string类型

    当模型将要生成指定的stop词语时停止。

    例如将stop指定为"你好",则模型将要生成“你好”时停止。

  • array类型

    array中的元素可以为token_id或者字符串,或者元素为token_id的array。当模型将要生成的token或其对应的token_id在stop中时,模型生成将会停止。以下为stop为array时的示例(tokenizer对应模型为qwen-turbo):

    1.元素为token_id:

    token_id为108386和104307分别对应token为“你好”和“天气”,设定stop为[108386,104307],则模型将要生成“你好”或者“天气”时停止。

    2.元素为字符串:

    设定stop为["你好","天气"],则模型将要生成“你好”或者“天气”时停止。

    3.元素为array:

    token_id为108386和103924分别对应token为“你好”和“啊”,token_id为35946和101243分别对应token为“我”和“很好”。设定stop为[[108386, 103924],[35946, 101243]],则模型将要生成“你好啊”或者“我很好”时停止。

    说明

    stop为array类型时,不可以将token_id和字符串同时作为元素输入,比如不可以指定stop为["你好",104307]

    重要

    qwen-vl相关模型目前不支持该参数。

tools(可选)

array

None

用于指定可供模型调用的工具库,一次function call流程模型会从中选择其中一个工具。tools中每一个tool的结构如下:

  • type,类型为string,表示tools的类型,当前仅支持function。

  • function,类型为object,键值包括name,description和parameters:

    • name:类型为string,表示工具函数的名称,必须是字母、数字,可以包含下划线和短划线,最大长度为64。

    • description:类型为string,表示工具函数的描述,供模型选择何时以及如何调用工具函数。

    • parameters:类型为object,表示工具的参数描述,需要是一个合法的JSON Schema。JSON Schema的描述可以见链接。如果parameters参数为空,表示function没有入参。

在function call流程中,无论是发起function call的轮次,还是向模型提交工具函数的执行结果,均需设置tools参数。当前支持的模型包括qwen-turbo、qwen-plus、qwen-max和qwen-max-longcontext。

说明

tools暂时无法与stream=True同时使用。

重要

qwen-vl相关模型目前不支持该参数。

stream_options(可选)

object

None

该参数用于配置在流式输出时是否展示使用的token数目。只有当stream为True的时候该参数才会激活生效。若您需要统计流式输出模式下的token数目,可将该参数配置为stream_options={"include_usage":True}

enable_search

(可选,通过extra_body配置)

boolean

False

用于控制模型在生成文本时是否使用互联网搜索结果进行参考。取值如下:

  • True:启用互联网搜索,模型会将搜索结果作为文本生成过程中的参考信息,但模型会基于其内部逻辑判断是否使用互联网搜索结果。

  • False(默认):关闭互联网搜索。

配置方式为:extra_body={"enable_search":True}

重要

qwen-vl相关模型目前不支持该参数。

返回参数说明

返回参数

数据类型

说明

备注

id

string

系统生成的标识本次调用的id。

model

string

本次调用的模型名。

system_fingerprint

string

模型运行时使用的配置版本,当前暂时不支持,返回为空字符串“”。

choices

array

模型生成内容的详情。

choices[i].finish_reason

string

有三种情况:

  • 正在生成时为null;

  • 因触发输入参数中的stop条件而结束为stop;

  • 因生成长度过长而结束为length。

choices[i].message

object

模型输出的消息。

choices[i].message.role

string

模型的角色,固定为assistant。

choices[i].message.content

string

模型生成的文本。

choices[i].index

integer

生成的结果序列编号,默认为0。

created

integer

当前生成结果的时间戳(s)。

usage

object

计量信息,表示本次请求所消耗的token数据。

usage.prompt_tokens

integer

用户输入文本转换成token后的长度。

您可以参考本地tokenizer统计token数据进行token的估计。

usage.completion_tokens

integer

模型生成回复转换为token后的长度。

usage.total_tokens

integer

usage.prompt_tokens与usage.completion_tokens的总和。

通过langchain_openai SDK调用

前提条件

  • 请确保您的计算机上安装了Python环境。

  • 通过运行以下命令安装langchain_openai SDK。

    # 如果下述命令报错,请将pip替换为pip3
    pip install -U langchain_openai

使用方式

您可以参考以下非流式输出与流式输出示例来通过langchain_openai SDK使用DashScope的千问模型。

非流式输出

非流式输出使用invoke方法实现,请参考以下示例代码:

from langchain_openai import ChatOpenAI
import os

def get_response():
    llm = ChatOpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"), # 如果您没有配置环境变量,请在此处用您的API Key进行替换
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", # 填写DashScope base_url
        model="qwen-plus"
        )
    messages = [
        {"role":"system","content":"You are a helpful assistant."}, 
        {"role":"user","content":"你是谁?"}
    ]
    response = llm.invoke(messages)
    print(response.json(ensure_ascii=False))

if __name__ == "__main__":
    get_response()

运行代码,可以得到以下结果:

{
    "content": "我是来自阿里云的大规模语言模型,我叫通义千问。",
    "additional_kwargs": {},
    "response_metadata": {
        "token_usage": {
            "completion_tokens": 16,
            "prompt_tokens": 22,
            "total_tokens": 38
        },
        "model_name": "qwen-plus",
        "system_fingerprint": "",
        "finish_reason": "stop",
        "logprobs": null
    },
    "type": "ai",
    "name": null,
    "id": "run-xxx",
    "example": false,
    "tool_calls": [],
    "invalid_tool_calls": []
}

流式输出

流式输出使用stream方法实现,无需在参数中配置stream参数。

from langchain_openai import ChatOpenAI
import os

def get_response():
    llm = ChatOpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", 
        model="qwen-plus"
        )
    messages = [
        {"role":"system","content":"You are a helpful assistant."}, 
        {"role":"user","content":"你是谁?"},
    ]
    response = llm.stream(messages)
    for chunk in response:
        print(chunk.json(ensure_ascii=False))

if __name__ == "__main__":
    get_response()

运行代码,可以得到以下结果:

{"content": "", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-58c183ab-68c8-4007-8cdd-37725ad54266", "example": false, "tool_calls": [], "invalid_tool_calls": [], "tool_call_chunks": []}
{"content": "我是", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-58c183ab-68c8-4007-8cdd-37725ad54266", "example": false, "tool_calls": [], "invalid_tool_calls": [], "tool_call_chunks": []}
{"content": "来自", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-58c183ab-68c8-4007-8cdd-37725ad54266", "example": false, "tool_calls": [], "invalid_tool_calls": [], "tool_call_chunks": []}
{"content": "阿里", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-58c183ab-68c8-4007-8cdd-37725ad54266", "example": false, "tool_calls": [], "invalid_tool_calls": [], "tool_call_chunks": []}
{"content": "云的大规模语言模型", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-58c183ab-68c8-4007-8cdd-37725ad54266", "example": false, "tool_calls": [], "invalid_tool_calls": [], "tool_call_chunks": []}
{"content": ",我叫通义千问。", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-58c183ab-68c8-4007-8cdd-37725ad54266", "example": false, "tool_calls": [], "invalid_tool_calls": [], "tool_call_chunks": []}
{"content": "", "additional_kwargs": {}, "response_metadata": {"finish_reason": "stop"}, "type": "AIMessageChunk", "name": null, "id": "run-58c183ab-68c8-4007-8cdd-37725ad54266", "example": false, "tool_calls": [], "invalid_tool_calls": [], "tool_call_chunks": []}

关于输入参数的配置,可以参考输入参数配置,相关参数在ChatOpenAI对象中定义。

通过HTTP接口调用

您可以通过HTTP接口来调用DashScope服务,获得与通过HTTP接口调用OpenAI服务相同结构的返回结果。

前提条件

提交接口调用

POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

请求示例

以下示例展示通过CURL命令来调用API的脚本。

说明

如果您没有配置API-KEY为环境变量,需将$DASHSCOPE_API_KEY更改为您的API-KEY

非流式输出

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?"
        }
    ]
}'

运行命令可得到以下结果:

{
    "choices": [
        {
            "message": {
                "role": "assistant",
                "content": "我是来自阿里云的大规模语言模型,我叫通义千问。"
            },
            "finish_reason": "stop",
            "index": 0,
            "logprobs": null
        }
    ],
    "object": "chat.completion",
    "usage": {
        "prompt_tokens": 11,
        "completion_tokens": 16,
        "total_tokens": 27
    },
    "created": 1715252778,
    "system_fingerprint": "",
    "model": "qwen-plus",
    "id": "chatcmpl-xxx"
}

流式输出

如果您需要使用流式输出,请在请求体中指定stream参数为true。

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
}'

运行命令可得到以下结果:

data: {"choices":[{"delta":{"content":"","role":"assistant"},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}

data: {"choices":[{"finish_reason":null,"delta":{"content":"我是"},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}

data: {"choices":[{"delta":{"content":"来自"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}

data: {"choices":[{"delta":{"content":"阿里"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}

data: {"choices":[{"delta":{"content":"云的大规模语言模型"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}

data: {"choices":[{"delta":{"content":",我叫通义千问。"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}

data: {"choices":[{"delta":{"content":""},"finish_reason":"stop","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}

data: [DONE]

输入参数的详情请参考输入参数配置

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。

{
    "error": {
        "message": "Incorrect API key provided. ",
        "type": "invalid_request_error",
        "param": null,
        "code": "invalid_api_key"
    }
}

状态码说明

错误码

说明

400 - Invalid Request Error

输入请求错误,细节请参见具体报错信息。

401 - Incorrect API key provided

apikey不正确。

429 - Rate limit reached for requests

qps、qpm等超限。

429 - You exceeded your current quota, please check your plan and billing details

额度超限或者欠费。

500 - The server had an error while processing your request

服务端错误。

503 - The engine is currently overloaded, please try again later

服务端负载过高,可重试。

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