OpenAI接口兼容

DashScope提供了与OpenAI兼容的使用方式。如果您之前使用OpenAI SDK、langchain_openai SDK或HTTP方式调用OpenAI的服务，您只需在原有框架下调整API-KEY、endpoint、model等参数，即可切换至调用DashScope模型服务。

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的qwen-turbo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endpoint
    )
    completion = client.chat.completions.create(
        model="qwen-turbo",  # 设置为您需要使用的模型
        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": "我是来自阿里云的大规模语言模型，我叫通义千问。",
                "role": "assistant",
                "function_call": null,
                "tool_calls": null
            }
        }
    ],
    "created": 1715239858,
    "model": "qwen-turbo",
    "object": "chat.completion",
    "system_fingerprint": "",
    "usage": {
        "completion_tokens": 16,
        "prompt_tokens": 21,
        "total_tokens": 37
    }
}

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endpoint
    )
    completion = client.chat.completions.create(
        model="qwen-turbo", # 设置为您需要使用的模型
        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-turbo","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-turbo","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-turbo","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-turbo","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-turbo","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-turbo","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-turbo","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：

返回参数说明

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

• 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，详情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langchain_openai SDK访问DashScope的qwen-turbo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 endpoint
        model="qwen-turbo"
        )
    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-turbo",
        "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-turbo"
        )
    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

• 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。

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。

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-turbo",
    "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-turbo",
    "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-turbo",
    "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-turbo","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-turbo","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-turbo","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-turbo","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-turbo","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-turbo","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-turbo","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