Alibaba Cloud Model Studio：OpenAI Chat介面相容

阿里雲百鍊的通義千問模型支援 OpenAI 相容介面，您只需調整 API Key、BASE_URL 和模型名稱，即可將原有 OpenAI 代碼遷移至阿里雲百鍊服務使用。

通過OpenAI SDK調用

前提條件

• 請確保您的電腦上安裝了Python環境。

• 請安裝最新版OpenAI SDK。

  # 如果下述命令報錯，請將pip替換為pip3
  pip install -U openai

• 您需要開通阿里雲百鍊模型服務並獲得API-KEY，詳情請參考：準備工作：擷取與配置 API Key。

• 我們推薦您將API-KEY配置到環境變數中以降低API-KEY的泄露風險，配置方法可參考配置API Key到環境變數（準備下線，併入配置 API Key）。您也可以在代碼中配置API-KEY，但是泄露風險會提高。

• 請選擇您需要使用的模型：支援的模型列表。

使用方式

您可以參考以下樣本來使用OpenAI SDK訪問百鍊服務上的通義千問模型。

非流式調用樣本

from openai import OpenAI
import os

def get_response():
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您沒有配置環境變數，請用阿里雲百鍊API Key將本行替換為：api_key="sk-xxx"
        # 填寫DashScope SDK的base_url，如果使用華北2（北京）地區的模型，需要將url替換為：https://dashscope.aliyuncs.com/compatible-mode/v1
        base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",  
    )
    completion = client.chat.completions.create(
        model="qwen-plus",  # 此處以qwen-plus為例，可按需更換模型名稱。模型列表：https://www.alibabacloud.com/help/zh/model-studio/getting-started/models
        messages=[{'role': 'system', 'content': 'You are a helpful assistant.'},
                  {'role': 'user', 'content': '你是誰？'}]
        )
    print(completion.model_dump_json())

if __name__ == '__main__':
    get_response()

運行代碼可以獲得以下結果：

{
    "id": "chatcmpl-xxx",
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "logprobs": null,
            "message": {
                "content": "我是來自阿里雲的超大規模預訓練模型，我叫通義千問。",
                "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
    }
}

流式調用樣本

from openai import OpenAI
import os


def get_response():
    client = OpenAI(
        # 如果您沒有配置環境變數，請用阿里雲百鍊API Key將下行替換為：api_key="sk-xxx"
        api_key=os.getenv("DASHSCOPE_API_KEY"),
        # 填寫DashScope SDK的base_url，如果使用華北2（北京）地區的模型，需要將url替換為：https://dashscope.aliyuncs.com/compatible-mode/v1
        base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
        
    )
    completion = client.chat.completions.create(
        model="qwen-plus",  # 此處以qwen-plus為例，可按需更換模型名稱。模型列表：https://www.alibabacloud.com/help/zh/model-studio/getting-started/models
        messages=[{'role': 'system', 'content': 'You are a helpful assistant.'},
                  {'role': 'user', 'content': '你是誰？'}],
        stream=True,
        # 通過以下設定，在流式輸出的最後一行展示token使用資訊
        stream_options={"include_usage": True}
        )
    for chunk in completion:
        print(chunk.model_dump_json())


if __name__ == '__main__':
    get_response()

運行代碼可以獲得以下結果：

{"id":"chatcmpl-xxx","choices":[{"delta":{"content":"","function_call":null,"role":"assistant","tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1719286190,"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":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":"來自","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1719286190,"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":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":"雲的大規模語言模型","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":"，我叫通義千問。","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1719286190,"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":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[],"created":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":{"completion_tokens":16,"prompt_tokens":22,"total_tokens":38}}

function call樣本

此處以天氣查詢工具與時間查詢工具為例，向您展示通過OpenAI介面相容實現function call的功能。範例程式碼可以實現多輪工具調用。

from openai import OpenAI
from datetime import datetime
import json
import os

client = OpenAI(
    # 若沒有配置環境變數，請用阿里雲百鍊API Key將下行替換為：api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # 填寫DashScope SDK的base_url，如果使用華北2（北京）地區的模型，需要將url替換為：https://dashscope.aliyuncs.com/compatible-mode/v1
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",  
)

# 定義工具列表，模型在選擇使用哪個工具時會參考工具的name和description
tools = [
    # 工具1 擷取當前時刻的時間
    {
        "type": "function",
        "function": {
            "name": "get_current_time",
            "description": "當你想知道現在的時間時非常有用。",
            # 因為擷取目前時間無需輸入參數，因此parameters為空白字典
            "parameters": {}
        }
    },  
    # 工具2 擷取指定城市的天氣
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "當你想查詢指定城市的天氣時非常有用。",
            "parameters": {  
                "type": "object",
                "properties": {
                    # 查詢天氣時需要提供位置，因此參數設定為location
                    "location": {
                        "type": "string",
                        "description": "城市或縣區，比如北京市、杭州市、餘杭區等。"
                    }
                }
            },
            "required": [
                "location"
            ]
        }
    }
]

# 類比天氣查詢工具。返回結果樣本：“北京今天是雨天。”
def get_current_weather(location):
    return f"{location}今天是雨天。 "

# 查詢目前時間的工具。返回結果樣本：“目前時間：2024-04-15 17:15:18。“
def get_current_time():
    # 擷取當前日期和時間
    current_datetime = datetime.now()
    # 格式化當前日期和時間
    formatted_time = current_datetime.strftime('%Y-%m-%d %H:%M:%S')
    # 返回格式化後的目前時間
    return f"目前時間：{formatted_time}。"

# 封裝模型響應函數
def get_response(messages):
    completion = client.chat.completions.create(
        model="qwen-plus",  # 此處以qwen-plus為例，可按需更換模型名稱。模型列表：https://www.alibabacloud.com/help/zh/model-studio/getting-started/models
        messages=messages,
        tools=tools
        )
    return completion.model_dump()

def call_with_messages():
    print('\n')
    messages = [
            {
                "content": input('請輸入：'),  # 提問樣本："現在幾點了？" "一個小時後幾點" "北京天氣如何？"
                "role": "user"
            }
    ]
    print("-"*60)
    # 模型的第一輪調用
    i = 1
    first_response = get_response(messages)
    assistant_output = first_response['choices'][0]['message']
    print(f"\n第{i}輪大模型輸出資訊：{first_response}\n")
    if  assistant_output['content'] is None:
        assistant_output['content'] = ""
    messages.append(assistant_output)
    # 如果不需要調用工具，則直接返回最終答案
    if assistant_output['tool_calls'] == None:  # 如果模型判斷無需調用工具，則將assistant的回複直接列印出來，無需進行模型的第二輪調用
        print(f"無需調用工具，我可以直接回複：{assistant_output['content']}")
        return
    # 如果需要調用工具，則進行模型的多輪調用，直到模型判斷無需調用工具
    while assistant_output['tool_calls'] != None:
        # 如果判斷需要調用查詢天氣工具，則執行查詢天氣工具
        if assistant_output['tool_calls'][0]['function']['name'] == 'get_current_weather':
            tool_info = {"name": "get_current_weather", "role":"tool"}
            # 提取位置參數資訊
            location = json.loads(assistant_output['tool_calls'][0]['function']['arguments'])['location']
            tool_info['content'] = get_current_weather(location)
        # 如果判斷需要調用查詢時間工具，則執行查詢時間工具
        elif assistant_output['tool_calls'][0]['function']['name'] == 'get_current_time':
            tool_info = {"name": "get_current_time", "role":"tool"}
            tool_info['content'] = get_current_time()
        print(f"工具輸出資訊：{tool_info['content']}\n")
        print("-"*60)
        messages.append(tool_info)
        assistant_output = get_response(messages)['choices'][0]['message']
        if  assistant_output['content'] is None:
            assistant_output['content'] = ""
        messages.append(assistant_output)
        i += 1
        print(f"第{i}輪大模型輸出資訊：{assistant_output}\n")
    print(f"最終答案：{assistant_output['content']}")

if __name__ == '__main__':
    call_with_messages()

當輸入：杭州和北京天氣怎麼樣？現在幾點了？時，程式會進行如下輸出：

輸入參數配置

輸入參數與OpenAI的介面參數對齊，當前已支援的參數如下：

返回參數說明

通過langchain_openai SDK調用

前提條件

• 請確保您的電腦上安裝了Python環境。

• 通過運行以下命令安裝langchain_openai SDK。

  # 如果下述命令報錯，請將pip替換為pip3
  pip install -U langchain_openai

• 您需要開通阿里雲百鍊模型服務並獲得API-KEY，詳情請參考：準備工作：擷取與配置 API Key。

• 我們推薦您將API-KEY配置到環境變數中以降低API-KEY的泄露風險，詳情可參考配置API Key到環境變數（準備下線，併入配置 API Key）。您也可以在代碼中配置API-KEY，但是泄露風險會提高。

• 請選擇您需要使用的模型：支援的模型列表。

使用方式

您可以參考以下樣本來通過langchain_openai SDK使用阿里雲百鍊的千問模型。

非流式輸出

非流式輸出使用invoke方法實現，請參考以下範例程式碼：

from langchain_openai import ChatOpenAI
import os

def get_response():
    llm = ChatOpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您沒有配置環境變數，請用阿里雲百鍊API Key將本行替換為：api_key="sk-xxx"
        base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",  # 如果使用華北2（北京）地區的模型，需要將url替換為：https://dashscope.aliyuncs.com/compatible-mode/v1
        model="qwen-plus"  # 此處以qwen-plus為例，可按需更換模型名稱。模型列表：https://www.alibabacloud.com/help/zh/model-studio/getting-started/models
        )
    messages = [
        {"role":"system","content":"You are a helpful assistant."},
        {"role":"user","content":"你是誰？"}
    ]
    response = llm.invoke(messages)
    print(response.json())

if __name__ == "__main__":
    get_response()

運行代碼，可以得到以下結果：

{
    "content": "我是來自阿里雲的大規模語言模型，我叫通義千問。",
    "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": []
}

流式輸出

流式輸出使用stream方法實現，無需在參數中配置stream參數。

from langchain_openai import ChatOpenAI
import os

def get_response():
    llm = ChatOpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您沒有配置環境變數，請用阿里雲百鍊API Key將本行替換為：api_key="sk-xxx"
        base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",   # 如果使用華北2（北京）地區的模型，需要將url替換為：https://dashscope.aliyuncs.com/compatible-mode/v1
        model="qwen-plus",   # 此處以qwen-plus為例，可按需更換模型名稱。模型列表：https://www.alibabacloud.com/help/zh/model-studio/getting-started/models
        stream_usage=True
        )
    messages = [
        {"role":"system","content":"You are a helpful assistant."}, 
        {"role":"user","content":"你是誰？"},
    ]
    response = llm.stream(messages)
    for chunk in response:
        print(chunk.model_dump_json())

if __name__ == "__main__":
    get_response()

運行代碼，可以得到以下結果：

{"content": "", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}
{"content": "我是", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}
{"content": "來自", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}
{"content": "阿里", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}
{"content": "雲", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}
{"content": "的大規模語言模型", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}
{"content": "，我叫通", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}
{"content": "義千問。", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}
{"content": "", "additional_kwargs": {}, "response_metadata": {"finish_reason": "stop"}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}
{"content": "", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": {"input_tokens": 22, "output_tokens": 16, "total_tokens": 38}, "tool_call_chunks": []}

關於輸入參數的配置，可以參考輸入參數配置，相關參數在ChatOpenAI對象中定義。

通過HTTP介面調用

您可以通過HTTP介面來調用阿里雲百鍊服務，獲得與通過HTTP介面調用OpenAI服務相同結構的返回結果。

前提條件

• 您需要開通阿里雲百鍊模型服務並獲得API-KEY，詳情請參考：準備工作：擷取與配置 API Key。

• 我們推薦您將API-KEY配置到環境變數中以降低API-KEY的泄露風險，配置方法可參考配置API Key到環境變數（準備下線，併入配置 API Key）。您也可以在代碼中配置API-KEY，但是泄露風險會提高。

提交介面調用

新加坡：POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions
華北2（北京）：POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

請求樣本

以下樣本展示通過cURL命令來調用API的指令碼。

非流式輸出

# 如果使用華北2（北京）地區的模型，需要將url替換為：https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
curl --location 'https://dashscope-intl.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": "你是誰？"
        }
    ]
}'

運行命令可得到以下結果：

{
    "choices": [
        {
            "message": {
                "role": "assistant",
                "content": "我是來自阿里雲的大規模語言模型，我叫通義千問。"
            },
            "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"
}

流式輸出

如果您需要使用流式輸出，請在請求體中指定stream參數為true。

# 如果使用華北2（北京）地區的模型，需要將url替換為：https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
curl --location 'https://dashscope-intl.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": "你是誰？"
        }
    ],
    "stream":true
}'

運行命令可得到以下結果：

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":"來自"},"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":"雲的大規模語言模型"},"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":"，我叫通義千問。"},"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]

輸入參數的詳情請參考輸入參數配置。

異常響應樣本

在訪問請求出錯的情況下，輸出的結果中會通過 code 和 message 指明出錯原因。

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

狀態代碼說明