Alibaba Cloud Model Studio:OpenAI-compatible - Chat

Model Studio provides an OpenAI-compatible interface for Qwen models. To migrate from OpenAI, update your API key, BASE_URL, and model name.

Call Qwen models via the OpenAI SDK

Prerequisites

• Install Python.

• Install the latest OpenAI SDK.

  # If the following command fails, replace pip with pip3.
  pip install -U openai

• Activate Model Studio and get an API key. See Obtain an API key.

• Configure the API key as an environment variable to reduce exposure risk. Configure the API key as an environment variable. You can also set it directly in code, but this increases exposure risk.

• Select a model from the List of supported models.

Usage

Non-streaming call example

from openai import OpenAI
import os

def get_response():
    client = OpenAI(
        # API keys differ by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # If you have not configured an environment variable, replace this line with your Model Studio API key: api_key="sk-xxx"
        # The following is the base_url for the Singapore region.
        base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",  
    )
    completion = client.chat.completions.create(
        model="qwen-plus",  # This example uses qwen-plus. You can change the model name as needed. For a list of models, see https://www.alibabacloud.com/help/en/model-studio/getting-started/models
        messages=[{'role': 'system', 'content': 'You are a helpful assistant.'},
                  {'role': 'user', 'content': 'Who are you?'}]
        )
    print(completion.model_dump_json())

if __name__ == '__main__':
    get_response()

Output:

{
    "id": "chatcmpl-xxx",
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "logprobs": null,
            "message": {
                "content": "I am a large-scale pre-trained model from Alibaba Cloud. My name is Qwen.",
                "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
    }
}

Streaming call example

from openai import OpenAI
import os

def get_response():
    client = OpenAI(
        # API keys differ by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        # If you have not configured an environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx"
        api_key=os.getenv("DASHSCOPE_API_KEY"),
        # The following is the base_url for the Singapore region.
        base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
        
    )
    completion = client.chat.completions.create(
        model="qwen-plus",  # This example uses qwen-plus. You can change the model name as needed. For a list of models, see https://www.alibabacloud.com/help/en/model-studio/getting-started/models
        messages=[{'role': 'system', 'content': 'You are a helpful assistant.'},
                  {'role': 'user', 'content': 'Who are you?'}],
        stream=True,
        # The following setting displays token usage information in the last line of the streaming output.
        stream_options={"include_usage": True}
        )
    for chunk in completion:
        print(chunk.model_dump_json())


if __name__ == '__main__':
    get_response()

Output:

{"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":"I am","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":" a large","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":" language model","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":" from Alibaba","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":" Cloud, and my","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":" name is Qwen.","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 example

The following code demonstrates multi-turn function calling with weather and time query tools.

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

client = OpenAI(
    # API keys differ by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If you have not configured an environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # The following is the base_url for the Singapore region.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",  
)

# Define the list of tools. The model refers to the name and description of the tools when selecting which one to use.
tools = [
    # Tool 1: Get the current time.
    {
        "type": "function",
        "function": {
            "name": "get_current_time",
            "description": "Useful when you want to know the current time.",
            # Since no input parameters are needed to get the current time, the 'parameters' object is empty.
            "parameters": {}
        }
    },  
    # Tool 2: Get the weather for a specified city.
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Useful when you want to query the weather for a specified city.",
            "parameters": {  
                "type": "object",
                "properties": {
                    # A location is required to query the weather, so a 'location' parameter is defined.
                    "location": {
                        "type": "string",
                        "description": "A city or district, such as Beijing, Hangzhou, or Yuhang."
                    }
                },
                "required": [
                    "location"
                ]
            }
        }
    }
]

# Simulate a weather query tool. Example result: "It is rainy in Beijing today."
def get_current_weather(location):
    return f"It is rainy in {location} today. "

# A tool to query the current time. Example result: "Current time: 2024-04-15 17:15:18."
def get_current_time():
    # Get the current date and time.
    current_datetime = datetime.now()
    # Format the current date and time.
    formatted_time = current_datetime.strftime('%Y-%m-%d %H:%M:%S')
    # Return the formatted current time.
    return f"Current time: {formatted_time}."

# Define the model response function.
def get_response(messages):
    completion = client.chat.completions.create(
        model="qwen-plus",  # This example uses qwen-plus. You can change the model name as needed. For a list of models, see https://www.alibabacloud.com/help/en/model-studio/getting-started/models
        messages=messages,
        tools=tools
        )
    return completion.model_dump()

def call_with_messages():
    print('\n')
    messages = [
            {
                "content": input('Please enter: '),  # Example prompts: "What time is it now?" "What time will it be in an hour?" "What is the weather like in Beijing?"
                "role": "user"
            }
    ]
    print("-"*60)
    # First turn of the model call.
    i = 1
    first_response = get_response(messages)
    assistant_output = first_response['choices'][0]['message']
    print(f"\nLLM output in turn {i}: {first_response}\n")
    if  assistant_output['content'] is None:
        assistant_output['content'] = ""
    messages.append(assistant_output)
    # If the model determines that a tool call is not needed, it prints the assistant's reply directly.
    if assistant_output['tool_calls'] == None:  
        print(f"No tool call is needed. I can reply directly: {assistant_output['content']}")
        return
    # If a tool call is needed, the code continues to make model calls until a final answer is generated.
    while assistant_output['tool_calls'] != None:
        # If the model determines that the weather query tool needs to be called, run the weather query tool.
        if assistant_output['tool_calls'][0]['function']['name'] == 'get_current_weather':
            tool_info = {"name": "get_current_weather", "role":"tool"}
            # Extract the location parameter.
            location = json.loads(assistant_output['tool_calls'][0]['function']['arguments'])['location']
            tool_info['content'] = get_current_weather(location)
        # If the model determines that the time query tool needs to be called, run the time query tool.
        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"Tool output: {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"LLM output in turn {i}: {assistant_output}\n")
    print(f"Final answer: {assistant_output['content']}")

if __name__ == '__main__':
    call_with_messages()

If you enter What's the weather like in Hangzhou and Beijing? What time is it now?, the program returns the following output:

Parameters

Input parameters compatible with the OpenAI API:

Response parameters

Call with the langchain_openai SDK

Prerequisites

• Install Python.

• Install the langchain_openai SDK.

  # If the following command fails, replace pip with pip3.
  pip install -U langchain_openai

• Activate Model Studio and get an API key. See Obtain an API key.

• Configure the API key as an environment variable to reduce exposure risk. Configure the API key as an environment variable. You can also set it in code, but this increases exposure risk.

• Select a model from the Supported model list.

Usage

Non-streaming output

Use the invoke method for non-streaming output:

from langchain_openai import ChatOpenAI
import os

def get_response():
    llm = ChatOpenAI(
        # API keys differ by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # If you have not configured an environment variable, replace this line with your Model Studio API key: api_key="sk-xxx"
        base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1", # This is the base_url for the Singapore region.
        model="qwen-plus"  # This example uses qwen-plus. You can change the model name as needed. For a list of models, see https://www.alibabacloud.com/help/en/model-studio/getting-started/models
        )
    messages = [
        {"role":"system","content":"You are a helpful assistant."},
        {"role":"user","content":"Who are you?"}
    ]
    response = llm.invoke(messages)
    print(response.json())

if __name__ == "__main__":
    get_response()

Output:

{
    "content": "I am a large language model from Alibaba Cloud. My name is Tongyi Qwen.",
    "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": []
}

Streaming output

Use the stream method for streaming output. No additional stream parameter is required.

from langchain_openai import ChatOpenAI
import os

def get_response():
    llm = ChatOpenAI(
        # API keys differ by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # If you have not configured an environment variable, replace this line with your Model Studio API key: api_key="sk-xxx"
        base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",   # This is the base_url for the Singapore region.
        model="qwen-plus",   # This example uses qwen-plus. You can change the model name as needed. For a list of models, see https://www.alibabacloud.com/help/en/model-studio/getting-started/models
        stream_usage=True
        )
    messages = [
        {"role":"system","content":"You are a helpful assistant."}, 
        {"role":"user","content":"Who are you?"},
    ]
    response = llm.stream(messages)
    for chunk in response:
        print(chunk.model_dump_json())

if __name__ == "__main__":
    get_response()

Output:

{"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": "I", "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": " am", "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": " a", "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": " large", "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": " language model", "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": " from", "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": " Alibaba Cloud. My name is Tongyi", "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": " Qwen.", "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": []}

For input parameters, see Input parameters.

HTTP API calls

Call Model Studio via HTTP. Responses follow the same structure as the OpenAI API.

Prerequisites

• Activate Model Studio and get an API key. See Obtain an API key.

• Configure the API key as an environment variable to reduce exposure risk. Configure an API key as an environment variable. You can also set it in code, but this increases exposure risk.

API request

Singapore: POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions
Japan (Tokyo): POST https://{WorkspaceId}.ap-northeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions
US (Virginia): POST https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions
China (Beijing): POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions
China (Hong Kong): POST https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/compatible-mode/v1/chat/completions

Request example

Call the API with cURL.

Non-streaming output

# This is the base_url for the Singapore region.
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.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": "Who are you?"
        }
    ]
}'

Output:

{
    "choices": [
        {
            "message": {
                "role": "assistant",
                "content": "I am a large language model from Alibaba Cloud. My name is Qwen."
            },
            "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"
}

Streaming output

To enable streaming output, set the stream parameter to true in the request body.

# This is the base_url for the Singapore region.
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.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": "Who are you?"
        }
    ],
    "stream":true
}'

Output:

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":"I am "},"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":"a large "},"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":"language "},"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":"model from "},"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":"Alibaba Cloud. "},"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":"My name is Qwen."},"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]

See Input parameter configuration.

Error response example

If a request fails, the response includes an error code and message explaining the failure.

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

Status codes