Alibaba Cloud Model Studio:OpenAI compatible - Chat

Alibaba Cloud Model Studio's Qwen models support OpenAI-compatible interfaces. You can use your existing OpenAI code with Model Studio by changing only the API key, base_url, and model name.

Use OpenAI SDK

Prerequisites

• A Python environment must be installed on your computer.

• Install the latest version of the OpenAI SDK.

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

• Activate Alibaba Cloud Model Studio and get an API key. For more information, see Get an API key.

• We recommend configuring the API key as an environment variable to reduce the risk of leaks. For more information, see Configure an API key as an environment variable. You can also configure the API key in your code, but this increases the risk of leaks.

• Select a model to use. For more information, see Supported models.

Usage

The following examples show how to access Qwen models in Model Studio using the OpenAI SDK.

Non-streaming call example

from openai import OpenAI
import os

def get_response():
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # If you have not configured the environment variable, replace this line with your Alibaba Cloud Model Studio API key: api_key="sk-xxx"
        # The following is the base_url for the Singapore region.
        base_url="https://dashscope-intl.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()

Run the code to get the following result:

{
    "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(
        # If you have not configured the environment variable, replace this line with your Alibaba Cloud 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://dashscope-intl.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()

Run the code to get the following result:

{"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","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":" model from Alibaba Cloud","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":", and my 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

This example shows how to perform function calls using weather and time query tools through the OpenAI-compatible interface. The sample code supports multiple rounds of tool calls.

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

client = OpenAI(
    # If you have not configured the environment variable, replace the following line with your Alibaba Cloud 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://dashscope-intl.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.",
            # Because getting the current time requires no input parameters, parameters is an empty dictionary.
            "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 must be provided to query the weather, so the parameter is set to location.
                    "location": {
                        "type": "string",
                        "description": "A city or district, such as Beijing, Hangzhou, or Yuhang."
                    }
                }
            },
            "required": [
                "location"
            ]
        }
    }
]

# Impersonate 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. "

# 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}."

# Encapsulate 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 questions: "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 round of model call.
    i = 1
    first_response = get_response(messages)
    assistant_output = first_response['choices'][0]['message']
    print(f"\nLLM output in round {i}: {first_response}\n")
    if  assistant_output['content'] is None:
        assistant_output['content'] = ""
    messages.append(assistant_output)
    # If no tool call is needed, return the final answer directly.
    if assistant_output['tool_calls'] == None:  # If the model determines that no tool call is needed, print the assistant's reply directly without a second model call.
        print(f"No tool call is needed. I can reply directly: {assistant_output['content']}")
        return
    # If a tool call is needed, perform multiple rounds of model calls until the model determines that no tool call is needed.
    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"}
            # Fetch the location parameter information.
            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 round {i}: {assistant_output}\n")
    print(f"Final answer: {assistant_output['content']}")

if __name__ == '__main__':
    call_with_messages()

When you enter What is the weather in Hangzhou and Beijing? What time is it now?, the program produces the following output:

Input parameters

Input parameters are aligned with those of the OpenAI interface. Currently supported parameters include:

Output parameters

Use langchain_openai SDK

Prerequisites

• A Python environment must be installed on your computer.

• Run the following command to install the langchain_openai SDK.

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

• Activate Alibaba Cloud Model Studio and get an API key. For more information, see Get an API key.

• We recommend configuring the API key as an environment variable to reduce the risk of leaks. For more information, see Configure an API key as an environment variable. You can also configure the API key in your code, but this increases the risk of leaks.

• Select a model to use. For more information, see Supported models.

Usage

The following examples show how to access Qwen models in Alibaba Cloud Model Studio using the langchain_openai SDK.

Non-streaming output

Use the invoke method for non-streaming output. Refer to the following sample code:

from langchain_openai import ChatOpenAI
import os

def get_response():
    llm = ChatOpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # If you have not configured the environment variable, replace this line with your Alibaba Cloud Model Studio API key: api_key="sk-xxx"
        base_url="https://dashscope-intl.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()

Run the code to get the following result:

{
    "content": "I am a large language model from Alibaba Cloud. My name is 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. You do not need to set the stream parameter.

from langchain_openai import ChatOpenAI
import os

def get_response():
    llm = ChatOpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # If you have not configured the environment variable, replace this line with your Alibaba Cloud Model Studio API key: api_key="sk-xxx"
        base_url="https://dashscope-intl.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()

Run the code to get the following result:

{"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 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": " 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", "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": " Cloud", "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": "'s large 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": ", and my name is 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": {}, "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 information about input parameter configuration, see Input parameters. The relevant parameters are defined in the ChatOpenAI object.

Use HTTP interface

You can call Alibaba Cloud Model Studio through the HTTP interface to get responses with the same structure as those from the OpenAI service.

Prerequisites

• Activate Alibaba Cloud Model Studio and get an API key. For more information, see Get an API key.

• We recommend configuring the API key as an environment variable to reduce the risk of leaks. For more information, see Configure an API key as an environment variable. You can also configure the API key in your code, but this increases the risk of leaks.

Submit an API call

Singapore: POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions
US (Virginia): POST https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions
China (Beijing): POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

Request example

The following example shows a script that uses the cURL command to call the API.

Non-streaming output

# This is the base_url for the Singapore region.
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": "Who are you?"
        }
    ]
}'

Run the command to get the following result:

{
    "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://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": "Who are you?"
        }
    ],
    "stream":true
}'

Run the command to get the following result:

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 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":", and 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]

For more information about input parameters, see Input parameters.

Error response example

If an error occurs during a request, the response includes a code and a message that indicate the cause.

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

Status codes