API reference for the Qwen-MT model - Alibaba Cloud Model Studio

This topic describes the input and output parameters for the Qwen-MT OpenAI-compatible API or DashScope API.

References: Machine translation (Qwen-MT)

OpenAI compatibility

Singapore region

For SDK, set base_url to: https://dashscope-intl.aliyuncs.com/compatible-mode/v1

For HTTP, the endpoint is: POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions

Beijing region

For SDK, set base_url to: https://dashscope.aliyuncs.com/compatible-mode/v1

For HTTP, the endpoint is: POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

You must first create an API key and export the API key as an environment variable. If using the OpenAI SDK, install the SDK.

Request body

Basic usage

Python

import os
from openai import OpenAI

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"),
    # If you use a model in the Beijing region, replace the base_url with: https://dashscope.aliyuncs.com/compatible-mode/v1
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
messages = [
    {
        "role": "user",
        "content": "No me reí después de ver este video"
    }
]
translation_options = {
    "source_lang": "auto",
    "target_lang": "English"
}

completion = client.chat.completions.create(
    model="qwen-mt-plus",
    messages=messages,
    extra_body={
        "translation_options": translation_options
    }
)
print(completion.choices[0].message.content)

Node.js

// Node.js v18 or later is required. Run the code in an ES Module environment.
import OpenAI from "openai";

const openai = new OpenAI(
    {
        // If you have not configured the environment variable, replace the following line with your Alibaba Cloud Model Studio API key: apiKey: "sk-xxx",
        apiKey: process.env.DASHSCOPE_API_KEY,
        // The following baseURL is for the Singapore region. If you use a model in the Beijing region, replace the baseURL with: https://dashscope.aliyuncs.com/compatible-mode/v1
        baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
    }
);
const completion = await openai.chat.completions.create({
    model: "qwen-mt-plus", 
    messages: [
        { role: "user", content: "No me reí después de ver este video" }
    ],
    translation_options: {
        source_lang: "auto",
        target_lang: "English"
    }
});
console.log(JSON.stringify(completion));

curl

# ======= Important =======
# The API keys for the Singapore and Beijing regions are different.
# The following endpoint is for the Singapore region. If you use a model in the Beijing region, replace the endpoint with: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
# === Delete this comment before execution ===
curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen-mt-plus",
    "messages": [{"role": "user", "content": "No me reí después de ver este video"}],
    "translation_options": {
      "source_lang": "auto",
      "target_lang": "English"
      }
}'

Term intervention

Python

import os
from openai import OpenAI

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"),
    # If you use a model in the Beijing region, replace the base_url with: https://dashscope.aliyuncs.com/compatible-mode/v1
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
messages = [
    {
        "role": "user",
        "content": "Este conjunto de biosensores utiliza grafeno, un material novedoso. Su objetivo son los elementos químicos. Su agudo «sentido del olfato» le permite reflejar el estado de salud del cuerpo de forma más profunda y precisa."
    }
]
translation_options = {
    "source_lang": "auto",
    "target_lang": "English",
    "terms": [
        {
            "source": "biosensor",
            "target": "biological sensor"
        },
        {
            "source": "grafeno",
            "target": "graphene"
        },
        {
            "source": "elementos químicos",
            "target": "chemical elements"
        },
        {
            "source": "estado de salud del cuerpo",
            "target": "health status of the body"
        }
    ]
}

completion = client.chat.completions.create(
    model="qwen-mt-plus",  # This example uses qwen-mt-plus. You can replace the model name as needed.
    messages=messages,
    extra_body={
        "translation_options": translation_options
    }
)
print(completion.choices[0].message.content)

Node.js

// Node.js v18 or later is required. Run the code in an ES Module environment.
import OpenAI from "openai";

const openai = new OpenAI(
    {
        // If you have not configured the environment variable, replace the following line with your Alibaba Cloud Model Studio API key: apiKey: "sk-xxx",
        apiKey: process.env.DASHSCOPE_API_KEY,
        // The following baseURL is for the Singapore region. If you use a model in the Beijing region, replace the baseURL with: https://dashscope.aliyuncs.com/compatible-mode/v1
        baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
    }
);
const completion = await openai.chat.completions.create({
    model: "qwen-mt-plus",
    messages: [
        { role: "user", content: "Este conjunto de biosensores utiliza grafeno, un material novedoso. Su objetivo son los elementos químicos. Su agudo «sentido del olfato» le permite reflejar el estado de salud del cuerpo de forma más profunda y precisa." }
    ],
    translation_options: {
        source_lang: "auto",
        target_lang: "English",
        terms: [
            {
                "source": "biosensor",
                "target": "biological sensor"
            },
            {
                "source": "grafeno",
                "target": "graphene"
            },
            {
                "source": "elementos químicos",
                "target": "chemical elements"
            },
            {
                "source": "estado de salud del cuerpo",
                "target": "health status of the body"
            }
        ]
    }
});
console.log(JSON.stringify(completion));

curl

# ======= Important =======
# The API keys for the Singapore and Beijing regions are different.
# The following endpoint is for the Singapore region. If you use a model in the Beijing region, replace the endpoint with: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
# === Delete this comment before execution ===
curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "model": "qwen-mt-plus",
  "messages": [
    {
      "role": "user",
      "content": "Este conjunto de biosensores utiliza grafeno, un material novedoso. Su objetivo son los elementos químicos. Su agudo «sentido del olfato» le permite reflejar el estado de salud del cuerpo de forma más profunda y precisa."
    }
  ],
  "translation_options": {
    "source_lang": "auto",
    "target_lang": "English",
    "terms": [
      {
        "source": "biosensor",
        "target": "biological sensor"
      },
      {
        "source": "grafeno",
        "target": "graphene"
      },
      {
        "source": "elementos químicos",
        "target": "chemical elements"
      },
      {
        "source": "estado de salud del cuerpo",
        "target": "health status of the body"
      }
    ]
  }
}'

Translation memory

Python

import os
from openai import OpenAI

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"),
    # If you use a model in the Beijing region, replace the base_url with: https://dashscope.aliyuncs.com/compatible-mode/v1
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
messages = [
    {
        "role": "user",
        "content": "El siguiente comando muestra la información de la versión de Thrift instalada."
    }
]
translation_options = {
    "source_lang": "auto",
    "target_lang": "English",
    "tm_list": [
        {
            "source": "Puede utilizar uno de los siguientes métodos para consultar la versión del motor de un clúster:",
            "target": "You can use one of the following methods to query the engine version of a cluster:"
        },
        {
            "source": "La versión de Thrift utilizada por nuestro HBase en la nube es la 0.9.0. Por lo tanto, recomendamos que la versión del cliente también sea la 0.9.0. Puede descargar Thrift 0.9.0 desde aquí. El paquete de código fuente descargado se utilizará posteriormente. Primero debe instalar el entorno de compilación de Thrift. Para la instalación desde el código fuente, puede consultar el sitio web oficial de Thrift.",
            "target": "The version of Thrift used by ApsaraDB for HBase is 0.9.0. Therefore, we recommend that you use Thrift 0.9.0 to create a client. Click here to download Thrift 0.9.0. The downloaded source code package will be used later. You must install the Thrift compiling environment first. For more information, see Thrift official website."
        },
        {
            "source": "Puede instalar el SDK a través de PyPI. El comando de instalación es el siguiente:",
            "target": "You can run the following command in Python Package Index (PyPI) to install Elastic Container Instance SDK for Python:"
        }
    ]
}

completion = client.chat.completions.create(
    model="qwen-mt-plus",  # This example uses qwen-mt-plus. You can replace the model name as needed.
    messages=messages,
    extra_body={
        "translation_options": translation_options
    }
)
print(completion.choices[0].message.content)

Node.js

// Node.js v18 or later is required. Run the code in an ES Module environment.
import OpenAI from "openai";

const openai = new OpenAI(
    {
        // If you have not configured the environment variable, replace the following line with your Alibaba Cloud Model Studio API key: apiKey: "sk-xxx",
        apiKey: process.env.DASHSCOPE_API_KEY,
        // The following baseURL is for the Singapore region. If you use a model in the Beijing region, replace the baseURL with: https://dashscope.aliyuncs.com/compatible-mode/v1
        baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
    }
);
const completion = await openai.chat.completions.create({
    model: "qwen-mt-plus",
    messages: [
        { role: "user", content: "The following command shows the version information of the installed Thrift;" }
    ],
    translation_options: {
        source_lang: "auto",
        target_lang: "English",
        tm_list: [
            {
                "source": "Puede utilizar uno de los siguientes métodos para consultar la versión del motor de un clúster:",
                "target": "You can use one of the following methods to query the engine version of a cluster:"
            },
            {
                "source": "La versión de Thrift utilizada por nuestro HBase en la nube es la 0.9.0. Por lo tanto, recomendamos que la versión del cliente también sea la 0.9.0. Puede descargar Thrift 0.9.0 desde aquí. El paquete de código fuente descargado se utilizará posteriormente. Primero debe instalar el entorno de compilación de Thrift. Para la instalación desde el código fuente, puede consultar el sitio web oficial de Thrift.",
                "target": "The version of Thrift used by ApsaraDB for HBase is 0.9.0. Therefore, we recommend that you use Thrift 0.9.0 to create a client. Click here to download Thrift 0.9.0. The downloaded source code package will be used later. You must install the Thrift compiling environment first. For more information, see Thrift official website."
            },
            {
                "source": "Puede instalar el SDK a través de PyPI. El comando de instalación es el siguiente:",
                "target": "You can run the following command in Python Package Index (PyPI) to install Elastic Container Instance SDK for Python:"
            }
        ]
    }
});
console.log(JSON.stringify(completion));

curl

# ======= Important =======
# The API keys for the Singapore and Beijing regions are different.
# The following endpoint is for the Singapore region. If you use a model in the Beijing region, replace the endpoint with: https://dashscope-aliyuncs.com/compatible-mode/v1/chat/completions
# === Delete this comment before execution ===
curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "model": "qwen-mt-plus",
  "messages": [
    {
      "role": "user",
      "content": "El siguiente comando muestra la información de la versión de Thrift instalada."
    }
  ],
  "translation_options": {
    "source_lang": "auto",
    "target_lang": "English",
    "tm_list":[
          {"source": "Puede utilizar uno de los siguientes métodos para consultar la versión del motor de un clúster:", "target": "You can use one of the following methods to query the engine version of a cluster:"},
          {"source": "La versión de Thrift utilizada por nuestro HBase en la nube es la 0.9.0. Por lo tanto, recomendamos que la versión del cliente también sea la 0.9.0. Puede descargar Thrift 0.9.0 desde aquí. El paquete de código fuente descargado se utilizará posteriormente. Primero debe instalar el entorno de compilación de Thrift. Para la instalación desde el código fuente, puede consultar el sitio web oficial de Thrift.", "target": "The version of Thrift used by ApsaraDB for HBase is 0.9.0. Therefore, we recommend that you use Thrift 0.9.0 to create a client. Click here to download Thrift 0.9.0. The downloaded source code package will be used later. You must install the Thrift compiling environment first. For more information, see Thrift official website."},
          {"source": "Puede instalar el SDK a través de PyPI. El comando de instalación es el siguiente:", "target": "You can run the following command in Python Package Index (PyPI) to install Elastic Container Instance SDK for Python:"}
    ]
  }
}'

Domain prompting

Python

import os
from openai import OpenAI

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"),
    # If you use a model in the Beijing region, replace the base_url with: https://dashscope.aliyuncs.com/compatible-mode/v1
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
messages = [
    {
        "role": "user",
        "content": "La segunda instrucción SELECT devuelve un número que indica la cantidad de filas que habría devuelto la primera instrucción SELECT si no se hubiera utilizado la cláusula LIMIT."
    }
]
translation_options = {
    "source_lang": "auto",
    "target_lang": "English",
    "domains": "The sentence is from Ali Cloud IT domain. It mainly involves computer-related software development and usage methods, including many terms related to computer software and hardware. Pay attention to professional troubleshooting terminologies and sentence patterns when translating. Translate into this IT domain style."
}

completion = client.chat.completions.create(
    model="qwen-mt-plus",  # This example uses qwen-mt-plus. You can replace the model name as needed.
    messages=messages,
    extra_body={
        "translation_options": translation_options
    }
)
print(completion.choices[0].message.content)

Node.js

// Node.js v18 or later is required. Run the code in an ES Module environment.
import OpenAI from "openai";

const openai = new OpenAI(
    {
        // If you have not configured the environment variable, replace the following line with your Alibaba Cloud Model Studio API key: apiKey: "sk-xxx",
        apiKey: process.env.DASHSCOPE_API_KEY,
        // The following baseURL is for the Singapore region. If you use a model in the Beijing region, replace the baseURL with: https://dashscope.aliyuncs.com/compatible-mode/v1
        baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
    }
);
const completion = await openai.chat.completions.create({
    model: "qwen-mt-plus",
    messages: [
        { role: "user", content: "La segunda instrucción SELECT devuelve un número que indica la cantidad de filas que habría devuelto la primera instrucción SELECT si no se hubiera utilizado la cláusula LIMIT." }
    ],
    translation_options: {
        source_lang: "auto",
        target_lang: "English",
        domains: "The sentence is from Ali Cloud IT domain. It mainly involves computer-related software development and usage methods, including many terms related to computer software and hardware. Pay attention to professional troubleshooting terminologies and sentence patterns when translating. Translate into this IT domain style."
    }
});
console.log(JSON.stringify(completion));

curl

# ======= Important =======
# The API keys for the Singapore and Beijing regions are different.
# The following endpoint is for the Singapore region. If you use a model in the Beijing region, replace the endpoint with: https://dashscope-aliyuncs.com/compatible-mode/v1/chat/completions
# === Delete this comment before execution ===
curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "model": "qwen-mt-plus",
  "messages": [
    {
      "role": "user",
      "content": "La segunda instrucción SELECT devuelve un número que indica la cantidad de filas que habría devuelto la primera instrucción SELECT si no se hubiera utilizado la cláusula LIMIT."
    }
  ],
  "translation_options": {
    "source_lang": "auto",
    "target_lang": "English",
    "domains": "The sentence is from Ali Cloud IT domain. It mainly involves computer-related software development and usage methods, including many terms related to computer software and hardware. Pay attention to professional troubleshooting terminologies and sentence patterns when translating. Translate into this IT domain style."
  }
}'

model string (Required)

The name of the model. Supported models: qwen-mt-plus, qwen-mt-flash, qwen-mt-lite, and qwen-mt-turbo.

messages array (Required)

An array of messages that provides context to the model. Only user messages are supported.

Message type

User Message object (Required)

A user message that contains the sentence to be translated.

Properties

content string (Required)

The sentence to be translated.

role string (Required)

The role of the user message. This must be set to user.

stream boolean (Optional) Defaults to false.

Specifies whether to return the response in streaming mode.

Valid values:

false: The entire response is returned after it is fully generated.
true: The response is returned in chunks as it is generated. The client must read the chunks to reconstruct the complete response.

Note

Currently, only qwen-mt-flash and qwen-mt-lite support incremental data return, where each chunk contains only the newly generated content. qwen-mt-plus and qwen-mt-turbo return data non-incrementally, where each chunk contains the entire sequence generated so far. This behavior cannot be changed. For example:

I didn

I didn't

I didn't laugh

I didn't laugh after

...

stream_options object (Optional)

The configuration items for streaming output. This parameter takes effect only when stream is set to true.

Properties

include_usage boolean (Optional) Defaults to false

Specifies whether to include token usage information in the last data chunk.

Valid values:

true
false

max_tokens integer (Optional)

The maximum number of tokens to generate. If the generated content exceeds this value, the response is truncated.

The default and maximum values are the maximum output length of the model. For more information, see Model selection.

seed integer (Optional)

The random number seed. This ensures that results are reproducible with the same input and parameters. If you use the same seed and other parameters in a call, the model returns the same result as consistently as possible.

Value range: [0,2³¹-1].

temperature float (Optional) Defaults to 0.65

The sampling temperature, which controls the diversity of the generated text.

A higher temperature value results in more diverse text. A lower temperature value results in more deterministic text.

Value range: [0, 2)

Both `temperature` and `top_p` control the diversity of the generated text. Set only one of them.

top_p float (Optional) Defaults to 0.8

The probability threshold for nucleus sampling, which controls the diversity of the generated text.

A higher `top_p` value results in more diverse text. A lower `top_p` value results in more deterministic text.

Value range: (0, 1.0]

Both `temperature` and `top_p` control the diversity of the generated text. Set only one of them.

top_k integer (Optional) Defaults to 1

The size of the candidate set for sampling during generation. For example, if you set this parameter to 50, only the 50 tokens with the highest scores in a single generation are used to form the candidate set for sampling. A larger value increases randomness, and a smaller value increases determinism. If the value is `None` or greater than 100, the `top_k` policy is disabled and only the `top_p` policy takes effect.

The value must be greater than or equal to 0.

This parameter is not a standard OpenAI parameter. When you use the Python SDK, place this parameter in the extra_body object. For example: extra_body={"top_k": xxx}. When you use the Node.js SDK or make an HTTP call, pass it as a top-level parameter.

repetition_penalty float (Optional) Defaults to 1.0

The penalty for repetition in consecutive sequences during model generation. A higher `repetition_penalty` value reduces repetition. A value of 1.0 indicates no penalty. The value must be greater than 0, but there is no strict value range.

This parameter is not a standard OpenAI parameter. For Python SDK calls, place it in the extra_body object, as follows: extra_body={"repetition_penalty": xxx}. For Node.js SDK or HTTP calls, pass it as a top-level parameter.

translation_options object (Required)

The translation parameters to configure.

Properties

source_lang string (Required)

The full English name of the source language. For more information, see Supported languages. If you set this to auto, the model automatically detects the input language.

target_lang string (Required)

The full English name of the target language. For more information, see Supported languages.

terms array (Optional)

The array of terms to set when you use the Term intervention feature.

Properties

source string (Required)

The term in the source language.

target string (Required)

The term in the target language.

tm_list array (Optional)

The array of translation memories to set when you use the Translation memory feature.

Properties

source string (Required)

The statement in the source language.

target string (Required)

The statement in the target language.

domains string (Optional)

The domain prompt to set when you use the Domain prompting feature.

Domain prompts must be in English.

This parameter is not a standard OpenAI parameter. When you use the Python SDK, place this parameter in the extra_body object. For example: extra_body={"translation_options": xxx}. When you use the Node.js SDK or make an HTTP call, pass it as a top-level parameter.

Chat response object (non-streaming output)	{ "id": "chatcmpl-999a5d8a-f646-4039-968a-167743ae0f22", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { "content": "I didn't laugh after watching this video.", "refusal": null, "role": "assistant", "annotations": null, "audio": null, "function_call": null, "tool_calls": null } } ], "created": 1762346157, "model": "qwen-mt-plus", "object": "chat.completion", "service_tier": null, "system_fingerprint": null, "usage": { "completion_tokens": 9, "prompt_tokens": 53, "total_tokens": 62, "completion_tokens_details": null, "prompt_tokens_details": null } }
id `string` The unique identifier of the request.
choices `array` An array of content generated by the model. Properties finish_reason `string` The reason why the model stopped generating content. The following two scenarios apply: `stop`: The model stopped generating content naturally. `length`: The generation stopped because the output length limit was reached. index `integer` The index of the current object in the `choices` array. message `object` The message output by the model. Properties content `string` The translation result from the model. refusal `string` This parameter is currently fixed to `null`. role `string` The role of the message. This is fixed to `assistant`. audio `object` This parameter is currently fixed to `null`. function_call `object` This parameter is currently fixed to `null`. tool_calls `array` This parameter is currently fixed to `null`.
created `integer` The UNIX timestamp when the request was created.
model `string` The model used for the request.
object `string` This is always `chat.completion`.
service_tier `string` This parameter is currently fixed to `null`.
system_fingerprint `string` This parameter is currently fixed to `null`.
usage `object` The token usage information for the request. Properties completion_tokens `integer` The number of tokens in the model output. prompt_tokens `integer` The number of tokens in the input. total_tokens `integer` The total number of tokens consumed. This is the sum of `prompt_tokens` and `completion_tokens`. completion_tokens_details `object` This parameter is currently fixed to `null`. prompt_tokens_details `object` This parameter is currently fixed to `null`.

Chat response chunk object (streaming output)	Incremental output {"id": "chatcmpl-d8aa6596-b366-4ed0-9f6d-2e89247f554e", "choices": [{"delta": {"content": "", "function_call": null, "refusal": null, "role": "assistant", "tool_calls": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1762504029, "model": "qwen-mt-flash", "object": "chat.completion.chunk", "service_tier": null, "system_fingerprint": null, "usage": null} {"id": "chatcmpl-d8aa6596-b366-4ed0-9f6d-2e89247f554e", "choices": [{"delta": {"content": "I", "function_call": null, "refusal": null, "role": null, "tool_calls": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1762504029, "model": "qwen-mt-flash", "object": "chat.completion.chunk", "service_tier": null, "system_fingerprint": null, "usage": null} {"id": "chatcmpl-d8aa6596-b366-4ed0-9f6d-2e89247f554e", "choices": [{"delta": {"content": " didn", "function_call": null, "refusal": null, "role": null, "tool_calls": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1762504029, "model": "qwen-mt-flash", "object": "chat.completion.chunk", "service_tier": null, "system_fingerprint": null, "usage": null} {"id": "chatcmpl-d8aa6596-b366-4ed0-9f6d-2e89247f554e", "choices": [{"delta": {"content": "'t", "function_call": null, "refusal": null, "role": null, "tool_calls": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1762504029, "model": "qwen-mt-flash", "object": "chat.completion.chunk", "service_tier": null, "system_fingerprint": null, "usage": null} {"id": "chatcmpl-d8aa6596-b366-4ed0-9f6d-2e89247f554e", "choices": [{"delta": {"content": " laugh", "function_call": null, "refusal": null, "role": null, "tool_calls": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1762504029, "model": "qwen-mt-flash", "object": "chat.completion.chunk", "service_tier": null, "system_fingerprint": null, "usage": null} {"id": "chatcmpl-d8aa6596-b366-4ed0-9f6d-2e89247f554e", "choices": [{"delta": {"content": " after", "function_call": null, "refusal": null, "role": null, "tool_calls": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1762504029, "model": "qwen-mt-flash", "object": "chat.completion.chunk", "service_tier": null, "system_fingerprint": null, "usage": null} {"id": "chatcmpl-d8aa6596-b366-4ed0-9f6d-2e89247f554e", "choices": [{"delta": {"content": " watching", "function_call": null, "refusal": null, "role": null, "tool_calls": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1762504029, "model": "qwen-mt-flash", "object": "chat.completion.chunk", "service_tier": null, "system_fingerprint": null, "usage": null} {"id": "chatcmpl-d8aa6596-b366-4ed0-9f6d-2e89247f554e", "choices": [{"delta": {"content": " this", "function_call": null, "refusal": null, "role": null, "tool_calls": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1762504029, "model": "qwen-mt-flash", "object": "chat.completion.chunk", "service_tier": null, "system_fingerprint": null, "usage": null} {"id": "chatcmpl-d8aa6596-b366-4ed0-9f6d-2e89247f554e", "choices": [{"delta": {"content": " video", "function_call": null, "refusal": null, "role": null, "tool_calls": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1762504029, "model": "qwen-mt-flash", "object": "chat.completion.chunk", "service_tier": null, "system_fingerprint": null, "usage": null} {"id": "chatcmpl-d8aa6596-b366-4ed0-9f6d-2e89247f554e", "choices": [{"delta": {"content": ".", "function_call": null, "refusal": null, "role": null, "tool_calls": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1762504029, "model": "qwen-mt-flash", "object": "chat.completion.chunk", "service_tier": null, "system_fingerprint": null, "usage": null} {"id": "chatcmpl-d8aa6596-b366-4ed0-9f6d-2e89247f554e", "choices": [{"delta": {"content": "", "function_call": null, "refusal": null, "role": null, "tool_calls": null}, "finish_reason": "stop", "index": 0, "logprobs": null}], "created": 1762504029, "model": "qwen-mt-flash", "object": "chat.completion.chunk", "service_tier": null, "system_fingerprint": null, "usage": null} {"id": "chatcmpl-d8aa6596-b366-4ed0-9f6d-2e89247f554e", "choices": [{"delta": {"content": "", "function_call": null, "refusal": null, "role": null, "tool_calls": null}, "finish_reason": "stop", "index": 0, "logprobs": null}], "created": 1762504029, "model": "qwen-mt-flash", "object": "chat.completion.chunk", "service_tier": null, "system_fingerprint": null, "usage": null} {"id": "chatcmpl-d8aa6596-b366-4ed0-9f6d-2e89247f554e", "choices": [], "created": 1762504029, "model": "qwen-mt-flash", "object": "chat.completion.chunk", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "prompt_tokens": 56, "total_tokens": 65, "completion_tokens_details": null, "prompt_tokens_details": null}} Non-incremental output {"id":"chatcmpl-478e183e-cbdc-4ea0-aeae-4c2ba1d03e4d","choices":[{"delta":{"content":"","function_call":null,"refusal":null,"role":"assistant","tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1762346453,"model":"qwen-mt-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-478e183e-cbdc-4ea0-aeae-4c2ba1d03e4d","choices":[{"delta":{"content":"I","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1762346453,"model":"qwen-mt-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-478e183e-cbdc-4ea0-aeae-4c2ba1d03e4d","choices":[{"delta":{"content":"I didn","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1762346453,"model":"qwen-mt-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-478e183e-cbdc-4ea0-aeae-4c2ba1d03e4d","choices":[{"delta":{"content":"I didn’t","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1762346453,"model":"qwen-mt-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-478e183e-cbdc-4ea0-aeae-4c2ba1d03e4d","choices":[{"delta":{"content":"I didn’t laugh","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1762346453,"model":"qwen-mt-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-478e183e-cbdc-4ea0-aeae-4c2ba1d03e4d","choices":[{"delta":{"content":"I didn’t laugh after","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1762346453,"model":"qwen-mt-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-478e183e-cbdc-4ea0-aeae-4c2ba1d03e4d","choices":[{"delta":{"content":"I didn’t laugh after watching","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1762346453,"model":"qwen-mt-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-478e183e-cbdc-4ea0-aeae-4c2ba1d03e4d","choices":[{"delta":{"content":"I didn’t laugh after watching this","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1762346453,"model":"qwen-mt-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-478e183e-cbdc-4ea0-aeae-4c2ba1d03e4d","choices":[{"delta":{"content":"I didn’t laugh after watching this video","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1762346453,"model":"qwen-mt-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-478e183e-cbdc-4ea0-aeae-4c2ba1d03e4d","choices":[{"delta":{"content":"I didn’t laugh after watching this video.","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1762346453,"model":"qwen-mt-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-478e183e-cbdc-4ea0-aeae-4c2ba1d03e4d","choices":[{"delta":{"content":"I didn’t laugh after watching this video.","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":"stop","index":0,"logprobs":null}],"created":1762346453,"model":"qwen-mt-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-478e183e-cbdc-4ea0-aeae-4c2ba1d03e4d","choices":[{"delta":{"content":"I didn’t laugh after watching this video.","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":"stop","index":0,"logprobs":null}],"created":1762346453,"model":"qwen-mt-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null} {"id":"chatcmpl-478e183e-cbdc-4ea0-aeae-4c2ba1d03e4d","choices":[],"created":1762346453,"model":"qwen-mt-plus","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":{"completion_tokens":9,"prompt_tokens":56,"total_tokens":65,"completion_tokens_details":null,"prompt_tokens_details":null}}
id `string` The unique identifier of the call. Each chunk object has the same ID.
choices `array` An array of content generated by the model. If `include_usage` is set to `true`, this array is empty in the last chunk. Properties delta `object` The output content returned in streaming mode. Properties content `string` The translation result. qwen-mt-flash and qwen-mt-lite provide incremental updates. qwen-mt-plus and qwen-mt-turbo provide non-incremental updates. function_call `object` This parameter is currently fixed to `null`. refusal `object` This parameter is currently fixed to `null`. role `string` The role of the message object. This has a value only in the first chunk. finish_reason `string` The model stops generating for one of three reasons: `stop`: The model stopped generating content naturally. `null`: The generation is not finished. `length`: The generation stopped because the output length limit was reached. index `integer` The index of the current response in the `choices` array.
created `integer` The UNIX timestamp when the request was created. Each chunk has the same timestamp.
model `string` The model used for the request.
object `string` This is always `chat.completion.chunk`.
service_tier `string` This parameter is currently fixed to `null`.
system_fingerprint `string` This parameter is currently fixed to `null`.
usage `object` The tokens consumed by the request. This is returned in the last chunk only when `include_usage` is `true`. Properties completion_tokens `integer` The number of tokens in the model output. prompt_tokens `integer` The number of input tokens. total_tokens `integer` The total number of tokens. This is the sum of `prompt_tokens` and `completion_tokens`. completion_tokens_details `object` This parameter is currently fixed to `null`. prompt_tokens_details `object` This parameter is currently fixed to `null`.

DashScope

Singapore region

For HTTP, the endpoint is: POST https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/text-generation/generation

For SDK, set base_url to:

Python code

dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

Java code

Method 1:

import com.alibaba.dashscope.protocol.Protocol;
Generation gen = new Generation(Protocol.HTTP.getValue(), "https://dashscope-intl.aliyuncs.com/api/v1");

Method 2:

import com.alibaba.dashscope.utils.Constants;
Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";

Beijing region

For HTTP, the endpoint is: POST https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation

For SDK calls, you do not need to configure base_url.

You must create an API key and export the API key as an environment variable. If using the DashScope SDK, install the DashScope SDK.

Request body	Basic usage Python import os import dashscope # If you use a model in the Beijing region, replace the base_url with: https://dashscope.aliyuncs.com/api/v1 dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1' messages = [ { "role": "user", "content": "No me reí después de ver este video" } ] translation_options = { "source_lang": "auto", "target_lang": "English", } response = dashscope.Generation.call( # 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'), model="qwen-mt-plus", # This example uses qwen-mt-plus. You can replace the model name as needed. messages=messages, result_format='message', translation_options=translation_options ) print(response.output.choices[0].message.content) Java // DashScope SDK 2.20.6 or later is required. import java.lang.System; import java.util.Collections; import com.alibaba.dashscope.aigc.generation.Generation; import com.alibaba.dashscope.aigc.generation.GenerationParam; import com.alibaba.dashscope.aigc.generation.GenerationResult; import com.alibaba.dashscope.aigc.generation.TranslationOptions; import com.alibaba.dashscope.common.Message; import com.alibaba.dashscope.common.Role; import com.alibaba.dashscope.exception.ApiException; import com.alibaba.dashscope.exception.InputRequiredException; import com.alibaba.dashscope.exception.NoApiKeyException; import com.alibaba.dashscope.protocol.Protocol; public class Main { public static GenerationResult callWithMessage() throws ApiException, NoApiKeyException, InputRequiredException { // The following URL is for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1 Generation gen = new Generation(Protocol.HTTP.getValue(), "https://dashscope-intl.aliyuncs.com/api/v1"); Message userMsg = Message.builder() .role(Role.USER.getValue()) .content("No me reí después de ver este video") .build(); TranslationOptions options = TranslationOptions.builder() .sourceLang("auto") .targetLang("English") .build(); GenerationParam param = GenerationParam.builder() // If you have not configured the environment variable, replace the following line with your Alibaba Cloud Model Studio API key: .apiKey("sk-xxx") .apiKey(System.getenv("DASHSCOPE_API_KEY")) .model("qwen-mt-plus") .messages(Collections.singletonList(userMsg)) .resultFormat(GenerationParam.ResultFormat.MESSAGE) .translationOptions(options) .build(); return gen.call(param); } public static void main(String[] args) { try { GenerationResult result = callWithMessage(); System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent()); } catch (ApiException \| NoApiKeyException \| InputRequiredException e) { System.err.println("Error message: "+e.getMessage()); e.printStackTrace(); } finally { System.exit(0); } } } curl # ======= Important ======= # If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation # === Delete this comment before execution ==== curl -X POST https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/text-generation/generation \ -H "Authorization: $DASHSCOPE_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen-mt-plus", "input": { "messages": [ { "content": "No me reí después de ver este video", "role": "user" } ] }, "parameters": { "translation_options": { "source_lang": "auto", "target_lang": "English" } } }' Term intervention Python import os import dashscope # If you use a model in the Beijing region, replace the base_url with: https://dashscope.aliyuncs.com/api/v1 dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1' messages = [ { "role": "user", "content": "Este conjunto de biosensores utiliza grafeno, un material novedoso. Su objetivo son los elementos químicos. Su agudo «sentido del olfato» le permite reflejar el estado de salud del cuerpo de forma más profunda y precisa." } ] translation_options = { "source_lang": "auto", "target_lang": "English", "terms": [ { "source": "biosensor", "target": "biological sensor" }, { "source": "grafeno", "target": "graphene" }, { "source": "elementos químicos", "target": "chemical elements" }, { "source": "estado de salud del cuerpo", "target": "health status of the body" } ] } response = dashscope.Generation.call( # 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'), model="qwen-mt-plus", # This example uses qwen-mt-plus. You can replace the model name as needed. messages=messages, result_format='message', translation_options=translation_options ) print(response.output.choices[0].message.content) Java // DashScope SDK 2.20.6 or later is required. import java.lang.System; import java.util.Collections; import java.util.Arrays; import com.alibaba.dashscope.aigc.generation.Generation; import com.alibaba.dashscope.aigc.generation.GenerationParam; import comalibaba.dashscope.aigc.generation.GenerationResult; import com.alibaba.dashscope.aigc.generation.TranslationOptions; import com.alibaba.dashscope.aigc.generation.TranslationOptions.Term; import com.alibaba.dashscope.common.Message; import com.alibaba.dashscope.common.Role; import com.alibaba.dashscope.exception.ApiException; import com.alibaba.dashscope.exception.InputRequiredException; import com.alibaba.dashscope.exception.NoApiKeyException; import com.alibaba.dashscope.protocol.Protocol; public class Main { public static GenerationResult callWithMessage() throws ApiException, NoApiKeyException, InputRequiredException { // The following URL is for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1 Generation gen = new Generation(Protocol.HTTP.getValue(), "https://dashscope-intl.aliyuncs.com/api/v1"); Message userMsg = Message.builder() .role(Role.USER.getValue()) .content("Este conjunto de biosensores utiliza grafeno, un material novedoso. Su objetivo son los elementos químicos. Su agudo «sentido del olfato» le permite reflejar el estado de salud del cuerpo de forma más profunda y precisa.") .build(); Term term1 = Term.builder() .source("biosensor") .target("biological sensor") .build(); Term term2 = Term.builder() .source("estado de salud del cuerpo") .target("health status of the body") .build(); TranslationOptions options = TranslationOptions.builder() .sourceLang("auto") .targetLang("English") .terms(Arrays.asList(term1, term2)) .build(); GenerationParam param = GenerationParam.builder() .apiKey(System.getenv("DASHSCOPE_API_KEY")) .model("qwen-mt-plus") .messages(Collections.singletonList(userMsg)) .resultFormat(GenerationParam.ResultFormat.MESSAGE) .translationOptions(options) .build(); return gen.call(param); } public static void main(String[] args) { try { GenerationResult result = callWithMessage(); System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent()); } catch (ApiException \| NoApiKeyException \| InputRequiredException e) { System.err.println("Error message: "+e.getMessage()); } System.exit(0); } } curl # ======= Important ======= # If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation # === Delete this comment before execution ==== curl -X POST https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/text-generation/generation \ -H "Authorization: $DASHSCOPE_API_KEY" \ -H 'Content-Type: application/json' \ -d '{ "model": "qwen-mt-plus", "input": { "messages": [ { "content": "Este conjunto de biosensores utiliza grafeno, un material novedoso. Su objetivo son los elementos químicos. Su agudo «sentido del olfato» le permite reflejar el estado de salud del cuerpo de forma más profunda y precisa.", "role": "user" } ] }, "parameters": { "translation_options": { "source_lang": "auto", "target_lang": "English", "terms": [ { "source": "biosensor", "target": "biological sensor" }, { "source": "estado de salud del cuerpo", "target": "health status of the body" } ] } }' Translation memory Python import os import dashscope # If you use a model in the Beijing region, replace the base_url with: https://dashscope.aliyuncs.com/api/v1 dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1' messages = [ { "role": "user", "content": "El siguiente comando muestra la información de la versión de Thrift instalada." } ] translation_options = { "source_lang": "auto", "target_lang": "English", "tm_list": [ { "source": "Puede utilizar uno de los siguientes métodos para consultar la versión del motor de un clúster:", "target": "You can use one of the following methods to query the engine version of a cluster:" }, { "source": "La versión de Thrift utilizada por nuestro HBase en la nube es la 0.9.0. Por lo tanto, recomendamos que la versión del cliente también sea la 0.9.0. Puede descargar Thrift 0.9.0 desde aquí. El paquete de código fuente descargado se utilizará posteriormente. Primero debe instalar el entorno de compilación de Thrift. Para la instalación desde el código fuente, puede consultar el sitio web oficial de Thrift.", "target": "The version of Thrift used by ApsaraDB for HBase is 0.9.0. Therefore, we recommend that you use Thrift 0.9.0 to create a client. Click here to download Thrift 0.9.0. The downloaded source code package will be used later. You must install the Thrift compiling environment first. For more information, see Thrift official website." }, { "source": "Puede instalar el SDK a través de PyPI. El comando de instalación es el siguiente:", "target": "You can run the following command in Python Package Index (PyPI) to install Elastic Container Instance SDK for Python:" } ]} response = dashscope.Generation.call( # 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'), model="qwen-mt-plus", # This example uses qwen-mt-plus. You can replace the model name as needed. messages=messages, result_format='message', translation_options=translation_options ) print(response.output.choices[0].message.content) Java // DashScope SDK 2.20.6 or later is required. import java.lang.System; import java.util.Collections; import java.util.Arrays; import com.alibaba.dashscope.aigc.generation.Generation; import com.alibaba.dashscope.aigc.generation.GenerationParam; import com.alibaba.dashscope.aigc.generation.GenerationResult; import com.alibaba.dashscope.aigc.generation.TranslationOptions; import com.alibaba.dashscope.aigc.generation.TranslationOptions.Tm; import com.alibaba.dashscope.common.Message; import com.alibaba.dashscope.common.Role; import com.alibaba.dashscope.exception.ApiException; import com.alibaba.dashscope.exception.InputRequiredException; import com.alibaba.dashscope.exception.NoApiKeyException; import com.alibaba.dashscope.protocol.Protocol; public class Main { public static GenerationResult callWithMessage() throws ApiException, NoApiKeyException, InputRequiredException { // The following URL is for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1 Generation gen = new Generation(Protocol.HTTP.getValue(), "https://dashscope-intl.aliyuncs.com/api/v1"); Message userMsg = Message.builder() .role(Role.USER.getValue()) .content("El siguiente comando muestra la información de la versión de Thrift instalada.") .build(); Tm tm1 = Tm.builder() .source("Puede utilizar uno de los siguientes métodos para consultar la versión del motor de un clúster:") .target("You can use one of the following methods to query the engine version of a cluster:") .build(); Tm tm2 = Tm.builder() .source("La versión de Thrift utilizada por nuestro HBase en la nube es la 0.9.0. Por lo tanto, recomendamos que la versión del cliente también sea la 0.9.0. Puede descargar Thrift 0.9.0 desde aquí. El paquete de código fuente descargado se utilizará posteriormente. Primero debe instalar el entorno de compilación de Thrift. Para la instalación desde el código fuente, puede consultar el sitio web oficial de Thrift.") .target("The version of Thrift used by ApsaraDB for HBase is 0.9.0. Therefore, we recommend that you use Thrift 0.9.0 to create a client. Click here to download Thrift 0.9.0. The downloaded source code package will be used later. You must install the Thrift compiling environment first. For more information, see Thrift official website.") .build(); Tm tm3 = Tm.builder() .source("Puede instalar el SDK a través de PyPI. El comando de instalación es el siguiente:") .target("You can run the following command in Python Package Index (PyPI) to install Elastic Container Instance SDK for Python:") .build(); TranslationOptions options = TranslationOptions.builder() .sourceLang("auto") .targetLang("English") .tmList(Arrays.asList(tm1, tm2, tm3)) .build(); GenerationParam param = GenerationParam.builder() .apiKey(System.getenv("DASHSCOPE_API_KEY")) .model("qwen-mt-plus") .messages(Collections.singletonList(userMsg)) .resultFormat(GenerationParam.ResultFormat.MESSAGE) .translationOptions(options) .build(); return gen.call(param); } public static void main(String[] args) { try { GenerationResult result = callWithMessage(); System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent()); } catch (ApiException \| NoApiKeyException \| InputRequiredException e) { System.err.println("Error message: "+e.getMessage()); } System.exit(0); } } curl # ======= Important ======= # If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation # === Delete this comment before execution ==== curl -X POST https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/text-generation/generation \ -H "Authorization: $DASHSCOPE_API_KEY" \ -H 'Content-Type: application/json' \ -d '{ "model": "qwen-mt-plus", "input": { "messages": [ { "content": "El siguiente comando muestra la información de la versión de Thrift instalada.", "role": "user" } ] }, "parameters": { "translation_options": { "source_lang": "auto", "target_lang": "English", "tm_list":[ {"source": "Puede utilizar uno de los siguientes métodos para consultar la versión del motor de un clúster:", "target": "You can use one of the following methods to query the engine version of a cluster:"}, {"source": "La versión de Thrift utilizada por nuestro HBase en la nube es la 0.9.0. Por lo tanto, recomendamos que la versión del cliente también sea la 0.9.0. Puede descargar Thrift 0.9.0 desde aquí. El paquete de código fuente descargado se utilizará posteriormente. Primero debe instalar el entorno de compilación de Thrift. Para la instalación desde el código fuente, puede consultar el sitio web oficial de Thrift.", "target": "The version of Thrift used by ApsaraDB for HBase is 0.9.0. Therefore, we recommend that you use Thrift 0.9.0 to create a client. Click here to download Thrift 0.9.0. The downloaded source code package will be used later. You must install the Thrift compiling environment first. For more information, see Thrift official website."}, {"source": "Puede instalar el SDK a través de PyPI. El comando de instalación es el siguiente:", "target": "You can run the following command in Python Package Index (PyPI) to install Elastic Container Instance SDK for Python:"} ] } }' Domain prompting Python import os import dashscope # If you use a model in the Beijing region, replace the base_url with: https://dashscope.aliyuncs.com/api/v1 dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1' messages = [ { "role": "user", "content": "La segunda instrucción SELECT devuelve un número que indica la cantidad de filas que habría devuelto la primera instrucción SELECT si no se hubiera utilizado la cláusula LIMIT." } ] translation_options = { "source_lang": "auto", "target_lang": "English", "domains": "The sentence is from Ali Cloud IT domain. It mainly involves computer-related software development and usage methods, including many terms related to computer software and hardware. Pay attention to professional troubleshooting terminologies and sentence patterns when translating. Translate into this IT domain style." } response = dashscope.Generation.call( # 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'), model="qwen-mt-plus", # This example uses qwen-mt-plus. You can replace the model name as needed. messages=messages, result_format='message', translation_options=translation_options ) print(response.output.choices[0].message.content) Java // DashScope SDK 2.20.6 or later is required. import java.lang.System; import java.util.Collections; import com.alibaba.dashscope.aigc.generation.Generation; import com.alibaba.dashscope.aigc.generation.GenerationParam; import com.alibaba.dashscope.aigc.generation.GenerationResult; import com.alibaba.dashscope.aigc.generation.TranslationOptions; import com.alibaba.dashscope.common.Message; import com.alibaba.dashscope.common.Role; import com.alibaba.dashscope.exception.ApiException; import com.alibaba.dashscope.exception.InputRequiredException; import com.alibaba.dashscope.exception.NoApiKeyException; import com.alibaba.dashscope.protocol.Protocol; public class Main { public static GenerationResult callWithMessage() throws ApiException, NoApiKeyException, InputRequiredException { // The following URL is for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1 Generation gen = new Generation(Protocol.HTTP.getValue(), "https://dashscope-intl.aliyuncs.com/api/v1"); Message userMsg = Message.builder() .role(Role.USER.getValue()) .content("La segunda instrucción SELECT devuelve un número que indica la cantidad de filas que habría devuelto la primera instrucción SELECT si no se hubiera utilizado la cláusula LIMIT.") .build(); TranslationOptions options = TranslationOptions.builder() .sourceLang("auto") .targetLang("English") .domains("The sentence is from Ali Cloud IT domain. It mainly involves computer-related software development and usage methods, including many terms related to computer software and hardware. Pay attention to professional troubleshooting terminologies and sentence patterns when translating. Translate into this IT domain style.") .build(); GenerationParam param = GenerationParam.builder() .apiKey(System.getenv("DASHSCOPE_API_KEY")) .model("qwen-mt-plus") .messages(Collections.singletonList(userMsg)) .resultFormat(GenerationParam.ResultFormat.MESSAGE) .translationOptions(options) .build(); return gen.call(param); } public static void main(String[] args) { try { GenerationResult result = callWithMessage(); System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent()); } catch (ApiException \| NoApiKeyException \| InputRequiredException e) { System.err.println("Error message: "+e.getMessage()); } System.exit(0); } } curl # ======= Important ======= # If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation # === Delete this comment before execution ==== curl -X POST https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/text-generation/generation \ -H "Authorization: $DASHSCOPE_API_KEY" \ -H 'Content-Type: application/json' \ -d '{ "model": "qwen-mt-plus", "input": { "messages": [ { "content": "La segunda instrucción SELECT devuelve un número que indica la cantidad de filas que habría devuelto la primera instrucción SELECT si no se hubiera utilizado la cláusula LIMIT.", "role": "user" } ] }, "parameters": { "translation_options": { "source_lang": "auto", "target_lang": "English", "domains": "The sentence is from Ali Cloud IT domain. It mainly involves computer-related software development and usage methods, including many terms related to computer software and hardware. Pay attention to professional troubleshooting terminologies and sentence patterns when translating. Translate into this IT domain style."} } }'
model `string` (Required) The name of the model. Supported models: qwen-mt-plus, qwen-mt-flash, qwen-mt-lite, and qwen-mt-turbo.
messages `array` (Required) An array of messages that provides context to the model. Only user messages are supported. Message type User Message `object` (Required) A user message that contains the sentence to be translated. Properties content `string` (Required) The sentence to be translated. role `string` (Required) The role of the user message. This must be set to `user`.
max_tokens `integer` (Optional) The maximum number of tokens to generate. If the generated content exceeds this value, the response is truncated. The default and maximum values are the maximum output length of the model. For more information, see Model selection. In the Java SDK, the parameter is maxTokens. For an HTTP call, place max_tokens in the parameters object.
seed `integer` (Optional) The random number seed. This ensures that results are reproducible with the same input and parameters. If you use the same `seed` and other parameters in a call, the model returns the same result as consistently as possible. Value range: `[0,2³¹-1]`. When you make an HTTP call, place seed in the parameters object.
temperature `float` (Optional) Defaults to 0.65 The sampling temperature, which controls the diversity of the generated text. A higher temperature value results in more diverse text. A lower temperature value results in more deterministic text. Value range: [0, 2) Both `temperature` and `top_p` control the diversity of the generated text. Set only one of them. When you make an HTTP call, place temperature in the parameters object.
top_p `float` (Optional) Defaults to 0.8 The probability threshold for nucleus sampling, which controls the diversity of the generated text. A higher `top_p` value results in more diverse text. A lower `top_p` value results in more deterministic text. Value range: (0, 1.0] Both `temperature` and `top_p` control the diversity of the generated text. Set only one of them. In the Java SDK, the parameter is topP. For HTTP calls, place top_p in the parameters object.
repetition_penalty `float` (Optional) Defaults to 1.0 The penalty for repetition in consecutive sequences during model generation. A higher `repetition_penalty` value reduces repetition. A value of 1.0 indicates no penalty. The value must be greater than 0, but there is no strict value range. In the Java SDK, the parameter is repetitionPenalty. When you make an HTTP call, place repetition_penalty in the parameters object.
top_k `integer` (Optional) Defaults to 1 The size of the candidate set for sampling during generation. For example, if you set this parameter to 50, only the 50 tokens with the highest scores in a single generation are used to form the candidate set for sampling. A larger value increases randomness, and a smaller value increases determinism. If the value is `None` or greater than 100, the `top_k` policy is disabled and only the `top_p` policy takes effect. The value must be greater than or equal to 0. In the Java SDK, this parameter is topKtop_k in the parameters object.
stream `boolean` (Optional) Specifies whether to return the response in streaming mode. Valid values: `false`: The entire response is returned after it is fully generated. `true`: The response is returned in chunks as it is generated. The client must read the chunks to reconstruct the complete response. Note Currently, only qwen-mt-flash and qwen-mt-lite support incremental data return, where each chunk contains only the newly generated content. qwen-mt-plus and qwen-mt-turbo return data non-incrementally, where each chunk contains the entire sequence generated so far. This behavior cannot be changed. For example: I I didn I didn't I didn't laugh I didn't laugh after ... This parameter is supported only by the Python SDK. To implement streaming output with the Java SDK, you can call the `streamCall` interface. To implement streaming output with an HTTP call, set `X-DashScope-SSE` to `enable` in the header.
translation_options `object` (Required) The translation parameters to configure. Properties source_lang `string` (Required) The full English name of the source language. For more information, see Supported languages. If you set this to `auto`, the model automatically detects the input language. target_lang `string` (Required) The full English name of the target language. For more information, see Supported languages. terms `array` (Optional) The array of terms to set when you use the Term intervention feature. Properties source `string` (Required) The term in the source language. target `string` (Required) The term in the target language. tm_list `array` (Optional) The array of translation memories to set when you use the Translation memory feature. Properties source `string` (Required) The statement in the source language. target `string` (Required) The statement in the target language. domains `string` (Optional) The domain prompt to set when you use the Domain prompting feature. Domain prompts must be in English. In the Java SDK, this parameter is `translationOptions`. When you make an HTTP call, place translation_options in the parameters object.

Chat response object (Same for streaming and non-streaming output)	`{ "status_code": 200, "request_id": "9b4ec3b2-6d29-40a6-a08b-7e3c9a51c289", "code": "", "message": "", "output": { "text": null, "finish_reason": "stop", "choices": [ { "finish_reason": "stop", "message": { "role": "assistant", "content": "I didn't laugh after watching this video." } } ], "model_name": "qwen-mt-plus" }, "usage": { "input_tokens": 53, "output_tokens": 9, "total_tokens": 62 } }`
status_code `string` The status code of the request. A value of 200 indicates that the request is successful. Otherwise, the request failed. The Java SDK does not return this parameter. If the call fails, an exception is thrown. The exception message contains the content of status_code and message.
request_id `string` The unique identifier of the call. In the Java SDK, the returned parameter is requestId
code `string` The error code. This is empty if the call is successful. Only the Python SDK returns this parameter.
output `object` The information about the call result. Properties text `string` This parameter is currently fixed to `null`. finish_reason `string` The reason why the model stopped generating content. Valid values: `null`: The generation is in progress. `stop`: The model stopped generating content naturally. `length`: The generation stopped because the output length limit was reached. choices `array` The output information from the model. Properties finish_reason `string` Consider the following scenarios: `null`: The generation is in progress. `stop`: The model stopped generating content naturally. `length`: The generation stopped because the output length limit was reached. message `object` The message object output by the model. Properties role `string` The role of the output message. This is fixed to `assistant`. content `string` The translation result.
usage `object` The token usage details for the request. Properties input_tokens `integer` The number of input tokens. output_tokens `integer` The number of output tokens. total_tokens `integer` The total number of tokens. This value is the sum of input_tokens and output_tokens.

Error codes

If the model call fails and an error message is returned, see Error messages to resolve the issue.

OpenAI compatibility

Singapore region

Beijing region

Request body

Basic usage

Python

Node.js

curl

Term intervention

Python

Node.js

curl

Translation memory

Python

Node.js

curl

Domain prompting

Python

Node.js

curl

Chat response object (non-streaming output)

Chat response chunk object (streaming output)

Incremental output

Non-incremental output

DashScope

Singapore region

Python code

Java code

Beijing region

Request body

Basic usage

Python

Java

curl

Term intervention

Python

Java

curl

Translation memory

Python

Java

curl

Domain prompting

Python

Java

curl

Chat response object (Same for streaming and non-streaming output)

Error codes