chore: 添加虚拟环境到仓库

- 添加 backend_service/venv 虚拟环境
- 包含所有Python依赖包
- 注意：虚拟环境约393MB，包含12655个文件

backend_service/venv/lib/python3.13/site-packages/dashscope/aigc/__init__.py

@@ -0,0 +1,20 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from .conversation import Conversation, History, HistoryItem
+from .generation import Generation, AioGeneration
+from .image_synthesis import ImageSynthesis, AioImageSynthesis
+from .multimodal_conversation import MultiModalConversation, AioMultiModalConversation
+from .video_synthesis import VideoSynthesis, AioVideoSynthesis
+
+__all__ = [
+    Generation,
+    AioGeneration,
+    Conversation,
+    HistoryItem,
+    History,
+    ImageSynthesis,
+    AioImageSynthesis,
+    MultiModalConversation,
+    AioMultiModalConversation,
+    VideoSynthesis,
+    AioVideoSynthesis,
+]

backend_service/venv/lib/python3.13/site-packages/dashscope/aigc/chat_completion.py

@@ -0,0 +1,282 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import json
+from typing import Any, Dict, Generator, List, Union
+
+import dashscope
+from dashscope.aigc.generation import Generation
+from dashscope.api_entities.chat_completion_types import (ChatCompletion,
+                                                          ChatCompletionChunk)
+from dashscope.api_entities.dashscope_response import (GenerationResponse,
+                                                       Message)
+from dashscope.client.base_api import BaseAioApi, CreateMixin
+from dashscope.common.error import InputRequired, ModelRequired
+from dashscope.common.utils import _get_task_group_and_task
+
+
+class Completions(CreateMixin):
+    """Support openai compatible chat completion interface.
+
+    """
+    SUB_PATH = ''
+
+    @classmethod
+    def create(
+        cls,
+        *,
+        model: str,
+        messages: List[Message],
+        stream: bool = False,
+        temperature: float = None,
+        top_p: float = None,
+        top_k: int = None,
+        stop: Union[List[str], List[List[int]]] = None,
+        max_tokens: int = None,
+        repetition_penalty: float = None,
+        api_key: str = None,
+        workspace: str = None,
+        extra_headers: Dict = None,
+        extra_body: Dict = None,
+        **kwargs
+    ) -> Union[ChatCompletion, Generator[ChatCompletionChunk, None, None]]:
+        """Call openai compatible chat completion model service.
+
+        Args:
+            model (str): The requested model, such as qwen-long
+            messages (list): The generation messages.
+                examples:
+                    [{'role': 'user',
+                      'content': 'The weather is fine today.'},
+                      {'role': 'assistant', 'content': 'Suitable for outings'}]
+            stream(bool, `optional`): Enable server-sent events
+                (ref: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events)  # noqa E501
+                the result will back partially[qwen-turbo,bailian-v1].
+            temperature(float, `optional`): Used to control the degree
+                of randomness and diversity. Specifically, the temperature
+                value controls the degree to which the probability distribution
+                of each candidate word is smoothed when generating text.
+                A higher temperature value will reduce the peak value of
+                the probability, allowing more low-probability words to be
+                selected, and the generated results will be more diverse;
+                while a lower temperature value will enhance the peak value
+                of the probability, making it easier for high-probability
+                words to be selected, the generated results are more
+                deterministic.
+            top_p(float, `optional`): A sampling strategy, called nucleus
+                sampling, where the model considers the results of the
+                tokens with top_p probability mass. So 0.1 means only
+                the tokens comprising the top 10% probability mass are
+                considered.
+            top_k(int, `optional`): The size of the sample candidate set when generated.  # noqa E501
+                For example, when the value is 50, only the 50 highest-scoring tokens  # noqa E501
+                in a single generation form a randomly sampled candidate set. # noqa E501
+                The larger the value, the higher the randomness generated;  # noqa E501
+                the smaller the value, the higher the certainty generated. # noqa E501
+                The default value is 0, which means the top_k policy is  # noqa E501
+                not enabled. At this time, only the top_p policy takes effect. # noqa E501
+            stop(list[str] or list[list[int]], `optional`): Used to control the generation to stop  # noqa E501
+                when encountering setting str or token ids, the result will not include # noqa E501
+                stop words or tokens.
+            max_tokens(int, `optional`): The maximum token num expected to be output. It should be # noqa E501
+                noted that the length generated by the model will only be less than max_tokens,  # noqa E501
+                not necessarily equal to it. If max_tokens is set too large, the service will # noqa E501
+                directly prompt that the length exceeds the limit. It is generally # noqa E501
+                not recommended to set this value.
+            repetition_penalty(float, `optional`): Used to control the repeatability when generating models.  # noqa E501
+                Increasing repetition_penalty can reduce the duplication of model generation.  # noqa E501
+                1.0 means no punishment.
+            api_key (str, optional): The api api_key, can be None,
+                if None, will get by default rule.
+            workspace (str, optional): The bailian workspace id.
+            **kwargs:
+                timeout: set request timeout.
+        Raises:
+            InvalidInput: The history and auto_history are mutually exclusive.
+
+        Returns:
+            Union[ChatCompletion,
+                  Generator[ChatCompletionChunk, None, None]]: If
+            stream is True, return Generator, otherwise ChatCompletion.
+        """
+        if messages is None or not messages:
+            raise InputRequired('Messages is required!')
+        if model is None or not model:
+            raise ModelRequired('Model is required!')
+        data = {}
+        data['model'] = model
+        data['messages'] = messages
+        if temperature is not None:
+            data['temperature'] = temperature
+        if top_p is not None:
+            data['top_p'] = top_p
+        if top_k is not None:
+            data['top_k'] = top_k
+        if stop is not None:
+            data['stop'] = stop
+        if max_tokens is not None:
+            data[max_tokens] = max_tokens
+        if repetition_penalty is not None:
+            data['repetition_penalty'] = repetition_penalty
+        if extra_body is not None and extra_body:
+            data = {**data, **extra_body}
+
+        if extra_headers is not None and extra_headers:
+            kwargs = {
+                'headers': extra_headers
+            } if kwargs else {
+                **kwargs,
+                **{
+                    'headers': extra_headers
+                }
+            }
+
+        response = super().call(data=data,
+                                path='chat/completions',
+                                base_address=dashscope.base_compatible_api_url,
+                                api_key=api_key,
+                                flattened_output=True,
+                                stream=stream,
+                                workspace=workspace,
+                                **kwargs)
+        if stream:
+            return (ChatCompletionChunk(**item) for _, item in response)
+        else:
+            return ChatCompletion(**response)
+
+
+class AioGeneration(BaseAioApi):
+    task = 'text-generation'
+    """API for AI-Generated Content(AIGC) models.
+
+    """
+    class Models:
+        """@deprecated, use qwen_turbo instead"""
+        qwen_v1 = 'qwen-v1'
+        """@deprecated, use qwen_plus instead"""
+        qwen_plus_v1 = 'qwen-plus-v1'
+
+        bailian_v1 = 'bailian-v1'
+        dolly_12b_v2 = 'dolly-12b-v2'
+        qwen_turbo = 'qwen-turbo'
+        qwen_plus = 'qwen-plus'
+        qwen_max = 'qwen-max'
+
+    @classmethod
+    async def call(
+        cls,
+        model: str,
+        prompt: Any = None,
+        history: list = None,
+        api_key: str = None,
+        messages: List[Message] = None,
+        plugins: Union[str, Dict[str, Any]] = None,
+        workspace: str = None,
+        **kwargs
+    ) -> Union[GenerationResponse, Generator[GenerationResponse, None, None]]:
+        """Call generation model service.
+
+        Args:
+            model (str): The requested model, such as qwen-turbo
+            prompt (Any): The input prompt.
+            history (list):The user provided history, deprecated
+                examples:
+                    [{'user':'The weather is fine today.',
+                    'bot': 'Suitable for outings'}].
+                Defaults to None.
+            api_key (str, optional): The api api_key, can be None,
+                if None, will get by default rule(TODO: api key doc).
+            messages (list): The generation messages.
+                examples:
+                    [{'role': 'user',
+                      'content': 'The weather is fine today.'},
+                      {'role': 'assistant', 'content': 'Suitable for outings'}]
+            plugins (Any): The plugin config. Can be plugins config str, or dict.
+            **kwargs:
+                stream(bool, `optional`): Enable server-sent events
+                    (ref: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events)  # noqa E501
+                    the result will back partially[qwen-turbo,bailian-v1].
+                temperature(float, `optional`): Used to control the degree
+                    of randomness and diversity. Specifically, the temperature
+                    value controls the degree to which the probability distribution
+                    of each candidate word is smoothed when generating text.
+                    A higher temperature value will reduce the peak value of
+                    the probability, allowing more low-probability words to be
+                    selected, and the generated results will be more diverse;
+                    while a lower temperature value will enhance the peak value
+                    of the probability, making it easier for high-probability
+                    words to be selected, the generated results are more
+                    deterministic, range(0, 2) .[qwen-turbo,qwen-plus].
+                top_p(float, `optional`): A sampling strategy, called nucleus
+                    sampling, where the model considers the results of the
+                    tokens with top_p probability mass. So 0.1 means only
+                    the tokens comprising the top 10% probability mass are
+                    considered[qwen-turbo,bailian-v1].
+                top_k(int, `optional`): The size of the sample candidate set when generated.  # noqa E501
+                    For example, when the value is 50, only the 50 highest-scoring tokens  # noqa E501
+                    in a single generation form a randomly sampled candidate set. # noqa E501
+                    The larger the value, the higher the randomness generated;  # noqa E501
+                    the smaller the value, the higher the certainty generated. # noqa E501
+                    The default value is 0, which means the top_k policy is  # noqa E501
+                    not enabled. At this time, only the top_p policy takes effect. # noqa E501
+                enable_search(bool, `optional`): Whether to enable web search(quark).  # noqa E501
+                    Currently works best only on the first round of conversation.
+                    Default to False, support model: [qwen-turbo].
+                customized_model_id(str, required) The enterprise-specific
+                    large model id, which needs to be generated from the
+                    operation background of the enterprise-specific
+                    large model product, support model: [bailian-v1].
+                result_format(str, `optional`): [message|text] Set result result format. # noqa E501
+                    Default result is text
+                incremental_output(bool, `optional`): Used to control the streaming output mode. # noqa E501
+                    If true, the subsequent output will include the previously input content. # noqa E501
+                    Otherwise, the subsequent output will not include the previously output # noqa E501
+                    content. Default false.
+                stop(list[str] or list[list[int]], `optional`): Used to control the generation to stop  # noqa E501
+                    when encountering setting str or token ids, the result will not include # noqa E501
+                    stop words or tokens.
+                max_tokens(int, `optional`): The maximum token num expected to be output. It should be # noqa E501
+                    noted that the length generated by the model will only be less than max_tokens,  # noqa E501
+                    not necessarily equal to it. If max_tokens is set too large, the service will # noqa E501
+                    directly prompt that the length exceeds the limit. It is generally # noqa E501
+                    not recommended to set this value.
+                repetition_penalty(float, `optional`): Used to control the repeatability when generating models.  # noqa E501
+                    Increasing repetition_penalty can reduce the duplication of model generation.  # noqa E501
+                    1.0 means no punishment.
+            workspace (str): The dashscope workspace id.
+        Raises:
+            InvalidInput: The history and auto_history are mutually exclusive.
+
+        Returns:
+            Union[GenerationResponse,
+                  Generator[GenerationResponse, None, None]]: If
+            stream is True, return Generator, otherwise GenerationResponse.
+        """
+        if (prompt is None or not prompt) and (messages is None
+                                               or not messages):
+            raise InputRequired('prompt or messages is required!')
+        if model is None or not model:
+            raise ModelRequired('Model is required!')
+        task_group, function = _get_task_group_and_task(__name__)
+        if plugins is not None:
+            headers = kwargs.pop('headers', {})
+            if isinstance(plugins, str):
+                headers['X-DashScope-Plugin'] = plugins
+            else:
+                headers['X-DashScope-Plugin'] = json.dumps(plugins)
+            kwargs['headers'] = headers
+        input, parameters = Generation._build_input_parameters(
+            model, prompt, history, messages, **kwargs)
+        response = await super().call(model=model,
+                                      task_group=task_group,
+                                      task=Generation.task,
+                                      function=function,
+                                      api_key=api_key,
+                                      input=input,
+                                      workspace=workspace,
+                                      **parameters)
+        is_stream = kwargs.get('stream', False)
+        if is_stream:
+            return (GenerationResponse.from_api_response(rsp)
+                    async for rsp in response)
+        else:
+            return GenerationResponse.from_api_response(response)

backend_service/venv/lib/python3.13/site-packages/dashscope/aigc/code_generation.py

@@ -0,0 +1,145 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+from typing import Generator, List, Union
+
+from dashscope.api_entities.dashscope_response import (DashScopeAPIResponse,
+                                                       DictMixin, Role)
+from dashscope.client.base_api import BaseApi
+from dashscope.common.constants import MESSAGE, SCENE
+from dashscope.common.error import InputRequired, ModelRequired
+from dashscope.common.utils import _get_task_group_and_task
+
+
+class MessageParam(DictMixin):
+    role: str
+
+    def __init__(self, role: str, **kwargs):
+        super().__init__(role=role, **kwargs)
+
+
+class UserRoleMessageParam(MessageParam):
+    content: str
+
+    def __init__(self, content: str, **kwargs):
+        super().__init__(role=Role.USER, content=content, **kwargs)
+
+
+class AttachmentRoleMessageParam(MessageParam):
+    meta: dict
+
+    def __init__(self, meta: dict, **kwargs):
+        super().__init__(role=Role.ATTACHMENT, meta=meta, **kwargs)
+
+
+class OtherRoleContentMessageParam(MessageParam):
+    content: str
+
+    def __init__(self, role: str, content: str, **kwargs):
+        super().__init__(role=role, content=content, **kwargs)
+
+
+class OtherRoleMetaMessageParam(MessageParam):
+    meta: dict
+
+    def __init__(self, role: str, meta: dict, **kwargs):
+        super().__init__(role=role, meta=meta, **kwargs)
+
+
+class CodeGeneration(BaseApi):
+    function = 'generation'
+    """API for AI-Generated Content(AIGC) models.
+
+    """
+    class Models:
+        tongyi_lingma_v1 = 'tongyi-lingma-v1'
+
+    class Scenes:
+        custom = 'custom'
+        nl2code = 'nl2code'
+        code2comment = 'code2comment'
+        code2explain = 'code2explain'
+        commit2msg = 'commit2msg'
+        unit_test = 'unittest'
+        code_qa = 'codeqa'
+        nl2sql = 'nl2sql'
+
+    @classmethod
+    def call(
+        cls,
+        model: str,
+        scene: str = None,
+        api_key: str = None,
+        message: List[MessageParam] = None,
+        workspace: str = None,
+        **kwargs
+    ) -> Union[DashScopeAPIResponse, Generator[DashScopeAPIResponse, None,
+                                               None]]:
+        """Call generation model service.
+
+        Args:
+            model (str): The requested model, such as tongyi-lingma-v1
+            scene (str): Scene type, single choice, such as custom
+                examples:
+                    custom：User defined prompt
+                    nl2code：Natural language generated code
+                    code2comment：annotation
+                    code2explain：explain
+                    commit2msg：Automatically generate commit
+                    uinttest：Generating Unit Tests
+                    codeqa：Code Q&A
+                    nl2sql：Generate SQL code using natural language
+            api_key (str, optional): The api api_key, can be None,
+                if None, will get by default rule(TODO: api key doc).
+            message (list): The generation messages.
+                scene == custom, examples:
+                    [{"role": "user", "content": "根据下面的功能描述生成一个python函数。代码的功能是计算给定路径下所有文件的总大小。"}]  # noqa E501
+                scene == nl2code, examples:
+                    [{"role": "user", "content": "计算给定路径下所有文件的总大小"}, {"role": "attachment", "meta": {"language": "java"}}]  # noqa E501
+                scene == code2comment, examples:
+                    [{"role": "user", "content": "1. 生成中文注释\n2. 仅生成代码部分，不需要额外解释函数功能\n"}, {"role": "attachment", "meta": {"code": "\t\t@Override\n\t\tpublic  CancelExportTaskResponse  cancelExportTask(\n\t\t\t\tCancelExportTask  cancelExportTask)  {\n\t\t\tAmazonEC2SkeletonInterface  ec2Service  =  ServiceProvider.getInstance().getServiceImpl(AmazonEC2SkeletonInterface.class);\n\t\t\treturn  ec2Service.cancelExportTask(cancelExportTask);\n\t\t}", "language": "java"}}]  # noqa E501
+                scene == code2explain, examples:
+                    [{"role": "user", "content": "要求不低于200字"}, {"role": "attachment", "meta": {"code": "@Override\n                                public  int  getHeaderCacheSize()\n                                {\n                                        return  0;\n                                }\n\n", "language": "java"}}]  # noqa E501
+                scene == commit2msg, examples:
+                    [{"role": "attachment", "meta": {"diff_list": [{"diff": "--- src/com/siondream/core/PlatformResolver.java\n+++ src/com/siondream/core/PlatformResolver.java\n@@ -1,11 +1,8 @@\npackage com.siondream.core;\n-\n-import com.badlogic.gdx.files.FileHandle;\n\npublic interface PlatformResolver {\npublic void openURL(String url);\npublic void rateApp();\npublic void sendFeedback();\n-\tpublic FileHandle[] listFolder(String path);\n}\n", "old_file_path": "src/com/siondream/core/PlatformResolver.java", "new_file_path": "src/com/siondream/core/PlatformResolver.java"}]}}]  # noqa E501
+                scene == unittest, examples:
+                    [{"role": "attachment", "meta": {"code": "public static <T> TimestampMap<T> parseTimestampMap(Class<T> typeClass, String input, DateTimeZone timeZone) throws IllegalArgumentException {\n        if (typeClass == null) {\n            throw new IllegalArgumentException(\"typeClass required\");\n        }\n\n        if (input == null) {\n            return null;\n        }\n\n        TimestampMap result;\n\n        typeClass = AttributeUtils.getStandardizedType(typeClass);\n        if (typeClass.equals(String.class)) {\n            result = new TimestampStringMap();\n        } else if (typeClass.equals(Byte.class)) {\n            result = new TimestampByteMap();\n        } else if (typeClass.equals(Short.class)) {\n            result = new TimestampShortMap();\n        } else if (typeClass.equals(Integer.class)) {\n            result = new TimestampIntegerMap();\n        } else if (typeClass.equals(Long.class)) {\n            result = new TimestampLongMap();\n        } else if (typeClass.equals(Float.class)) {\n            result = new TimestampFloatMap();\n        } else if (typeClass.equals(Double.class)) {\n            result = new TimestampDoubleMap();\n        } else if (typeClass.equals(Boolean.class)) {\n            result = new TimestampBooleanMap();\n        } else if (typeClass.equals(Character.class)) {\n            result = new TimestampCharMap();\n        } else {\n            throw new IllegalArgumentException(\"Unsupported type \" + typeClass.getClass().getCanonicalName());\n        }\n\n        if (input.equalsIgnoreCase(EMPTY_VALUE)) {\n            return result;\n        }\n\n        StringReader reader = new StringReader(input + ' ');// Add 1 space so\n                                                            // reader.skip\n                                                            // function always\n                                                            // works when\n                                                            // necessary (end of\n                                                            // string not\n                                                            // reached).\n\n        try {\n            int r;\n            char c;\n            while ((r = reader.read()) != -1) {\n                c = (char) r;\n                switch (c) {\n                    case LEFT_BOUND_SQUARE_BRACKET:\n                    case LEFT_BOUND_BRACKET:\n                        parseTimestampAndValue(typeClass, reader, result, timeZone);\n                        break;\n                    default:\n                        // Ignore other chars outside of bounds\n                }\n            }\n        } catch (IOException ex) {\n            throw new RuntimeException(\"Unexpected expection while parsing timestamps\", ex);\n        }\n\n        return result;\n    }", "language": "java"}}]  # noqa E501
+                scene == codeqa, examples:
+                    [{"role": "user", "content": "I'm writing a small web server in Python, using BaseHTTPServer and a custom subclass of BaseHTTPServer.BaseHTTPRequestHandler. Is it possible to make this listen on more than one port?\nWhat I'm doing now:\nclass MyRequestHandler(BaseHTTPServer.BaseHTTPRequestHandler):\n  def doGET\n  [...]\n\nclass ThreadingHTTPServer(ThreadingMixIn, HTTPServer): \n    pass\n\nserver = ThreadingHTTPServer(('localhost', 80), MyRequestHandler)\nserver.serve_forever()"}]  # noqa E501
+                scene == nl2sql, examples:
+                    [{"role": "user", "content": "小明的总分数是多少"}, {"role": "attachment", "meta": {"synonym_infos": {"学生姓名": "姓名|名字|名称", "学生分数": "分数|得分"}, "recall_infos": [{"content": "student_score.id='小明'", "score": "0.83"}], "schema_infos": [{"table_id": "student_score", "table_desc": "学生分数表", "columns": [{"col_name": "id", "col_caption": "学生id", "col_desc": "例值为:1,2,3", "col_type": "string"}, {"col_name": "name", "col_caption": "学生姓名", "col_desc": "例值为:张三,李四,小明", "col_type": "string"}, {"col_name": "score", "col_caption": "学生分数", "col_desc": "例值为:98,100,66", "col_type": "string"}]}]}}]  # noqa E501
+            workspace (str): The dashscope workspace id.
+            **kwargs:
+                n(int, `optional`): The number of output results, currently only supports 1, with a default value of 1  # noqa E501
+
+        Returns:
+            Union[DashScopeAPIResponse,
+                  Generator[DashScopeAPIResponse, None, None]]: If
+            stream is True, return Generator, otherwise DashScopeAPIResponse.
+        """
+        if (scene is None or not scene) or (message is None or not message):
+            raise InputRequired('scene and message is required!')
+        if model is None or not model:
+            raise ModelRequired('Model is required!')
+        task_group, task = _get_task_group_and_task(__name__)
+        input, parameters = cls._build_input_parameters(
+            model, scene, message, **kwargs)
+        response = super().call(model=model,
+                                task_group=task_group,
+                                task=task,
+                                function=CodeGeneration.function,
+                                api_key=api_key,
+                                input=input,
+                                workspace=workspace,
+                                **parameters)
+
+        is_stream = kwargs.get('stream', False)
+        if is_stream:
+            return (rsp for rsp in response)
+        else:
+            return response
+
+    @classmethod
+    def _build_input_parameters(cls, model, scene, message, **kwargs):
+        parameters = {'n': kwargs.pop('n', 1)}
+        input = {SCENE: scene, MESSAGE: message}
+        return input, {**parameters, **kwargs}

backend_service/venv/lib/python3.13/site-packages/dashscope/aigc/conversation.py

@@ -0,0 +1,314 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import json
+from copy import deepcopy
+from http import HTTPStatus
+from typing import Any, Dict, Generator, List, Union
+
+from dashscope.api_entities.dashscope_response import (ConversationResponse,
+                                                       Message, Role)
+from dashscope.client.base_api import BaseApi
+from dashscope.common.constants import DEPRECATED_MESSAGE, HISTORY, PROMPT
+from dashscope.common.error import InputRequired, InvalidInput, ModelRequired
+from dashscope.common.logging import logger
+from dashscope.common.utils import _get_task_group_and_task
+
+
+class HistoryItem(dict):
+    """A conversation history item.
+
+    """
+    def __init__(self, role: str, text: str = None, **kwargs):
+        """Init a history item.
+
+        Args:
+            role (str): The role name.
+            text (str): The text history. Default ot None.
+            kwargs: The key/value pair of history content.
+
+        Raises:
+            InvalidInput: The key and value must pair.
+        """
+        logger.warning(DEPRECATED_MESSAGE)
+        self.role = role
+        dict.__init__(self, {role: []})
+        if text is not None:
+            self[self.role].append({'text': text})
+        for k, v in kwargs.items():
+            self[self.role].append({k: v})
+
+    def add(self, key: str, content: Any):
+        """Add a key/value to history.
+
+        Args:
+            key (str): The key of the content.
+            value (Any): The history content.
+        """
+        self[self.role].append({key: content})
+
+
+class History(list):
+    """Manage the conversation history.
+
+    """
+    def __init__(self, items: List[HistoryItem] = None):
+        """Init a history with list of HistoryItems.
+
+        Args:
+            items (List[HistoryItem], optional): The history items.
+                Defaults to None.
+        """
+        if items is not None:
+            logger.warning(DEPRECATED_MESSAGE)
+            list.__init__(items)
+        else:
+            list.__init__([])
+
+
+def _history_to_qwen_format(history: History, n_history: int):
+    """Convert history to simple format.
+       [{"user":"您好", "bot":"我是你的助手，很高兴为您服务"},
+        {"user":"user input", "bot":"bot output"}]
+    """
+    simple_history = []
+    user = None
+    bot = None
+
+    if n_history != -1 and len(history) >= 2 * n_history:
+        history = history[len(history) - 2 * n_history:]
+
+    for item in history:
+        if 'user' in item:
+            user = item['user'][0]['text']
+        if 'bot' in item:
+            bot = item['bot'][0]['text']
+        if user is not None and bot is not None:
+            simple_history.append({'user': user, 'bot': bot})
+            user = None
+            bot = None
+    return simple_history
+
+
+class Conversation(BaseApi):
+    """Conversational robot interface.
+    """
+    task = 'generation'
+
+    class Models:
+        """@deprecated, use qwen_turbo instead"""
+        qwen_v1 = 'qwen-v1'
+        """@deprecated, use qwen_plus instead"""
+        qwen_plus_v1 = 'qwen-plus-v1'
+
+        qwen_turbo = 'qwen-turbo'
+        qwen_plus = 'qwen-plus'
+        qwen_max = 'qwen-max'
+
+    def __init__(self, history: History = None) -> None:
+        """Init a chat.
+
+        Args:
+            history (dict): The conversation initialization settings.
+               will be recorded in the system history list.
+                Defaults to None.
+        """
+        super().__init__()
+        if history is None:
+            self.history = History()
+        else:
+            logger.warning(DEPRECATED_MESSAGE)
+            self.history = history
+
+    def call(
+        self,
+        model: str,
+        prompt: Any = None,
+        history: History = None,
+        auto_history: bool = False,
+        n_history: int = -1,
+        api_key: str = None,
+        messages: List[Message] = None,
+        plugins: Union[str, Dict[str, Any]] = None,
+        workspace: str = None,
+        **kwargs
+    ) -> Union[ConversationResponse, Generator[ConversationResponse, None,
+                                               None]]:
+        """Call conversational robot generator a response.
+
+        Args:
+            model (str): The request model.
+            prompt (Any): The input prompt.
+            history(History): The user provided history.
+                Only works for this call, will not be recorded in the
+                system history list. The ``history`` and ``auto_history``
+                are mutually exclusive. Default to None.
+            auto_history (bool): Call with the automatically maintenance
+                conversation history list. The ``history`` and ``auto_history``
+                are mutually exclusive.
+            n_history (int): Number of latest history in conversation,
+                -1 all history. Default to -1
+            api_key (str, optional): The api api_key, if not present,
+                will get by default rule(TODO: api key doc). Defaults to None.
+            messages (list): The generation messages.
+                examples:
+                    [{'role': 'user',
+                      'content': 'The weather is fine today.'},
+                      {'role': 'assistant', 'content': 'Suitable for outings'}]
+            plugins (Any): The plugin config, Can be plugins config str, or dict.
+            **kwargs(qwen-turbo, qwen-plus):
+                stream(bool, `optional`): Enable server-sent events
+                    (ref: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events)  # noqa E501
+                    the result will back partially.
+                temperature(float, `optional`): Used to control the degree
+                    of randomness and diversity. Specifically, the temperature
+                    value controls the degree to which the probability distribution
+                    of each candidate word is smoothed when generating text.
+                    A higher temperature value will reduce the peak value of
+                    the probability, allowing more low-probability words to be
+                    selected, and the generated results will be more diverse;
+                    while a lower temperature value will enhance the peak value
+                    of the probability, making it easier for high-probability
+                    words to be selected, the generated results are more
+                    deterministic,range(0, 2) .[qwen-turbo,qwen-plus].
+                top_p(float, `optional`): A sampling strategy, called nucleus
+                    sampling, where the model considers the results of the
+                    tokens with top_p probability mass. So 0.1 means only
+                    the tokens comprising the top 10% probability mass are
+                    considered.
+                top_k(int, `optional`): The size of the sample candidate set when generated.  # noqa E501
+                    For example, when the value is 50, only the 50 highest-scoring tokens  # noqa E501
+                    in a single generation form a randomly sampled candidate set. # noqa E501
+                    The larger the value, the higher the randomness generated;  # noqa E501
+                    the smaller the value, the higher the certainty generated. # noqa E501
+                    The default value is 0, which means the top_k policy is  # noqa E501
+                    not enabled. At this time, only the top_p policy takes effect. # noqa E501
+                enable_search(bool, `optional`): Whether to enable web search(quark).  # noqa E501
+                    Currently works best only on the first round of conversation.
+                    Default to False, support model: [qwen-turbo].
+                customized_model_id(str, required) The enterprise-specific
+                    large model id, which needs to be generated from the
+                    operation background of the enterprise-specific
+                    large model product, support model: [bailian-v1].
+                result_format(str, `optional`): [message|text] Set result result format. # noqa E501
+                    Default result is text
+                incremental_output(bool, `optional`): Used to control the streaming output mode. # noqa E501
+                    If true, the subsequent output will include the previously input content. # noqa E501
+                    Otherwise, the subsequent output will not include the previously output # noqa E501
+                    content. Default false.
+                stop(list[str] or list[list[int]], `optional`): Used to control the generation to stop  # noqa E501
+                    when encountering setting str or token ids, the result will not include # noqa E501
+                    stop words or tokens.
+                max_tokens(int, `optional`): The maximum token num expected to be output. It should be # noqa E501
+                    noted that the length generated by the model will only be less than max_tokens,  # noqa E501
+                    not necessarily equal to it. If max_tokens is set too large, the service will # noqa E501
+                    directly prompt that the length exceeds the limit. It is generally # noqa E501
+                    not recommended to set this value.
+                repetition_penalty(float, `optional`): Used to control the repeatability when generating models.  # noqa E501
+                    Increasing repetition_penalty can reduce the duplication of model generation.  # noqa E501
+                    1.0 means no punishment.
+            workspace (str): The dashscope workspace id.
+        Raises:
+            InputRequired: The prompt cannot be empty.
+            InvalidInput: The history and auto_history are mutually exclusive.
+
+        Returns:
+            Union[ConversationResponse,
+                  Generator[ConversationResponse, None, None]]: If
+            stream is True, return Generator, otherwise ConversationResponse.
+
+        """
+        if ((prompt is None or not prompt)
+                and ((messages is None or not messages))):
+            raise InputRequired('prompt or messages is required!')
+        if model is None or not model:
+            raise ModelRequired('Model is required!')
+        task_group, _ = _get_task_group_and_task(__name__)
+        if plugins is not None:
+            headers = kwargs.pop('headers', {})
+            if isinstance(plugins, str):
+                headers['X-DashScope-Plugin'] = plugins
+            else:
+                headers['X-DashScope-Plugin'] = json.dumps(plugins)
+            kwargs['headers'] = headers
+        input, parameters = self._build_input_parameters(
+            model, prompt, history, auto_history, n_history, messages,
+            **kwargs)
+        response = super().call(model=model,
+                                task_group=task_group,
+                                task='text-generation',
+                                function='generation',
+                                api_key=api_key,
+                                input=input,
+                                workspace=workspace,
+                                **parameters)
+        is_stream = kwargs.get('stream', False)
+        return self._handle_response(prompt, response, is_stream)
+
+    def _handle_stream_response(self, prompt, responses):
+        for rsp in responses:
+            rsp = ConversationResponse.from_api_response(rsp)
+            yield rsp
+        if rsp.status_code == HTTPStatus.OK and rsp.output.choices is None:
+            user_item = HistoryItem('user', text=prompt)
+            bot_history_item = HistoryItem('bot', text=rsp.output.text)
+            self.history.append(user_item)
+            self.history.append(bot_history_item)
+
+    def _handle_response(self, prompt, response, is_stream):
+        if is_stream:
+            return (rsp
+                    for rsp in self._handle_stream_response(prompt, response))
+        else:
+            response = ConversationResponse.from_api_response(response)
+            if (response.status_code == HTTPStatus.OK
+                    and response.output.choices is None):
+                user_item = HistoryItem('user', text=prompt)
+                bot_history_item = HistoryItem('bot',
+                                               text=response.output['text'])
+                self.history.append(user_item)
+                self.history.append(bot_history_item)
+            return response
+
+    def _build_input_parameters(self, model, prompt, history, auto_history,
+                                n_history, messages, **kwargs):
+        if model == Conversation.Models.qwen_v1:
+            logger.warning(
+                'Model %s is deprecated, use %s instead!' %
+                (Conversation.Models.qwen_v1, Conversation.Models.qwen_turbo))
+        if model == Conversation.Models.qwen_plus_v1:
+            logger.warning('Model %s is deprecated, use %s instead!' %
+                           (Conversation.Models.qwen_plus_v1,
+                            Conversation.Models.qwen_plus))
+        parameters = {}
+        if history is not None and auto_history:
+            raise InvalidInput('auto_history is True, history must None')
+        if history is not None:  # use user provided history or system.
+            logger.warning(DEPRECATED_MESSAGE)
+            input = {
+                PROMPT:
+                prompt,
+                HISTORY:
+                _history_to_qwen_format(history, n_history) if history else [],
+            }
+        elif auto_history:
+            logger.warning(DEPRECATED_MESSAGE)
+            input = {
+                PROMPT: prompt,
+                HISTORY: _history_to_qwen_format(self.history, n_history)
+            }
+        elif messages:
+            msgs = deepcopy(messages)
+            if prompt is not None and prompt:
+                msgs.append({'role': Role.USER, 'content': prompt})
+            input = {'messages': msgs}
+        else:
+            input = {
+                PROMPT: prompt,
+            }
+        # parameters
+        if model.startswith('qwen'):
+            enable_search = kwargs.pop('enable_search', False)
+            if enable_search:
+                parameters['enable_search'] = enable_search
+
+        return input, {**parameters, **kwargs}

backend_service/venv/lib/python3.13/site-packages/dashscope/aigc/generation.py

@@ -0,0 +1,407 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import copy
+import json
+from typing import Any, Dict, Generator, List, Union, AsyncGenerator
+
+from dashscope.api_entities.dashscope_response import (GenerationResponse,
+                                                       Message, Role)
+from dashscope.client.base_api import BaseAioApi, BaseApi
+from dashscope.common.constants import (CUSTOMIZED_MODEL_ID,
+                                        DEPRECATED_MESSAGE, HISTORY, MESSAGES,
+                                        PROMPT)
+from dashscope.common.error import InputRequired, ModelRequired
+from dashscope.common.logging import logger
+from dashscope.common.utils import _get_task_group_and_task
+from dashscope.utils.param_utils import ParamUtil
+from dashscope.utils.message_utils import merge_single_response
+
+
+class Generation(BaseApi):
+    task = 'text-generation'
+    """API for AI-Generated Content(AIGC) models.
+
+    """
+    class Models:
+        """@deprecated, use qwen_turbo instead"""
+        qwen_v1 = 'qwen-v1'
+        """@deprecated, use qwen_plus instead"""
+        qwen_plus_v1 = 'qwen-plus-v1'
+
+        bailian_v1 = 'bailian-v1'
+        dolly_12b_v2 = 'dolly-12b-v2'
+        qwen_turbo = 'qwen-turbo'
+        qwen_plus = 'qwen-plus'
+        qwen_max = 'qwen-max'
+
+    @classmethod
+    def call(
+        cls,
+        model: str,
+        prompt: Any = None,
+        history: list = None,
+        api_key: str = None,
+        messages: List[Message] = None,
+        plugins: Union[str, Dict[str, Any]] = None,
+        workspace: str = None,
+        **kwargs
+    ) -> Union[GenerationResponse, Generator[GenerationResponse, None, None]]:
+        """Call generation model service.
+
+        Args:
+            model (str): The requested model, such as qwen-turbo
+            prompt (Any): The input prompt.
+            history (list):The user provided history, deprecated
+                examples:
+                    [{'user':'The weather is fine today.',
+                    'bot': 'Suitable for outings'}].
+                Defaults to None.
+            api_key (str, optional): The api api_key, can be None,
+                if None, will get by default rule(TODO: api key doc).
+            messages (list): The generation messages.
+                examples:
+                    [{'role': 'user',
+                      'content': 'The weather is fine today.'},
+                      {'role': 'assistant', 'content': 'Suitable for outings'}]
+            plugins (Any): The plugin config. Can be plugins config str, or dict.
+            **kwargs:
+                stream(bool, `optional`): Enable server-sent events
+                    (ref: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events)  # noqa E501
+                    the result will back partially[qwen-turbo,bailian-v1].
+                temperature(float, `optional`): Used to control the degree
+                    of randomness and diversity. Specifically, the temperature
+                    value controls the degree to which the probability distribution
+                    of each candidate word is smoothed when generating text.
+                    A higher temperature value will reduce the peak value of
+                    the probability, allowing more low-probability words to be
+                    selected, and the generated results will be more diverse;
+                    while a lower temperature value will enhance the peak value
+                    of the probability, making it easier for high-probability
+                    words to be selected, the generated results are more
+                    deterministic, range(0, 2) .[qwen-turbo,qwen-plus].
+                top_p(float, `optional`): A sampling strategy, called nucleus
+                    sampling, where the model considers the results of the
+                    tokens with top_p probability mass. So 0.1 means only
+                    the tokens comprising the top 10% probability mass are
+                    considered[qwen-turbo,bailian-v1].
+                top_k(int, `optional`): The size of the sample candidate set when generated.  # noqa E501
+                    For example, when the value is 50, only the 50 highest-scoring tokens  # noqa E501
+                    in a single generation form a randomly sampled candidate set. # noqa E501
+                    The larger the value, the higher the randomness generated;  # noqa E501
+                    the smaller the value, the higher the certainty generated. # noqa E501
+                    The default value is 0, which means the top_k policy is  # noqa E501
+                    not enabled. At this time, only the top_p policy takes effect. # noqa E501
+                enable_search(bool, `optional`): Whether to enable web search(quark).  # noqa E501
+                    Currently works best only on the first round of conversation.
+                    Default to False, support model: [qwen-turbo].
+                customized_model_id(str, required) The enterprise-specific
+                    large model id, which needs to be generated from the
+                    operation background of the enterprise-specific
+                    large model product, support model: [bailian-v1].
+                result_format(str, `optional`): [message|text] Set result result format. # noqa E501
+                    Default result is text
+                incremental_output(bool, `optional`): Used to control the streaming output mode. # noqa E501
+                    If true, the subsequent output will include the previously input content. # noqa E501
+                    Otherwise, the subsequent output will not include the previously output # noqa E501
+                    content. Default false.
+                stop(list[str] or list[list[int]], `optional`): Used to control the generation to stop  # noqa E501
+                    when encountering setting str or token ids, the result will not include # noqa E501
+                    stop words or tokens.
+                max_tokens(int, `optional`): The maximum token num expected to be output. It should be # noqa E501
+                    noted that the length generated by the model will only be less than max_tokens,  # noqa E501
+                    not necessarily equal to it. If max_tokens is set too large, the service will # noqa E501
+                    directly prompt that the length exceeds the limit. It is generally # noqa E501
+                    not recommended to set this value.
+                repetition_penalty(float, `optional`): Used to control the repeatability when generating models.  # noqa E501
+                    Increasing repetition_penalty can reduce the duplication of model generation.  # noqa E501
+                    1.0 means no punishment.
+            workspace (str): The dashscope workspace id.
+        Raises:
+            InvalidInput: The history and auto_history are mutually exclusive.
+
+        Returns:
+            Union[GenerationResponse,
+                  Generator[GenerationResponse, None, None]]: If
+            stream is True, return Generator, otherwise GenerationResponse.
+        """
+        if (prompt is None or not prompt) and (messages is None
+                                               or not messages):
+            raise InputRequired('prompt or messages is required!')
+        if model is None or not model:
+            raise ModelRequired('Model is required!')
+        task_group, function = _get_task_group_and_task(__name__)
+        if plugins is not None:
+            headers = kwargs.pop('headers', {})
+            if isinstance(plugins, str):
+                headers['X-DashScope-Plugin'] = plugins
+            else:
+                headers['X-DashScope-Plugin'] = json.dumps(plugins)
+            kwargs['headers'] = headers
+        input, parameters = cls._build_input_parameters(
+            model, prompt, history, messages, **kwargs)
+
+        is_stream = parameters.get('stream', False)
+        # Check if we need to merge incremental output
+        is_incremental_output = kwargs.get('incremental_output', None)
+        to_merge_incremental_output = False
+        if (ParamUtil.should_modify_incremental_output(model) and
+                is_stream and is_incremental_output is False):
+            to_merge_incremental_output = True
+            parameters['incremental_output'] = True
+
+        # Pass incremental_to_full flag via headers user-agent
+        if 'headers' not in parameters:
+            parameters['headers'] = {}
+        flag = '1' if to_merge_incremental_output else '0'
+        parameters['headers']['user-agent'] = f'incremental_to_full/{flag}'
+
+        response = super().call(model=model,
+                                task_group=task_group,
+                                task=Generation.task,
+                                function=function,
+                                api_key=api_key,
+                                input=input,
+                                workspace=workspace,
+                                **parameters)
+        if is_stream:
+            if to_merge_incremental_output:
+                # Extract n parameter for merge logic
+                n = parameters.get('n', 1)
+                return cls._merge_generation_response(response, n)
+            else:
+                return (GenerationResponse.from_api_response(rsp)
+                        for rsp in response)
+        else:
+            return GenerationResponse.from_api_response(response)
+
+    @classmethod
+    def _build_input_parameters(cls, model, prompt, history, messages,
+                                **kwargs):
+        if model == Generation.Models.qwen_v1:
+            logger.warning(
+                'Model %s is deprecated, use %s instead!' %
+                (Generation.Models.qwen_v1, Generation.Models.qwen_turbo))
+        if model == Generation.Models.qwen_plus_v1:
+            logger.warning(
+                'Model %s is deprecated, use %s instead!' %
+                (Generation.Models.qwen_plus_v1, Generation.Models.qwen_plus))
+        parameters = {}
+        input = {}
+        if history is not None:
+            logger.warning(DEPRECATED_MESSAGE)
+            input[HISTORY] = history
+            if prompt is not None and prompt:
+                input[PROMPT] = prompt
+        elif messages is not None:
+            msgs = copy.deepcopy(messages)
+            if prompt is not None and prompt:
+                msgs.append({'role': Role.USER, 'content': prompt})
+            input = {MESSAGES: msgs}
+        else:
+            input[PROMPT] = prompt
+
+        if model.startswith('qwen'):
+            enable_search = kwargs.pop('enable_search', False)
+            if enable_search:
+                parameters['enable_search'] = enable_search
+        elif model.startswith('bailian'):
+            customized_model_id = kwargs.pop('customized_model_id', None)
+            if customized_model_id is None:
+                raise InputRequired('customized_model_id is required for %s' %
+                                    model)
+            input[CUSTOMIZED_MODEL_ID] = customized_model_id
+
+        return input, {**parameters, **kwargs}
+
+    @classmethod
+    def _merge_generation_response(cls, response, n=1) -> Generator[GenerationResponse, None, None]:
+        """Merge incremental response chunks to simulate non-incremental output."""
+        accumulated_data = {}
+        for rsp in response:
+            parsed_response = GenerationResponse.from_api_response(rsp)
+            result = merge_single_response(parsed_response, accumulated_data, n)
+            if result is True:
+                yield parsed_response
+            elif isinstance(result, list):
+                # Multiple responses to yield (for n>1 non-stop cases)
+                for resp in result:
+                    yield resp
+
+
+class AioGeneration(BaseAioApi):
+    task = 'text-generation'
+    """API for AI-Generated Content(AIGC) models.
+
+    """
+    class Models:
+        """@deprecated, use qwen_turbo instead"""
+        qwen_v1 = 'qwen-v1'
+        """@deprecated, use qwen_plus instead"""
+        qwen_plus_v1 = 'qwen-plus-v1'
+
+        bailian_v1 = 'bailian-v1'
+        dolly_12b_v2 = 'dolly-12b-v2'
+        qwen_turbo = 'qwen-turbo'
+        qwen_plus = 'qwen-plus'
+        qwen_max = 'qwen-max'
+
+    @classmethod
+    async def call(
+        cls,
+        model: str,
+        prompt: Any = None,
+        history: list = None,
+        api_key: str = None,
+        messages: List[Message] = None,
+        plugins: Union[str, Dict[str, Any]] = None,
+        workspace: str = None,
+        **kwargs
+    ) -> Union[GenerationResponse, AsyncGenerator[GenerationResponse, None]]:
+        """Call generation model service.
+
+        Args:
+            model (str): The requested model, such as qwen-turbo
+            prompt (Any): The input prompt.
+            history (list):The user provided history, deprecated
+                examples:
+                    [{'user':'The weather is fine today.',
+                    'bot': 'Suitable for outings'}].
+                Defaults to None.
+            api_key (str, optional): The api api_key, can be None,
+                if None, will get by default rule(TODO: api key doc).
+            messages (list): The generation messages.
+                examples:
+                    [{'role': 'user',
+                      'content': 'The weather is fine today.'},
+                      {'role': 'assistant', 'content': 'Suitable for outings'}]
+            plugins (Any): The plugin config. Can be plugins config str, or dict.
+            **kwargs:
+                stream(bool, `optional`): Enable server-sent events
+                    (ref: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events)  # noqa E501
+                    the result will back partially[qwen-turbo,bailian-v1].
+                temperature(float, `optional`): Used to control the degree
+                    of randomness and diversity. Specifically, the temperature
+                    value controls the degree to which the probability distribution
+                    of each candidate word is smoothed when generating text.
+                    A higher temperature value will reduce the peak value of
+                    the probability, allowing more low-probability words to be
+                    selected, and the generated results will be more diverse;
+                    while a lower temperature value will enhance the peak value
+                    of the probability, making it easier for high-probability
+                    words to be selected, the generated results are more
+                    deterministic, range(0, 2) .[qwen-turbo,qwen-plus].
+                top_p(float, `optional`): A sampling strategy, called nucleus
+                    sampling, where the model considers the results of the
+                    tokens with top_p probability mass. So 0.1 means only
+                    the tokens comprising the top 10% probability mass are
+                    considered[qwen-turbo,bailian-v1].
+                top_k(int, `optional`): The size of the sample candidate set when generated.  # noqa E501
+                    For example, when the value is 50, only the 50 highest-scoring tokens  # noqa E501
+                    in a single generation form a randomly sampled candidate set. # noqa E501
+                    The larger the value, the higher the randomness generated;  # noqa E501
+                    the smaller the value, the higher the certainty generated. # noqa E501
+                    The default value is 0, which means the top_k policy is  # noqa E501
+                    not enabled. At this time, only the top_p policy takes effect. # noqa E501
+                enable_search(bool, `optional`): Whether to enable web search(quark).  # noqa E501
+                    Currently works best only on the first round of conversation.
+                    Default to False, support model: [qwen-turbo].
+                customized_model_id(str, required) The enterprise-specific
+                    large model id, which needs to be generated from the
+                    operation background of the enterprise-specific
+                    large model product, support model: [bailian-v1].
+                result_format(str, `optional`): [message|text] Set result result format. # noqa E501
+                    Default result is text
+                incremental_output(bool, `optional`): Used to control the streaming output mode. # noqa E501
+                    If true, the subsequent output will include the previously input content. # noqa E501
+                    Otherwise, the subsequent output will not include the previously output # noqa E501
+                    content. Default false.
+                stop(list[str] or list[list[int]], `optional`): Used to control the generation to stop  # noqa E501
+                    when encountering setting str or token ids, the result will not include # noqa E501
+                    stop words or tokens.
+                max_tokens(int, `optional`): The maximum token num expected to be output. It should be # noqa E501
+                    noted that the length generated by the model will only be less than max_tokens,  # noqa E501
+                    not necessarily equal to it. If max_tokens is set too large, the service will # noqa E501
+                    directly prompt that the length exceeds the limit. It is generally # noqa E501
+                    not recommended to set this value.
+                repetition_penalty(float, `optional`): Used to control the repeatability when generating models.  # noqa E501
+                    Increasing repetition_penalty can reduce the duplication of model generation.  # noqa E501
+                    1.0 means no punishment.
+            workspace (str): The dashscope workspace id.
+        Raises:
+            InvalidInput: The history and auto_history are mutually exclusive.
+
+        Returns:
+            Union[GenerationResponse,
+                  AsyncGenerator[GenerationResponse, None]]: If
+            stream is True, return AsyncGenerator, otherwise GenerationResponse.
+        """
+        if (prompt is None or not prompt) and (messages is None
+                                               or not messages):
+            raise InputRequired('prompt or messages is required!')
+        if model is None or not model:
+            raise ModelRequired('Model is required!')
+        task_group, function = _get_task_group_and_task(__name__)
+        if plugins is not None:
+            headers = kwargs.pop('headers', {})
+            if isinstance(plugins, str):
+                headers['X-DashScope-Plugin'] = plugins
+            else:
+                headers['X-DashScope-Plugin'] = json.dumps(plugins)
+            kwargs['headers'] = headers
+        input, parameters = Generation._build_input_parameters(
+            model, prompt, history, messages, **kwargs)
+
+        is_stream = parameters.get('stream', False)
+        # Check if we need to merge incremental output
+        is_incremental_output = kwargs.get('incremental_output', None)
+        to_merge_incremental_output = False
+        if (ParamUtil.should_modify_incremental_output(model) and
+                is_stream and is_incremental_output is False):
+            to_merge_incremental_output = True
+            parameters['incremental_output'] = True
+
+        # Pass incremental_to_full flag via headers user-agent
+        if 'headers' not in parameters:
+            parameters['headers'] = {}
+        flag = '1' if to_merge_incremental_output else '0'
+        parameters['headers']['user-agent'] = f'incremental_to_full/{flag}'
+
+        response = await super().call(model=model,
+                                      task_group=task_group,
+                                      task=Generation.task,
+                                      function=function,
+                                      api_key=api_key,
+                                      input=input,
+                                      workspace=workspace,
+                                      **parameters)
+        if is_stream:
+            if to_merge_incremental_output:
+                # Extract n parameter for merge logic
+                n = parameters.get('n', 1)
+                return cls._merge_generation_response(response, n)
+            else:
+                return cls._stream_responses(response)
+        else:
+            return GenerationResponse.from_api_response(response)
+
+    @classmethod
+    async def _stream_responses(cls, response) -> AsyncGenerator[GenerationResponse, None]:
+        """Convert async response stream to GenerationResponse stream."""
+        # Type hint: when stream=True, response is actually an AsyncIterable
+        async for rsp in response:  # type: ignore
+            yield GenerationResponse.from_api_response(rsp)
+
+    @classmethod
+    async def _merge_generation_response(cls, response, n=1) -> AsyncGenerator[GenerationResponse, None]:
+        """Async version of merge incremental response chunks."""
+        accumulated_data = {}
+
+        async for rsp in response:  # type: ignore
+            parsed_response = GenerationResponse.from_api_response(rsp)
+            result = merge_single_response(parsed_response, accumulated_data, n)
+            if result is True:
+                yield parsed_response
+            elif isinstance(result, list):
+                # Multiple responses to yield (for n>1 non-stop cases)
+                for resp in result:
+                    yield resp

backend_service/venv/lib/python3.13/site-packages/dashscope/aigc/image_synthesis.py

@@ -0,0 +1,630 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+from typing import Any, Dict, List, Union
+
+from dashscope.api_entities.dashscope_response import (DashScopeAPIResponse,
+                                                       ImageSynthesisResponse)
+from dashscope.client.base_api import BaseAsyncApi, BaseApi, BaseAsyncAioApi, BaseAioApi
+from dashscope.common.constants import IMAGES, NEGATIVE_PROMPT, PROMPT
+from dashscope.common.error import InputRequired
+from dashscope.common.utils import _get_task_group_and_task
+from dashscope.utils.oss_utils import check_and_upload_local
+
+
+class ImageSynthesis(BaseAsyncApi):
+    task = 'text2image'
+    """API for image synthesis.
+    """
+    class Models:
+        wanx_v1 = 'wanx-v1'
+        wanx_sketch_to_image_v1 = 'wanx-sketch-to-image-v1'
+
+        wanx_2_1_imageedit = 'wanx2.1-imageedit'
+
+    @classmethod
+    def call(cls,
+             model: str,
+             prompt: Any,
+             negative_prompt: Any = None,
+             images: List[str] = None,
+             api_key: str = None,
+             sketch_image_url: str = None,
+             ref_img: str = None,
+             workspace: str = None,
+             extra_input: Dict = None,
+             task: str = None,
+             function: str = None,
+             mask_image_url: str = None,
+             base_image_url: str = None,
+             **kwargs) -> ImageSynthesisResponse:
+        """Call image(s) synthesis service and get result.
+
+        Args:
+            model (str): The model, reference ``Models``.
+            prompt (Any): The prompt for image(s) synthesis.
+            negative_prompt (Any): The negative_prompt. Defaults to None.
+            images (List[str]): The input list of images url,
+                currently not supported.
+            api_key (str, optional): The api api_key. Defaults to None.
+            sketch_image_url (str, optional): Only for wanx-sketch-to-image-v1,
+                can be local file.
+                Defaults to None.
+            workspace (str): The dashscope workspace id.
+            extra_input (Dict): The extra input parameters.
+            task (str): The task of api, ref doc.
+            function (str): The specific functions to be achieved. like:
+                colorization,super_resolution,expand,remove_watermaker,doodle,
+                description_edit_with_mask,description_edit,stylization_local,stylization_all
+            base_image_url (str): Enter the URL address of the target edited image.
+            mask_image_url (str): Provide the URL address of the image of the marked area by the user. It should be consistent with the image resolution of the base_image_url.
+            **kwargs:
+                n(int, `optional`): Number of images to synthesis.
+                size(str, `optional`): The output image(s) size(width*height).
+                similarity(float, `optional`): The similarity between the
+                    output image and the input image
+                sketch_weight(int, optional): How much the input sketch
+                    affects the output image[0-10], only for wanx-sketch-to-image-v1. # noqa E501
+                    Default 10.
+                realisticness(int, optional): The realisticness of the output
+                    image[0-10], only for wanx-sketch-to-image-v1. Default 5
+
+        Raises:
+            InputRequired: The prompt cannot be empty.
+
+        Returns:
+            ImageSynthesisResponse: The image(s) synthesis result.
+        """
+        return super().call(model,
+                            prompt,
+                            negative_prompt,
+                            images,
+                            api_key=api_key,
+                            sketch_image_url=sketch_image_url,
+                            ref_img=ref_img,
+                            workspace=workspace,
+                            extra_input=extra_input,
+                            task=task,
+                            function=function,
+                            mask_image_url=mask_image_url,
+                            base_image_url=base_image_url,
+                            **kwargs)
+
+    @classmethod
+    def sync_call(cls,
+                   model: str,
+                   prompt: Any,
+                   negative_prompt: Any = None,
+                   images: List[str] = None,
+                   api_key: str = None,
+                   sketch_image_url: str = None,
+                   ref_img: str = None,
+                   workspace: str = None,
+                   extra_input: Dict = None,
+                   task: str = None,
+                   function: str = None,
+                   mask_image_url: str = None,
+                   base_image_url: str = None,
+                   **kwargs) -> ImageSynthesisResponse:
+        """
+        Note: This method currently now only supports wan2.2-t2i-flash and wan2.2-t2i-plus.
+            Using other models will result in an error，More raw image models may be added for use later
+        """
+        task_group, f = _get_task_group_and_task(__name__)
+        inputs, kwargs, task = cls._get_input(model, prompt, negative_prompt,
+                                             images, api_key, sketch_image_url,
+                                             ref_img, extra_input, task, function,
+                                             mask_image_url, base_image_url, **kwargs)
+        response = BaseApi.call(model, inputs, task_group, task, f, api_key, workspace, **kwargs)
+        return ImageSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    def _get_input(cls,
+                    model: str,
+                    prompt: Any,
+                    negative_prompt: Any = None,
+                    images: List[str] = None,
+                    api_key: str = None,
+                    sketch_image_url: str = None,
+                    ref_img: str = None,
+                    extra_input: Dict = None,
+                    task: str = None,
+                    function: str = None,
+                    mask_image_url: str = None,
+                    base_image_url: str = None,
+                    **kwargs):
+        if prompt is None or not prompt:
+            raise InputRequired('prompt is required!')
+        inputs = {PROMPT: prompt}
+        has_upload = False
+        if negative_prompt is not None:
+            inputs[NEGATIVE_PROMPT] = negative_prompt
+        if images is not None and images and len(images) > 0:
+            new_images = []
+            for image in images:
+                is_upload, new_image = check_and_upload_local(
+                    model, image, api_key)
+                if is_upload:
+                    has_upload = True
+                new_images.append(new_image)
+            inputs[IMAGES] = new_images
+        if sketch_image_url is not None and sketch_image_url:
+            is_upload, sketch_image_url = check_and_upload_local(
+                model, sketch_image_url, api_key)
+            if is_upload:
+                has_upload = True
+            inputs['sketch_image_url'] = sketch_image_url
+        if ref_img is not None and ref_img:
+            is_upload, ref_img = check_and_upload_local(
+                model, ref_img, api_key)
+            if is_upload:
+                has_upload = True
+            inputs['ref_img'] = ref_img
+
+        if function is not None and function:
+            inputs['function'] = function
+
+        if mask_image_url is not None and mask_image_url:
+            is_upload, res_mask_image_url = check_and_upload_local(
+                model, mask_image_url, api_key)
+            if is_upload:
+                has_upload = True
+            inputs['mask_image_url'] = res_mask_image_url
+
+        if base_image_url is not None and base_image_url:
+            is_upload, res_base_image_url = check_and_upload_local(
+                model, base_image_url, api_key)
+            if is_upload:
+                has_upload = True
+            inputs['base_image_url'] = res_base_image_url
+
+        if extra_input is not None and extra_input:
+            inputs = {**inputs, **extra_input}
+
+        if has_upload:
+            headers = kwargs.pop('headers', {})
+            headers['X-DashScope-OssResourceResolve'] = 'enable'
+            kwargs['headers'] = headers
+
+        def __get_i2i_task(task, model) -> str:
+            # 处理task参数：优先使用有效的task值
+            if task is not None and task != "":
+                return task
+
+            # 根据model确定任务类型
+            if model is not None and model != "":
+                if 'imageedit' in model or "wan2.5-i2i" in model:
+                    return 'image2image'
+
+            # 默认返回文本到图像任务
+            return ImageSynthesis.task
+
+        task = __get_i2i_task(task, model)
+
+        return inputs, kwargs, task
+
+    @classmethod
+    def async_call(cls,
+                   model: str,
+                   prompt: Any,
+                   negative_prompt: Any = None,
+                   images: List[str] = None,
+                   api_key: str = None,
+                   sketch_image_url: str = None,
+                   ref_img: str = None,
+                   workspace: str = None,
+                   extra_input: Dict = None,
+                   task: str = None,
+                   function: str = None,
+                   mask_image_url: str = None,
+                   base_image_url: str = None,
+                   **kwargs) -> ImageSynthesisResponse:
+        """Create a image(s) synthesis task, and return task information.
+
+        Args:
+            model (str): The model, reference ``Models``.
+            prompt (Any): The prompt for image(s) synthesis.
+            negative_prompt (Any): The negative_prompt. Defaults to None.
+            images (List[str]): The input list of images url.
+            api_key (str, optional): The api api_key. Defaults to None.
+            sketch_image_url (str, optional): Only for wanx-sketch-to-image-v1.
+                Defaults to None.
+            workspace (str): The dashscope workspace id.
+            extra_input (Dict): The extra input parameters.
+            task (str): The task of api, ref doc.
+            function (str): The specific functions to be achieved. like:
+                colorization,super_resolution,expand,remove_watermaker,doodle,
+                description_edit_with_mask,description_edit,stylization_local,stylization_all
+            base_image_url (str): Enter the URL address of the target edited image.
+            mask_image_url (str): Provide the URL address of the image of the marked area by the user. It should be consistent with the image resolution of the base_image_url.
+            **kwargs(wanx-v1):
+                n(int, `optional`): Number of images to synthesis.
+                size: The output image(s) size, Default 1024*1024
+                similarity(float, `optional`): The similarity between the
+                    output image and the input image.
+                sketch_weight(int, optional): How much the input sketch
+                    affects the output image[0-10], only for wanx-sketch-to-image-v1. # noqa E501
+                    Default 10.
+                realisticness(int, optional): The realisticness of the output
+                    image[0-10], only for wanx-sketch-to-image-v1. Default 5
+
+        Raises:
+            InputRequired: The prompt cannot be empty.
+
+        Returns:
+            DashScopeAPIResponse: The image synthesis
+                task id in the response.
+        """
+        task_group, f = _get_task_group_and_task(__name__)
+        inputs, kwargs, task = cls._get_input(model, prompt, negative_prompt,
+                                             images, api_key, sketch_image_url,
+                                             ref_img, extra_input, task, function,
+                                             mask_image_url, base_image_url, **kwargs)
+        response = super().async_call(
+            model=model,
+            task_group=task_group,
+            task=task,
+            function=f,
+            api_key=api_key,
+            input=inputs,
+            workspace=workspace,
+            **kwargs)
+        return ImageSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    def fetch(cls,
+              task: Union[str, ImageSynthesisResponse],
+              api_key: str = None,
+              workspace: str = None) -> ImageSynthesisResponse:
+        """Fetch image(s) synthesis task status or result.
+
+        Args:
+            task (Union[str, ImageSynthesisResponse]): The task_id or
+                ImageSynthesisResponse return by async_call().
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            ImageSynthesisResponse: The task status or result.
+        """
+        response = super().fetch(task, api_key=api_key, workspace=workspace)
+        return ImageSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    def wait(cls,
+             task: Union[str, ImageSynthesisResponse],
+             api_key: str = None,
+             workspace: str = None) -> ImageSynthesisResponse:
+        """Wait for image(s) synthesis task to complete, and return the result.
+
+        Args:
+            task (Union[str, ImageSynthesisResponse]): The task_id or
+                ImageSynthesisResponse return by async_call().
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            ImageSynthesisResponse: The task result.
+        """
+        response = super().wait(task, api_key, workspace=workspace)
+        return ImageSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    def cancel(cls,
+               task: Union[str, ImageSynthesisResponse],
+               api_key: str = None,
+               workspace: str = None) -> DashScopeAPIResponse:
+        """Cancel image synthesis task.
+        Only tasks whose status is PENDING can be canceled.
+
+        Args:
+            task (Union[str, ImageSynthesisResponse]): The task_id or
+                ImageSynthesisResponse return by async_call().
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            DashScopeAPIResponse: The response data.
+        """
+        return super().cancel(task, api_key, workspace=workspace)
+
+    @classmethod
+    def list(cls,
+             start_time: str = None,
+             end_time: str = None,
+             model_name: str = None,
+             api_key_id: str = None,
+             region: str = None,
+             status: str = None,
+             page_no: int = 1,
+             page_size: int = 10,
+             api_key: str = None,
+             workspace: str = None,
+             **kwargs) -> DashScopeAPIResponse:
+        """List async tasks.
+
+        Args:
+            start_time (str, optional): The tasks start time,
+                for example: 20230420000000. Defaults to None.
+            end_time (str, optional): The tasks end time,
+                for example: 20230420000000. Defaults to None.
+            model_name (str, optional): The tasks model name. Defaults to None.
+            api_key_id (str, optional): The tasks api-key-id. Defaults to None.
+            region (str, optional): The service region,
+                for example: cn-beijing. Defaults to None.
+            status (str, optional): The status of tasks[PENDING,
+                RUNNING, SUCCEEDED, FAILED, CANCELED]. Defaults to None.
+            page_no (int, optional): The page number. Defaults to 1.
+            page_size (int, optional): The page size. Defaults to 10.
+            api_key (str, optional): The user api-key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            DashScopeAPIResponse: The response data.
+        """
+        return super().list(start_time=start_time,
+                            end_time=end_time,
+                            model_name=model_name,
+                            api_key_id=api_key_id,
+                            region=region,
+                            status=status,
+                            page_no=page_no,
+                            page_size=page_size,
+                            api_key=api_key,
+                            workspace=workspace,
+                            **kwargs)
+
+class AioImageSynthesis(BaseAsyncAioApi):
+    @classmethod
+    async def call(cls,
+                  model: str,
+                  prompt: Any,
+                  negative_prompt: Any = None,
+                  images: List[str] = None,
+                  api_key: str = None,
+                  sketch_image_url: str = None,
+                  ref_img: str = None,
+                  workspace: str = None,
+                  extra_input: Dict = None,
+                  task: str = None,
+                  function: str = None,
+                  mask_image_url: str = None,
+                  base_image_url: str = None,
+                  **kwargs) -> ImageSynthesisResponse:
+        """Call image(s) synthesis service and get result.
+
+        Args:
+            model (str): The model, reference ``Models``.
+            prompt (Any): The prompt for image(s) synthesis.
+            negative_prompt (Any): The negative_prompt. Defaults to None.
+            images (List[str]): The input list of images url,
+                currently not supported.
+            api_key (str, optional): The api api_key. Defaults to None.
+            sketch_image_url (str, optional): Only for wanx-sketch-to-image-v1,
+                can be local file.
+                Defaults to None.
+            workspace (str): The dashscope workspace id.
+            extra_input (Dict): The extra input parameters.
+            task (str): The task of api, ref doc.
+            function (str): The specific functions to be achieved. like:
+                colorization,super_resolution,expand,remove_watermaker,doodle,
+                description_edit_with_mask,description_edit,stylization_local,stylization_all
+            base_image_url (str): Enter the URL address of the target edited image.
+            mask_image_url (str): Provide the URL address of the image of the marked area by the user. It should be consistent with the image resolution of the base_image_url.
+            **kwargs:
+                n(int, `optional`): Number of images to synthesis.
+                size(str, `optional`): The output image(s) size(width*height).
+                similarity(float, `optional`): The similarity between the
+                    output image and the input image
+                sketch_weight(int, optional): How much the input sketch
+                    affects the output image[0-10], only for wanx-sketch-to-image-v1. # noqa E501
+                    Default 10.
+                realisticness(int, optional): The realisticness of the output
+                    image[0-10], only for wanx-sketch-to-image-v1. Default 5
+
+        Raises:
+            InputRequired: The prompt cannot be empty.
+
+        Returns:
+            ImageSynthesisResponse: The image(s) synthesis result.
+        """
+        task_group, f = _get_task_group_and_task(__name__)
+        inputs, kwargs, task = ImageSynthesis._get_input(model, prompt, negative_prompt,
+                                               images, api_key, sketch_image_url,
+                                               ref_img, extra_input, task, function,
+                                               mask_image_url, base_image_url, **kwargs)
+        response = await super().call(model, inputs, task_group, task, f, api_key, workspace, **kwargs)
+        return ImageSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    async def sync_call(cls,
+                   model: str,
+                   prompt: Any,
+                   negative_prompt: Any = None,
+                   images: List[str] = None,
+                   api_key: str = None,
+                   sketch_image_url: str = None,
+                   ref_img: str = None,
+                   workspace: str = None,
+                   extra_input: Dict = None,
+                   task: str = None,
+                   function: str = None,
+                   mask_image_url: str = None,
+                   base_image_url: str = None,
+                   **kwargs) -> ImageSynthesisResponse:
+        """
+        Note: This method currently now only supports wan2.2-t2i-flash and wan2.2-t2i-plus.
+            Using other models will result in an error，More raw image models may be added for use later
+        """
+        task_group, f = _get_task_group_and_task(__name__)
+        inputs, kwargs, task = ImageSynthesis._get_input(model, prompt, negative_prompt,
+                                             images, api_key, sketch_image_url,
+                                             ref_img, extra_input, task, function,
+                                             mask_image_url, base_image_url, **kwargs)
+        response = await BaseAioApi.call(model, inputs, task_group, task, f, api_key, workspace, **kwargs)
+        return ImageSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    async def async_call(cls,
+                  model: str,
+                  prompt: Any,
+                  negative_prompt: Any = None,
+                  images: List[str] = None,
+                  api_key: str = None,
+                  sketch_image_url: str = None,
+                  ref_img: str = None,
+                  workspace: str = None,
+                  extra_input: Dict = None,
+                  task: str = None,
+                  function: str = None,
+                  mask_image_url: str = None,
+                  base_image_url: str = None,
+                  **kwargs) -> ImageSynthesisResponse:
+        """Create a image(s) synthesis task, and return task information.
+
+        Args:
+            model (str): The model, reference ``Models``.
+            prompt (Any): The prompt for image(s) synthesis.
+            negative_prompt (Any): The negative_prompt. Defaults to None.
+            images (List[str]): The input list of images url.
+            api_key (str, optional): The api api_key. Defaults to None.
+            sketch_image_url (str, optional): Only for wanx-sketch-to-image-v1.
+                Defaults to None.
+            workspace (str): The dashscope workspace id.
+            extra_input (Dict): The extra input parameters.
+            task (str): The task of api, ref doc.
+            function (str): The specific functions to be achieved. like:
+                colorization,super_resolution,expand,remove_watermaker,doodle,
+                description_edit_with_mask,description_edit,stylization_local,stylization_all
+            base_image_url (str): Enter the URL address of the target edited image.
+            mask_image_url (str): Provide the URL address of the image of the marked area by the user. It should be consistent with the image resolution of the base_image_url.
+            **kwargs(wanx-v1):
+                n(int, `optional`): Number of images to synthesis.
+                size: The output image(s) size, Default 1024*1024
+                similarity(float, `optional`): The similarity between the
+                    output image and the input image.
+                sketch_weight(int, optional): How much the input sketch
+                    affects the output image[0-10], only for wanx-sketch-to-image-v1. # noqa E501
+                    Default 10.
+                realisticness(int, optional): The realisticness of the output
+                    image[0-10], only for wanx-sketch-to-image-v1. Default 5
+
+        Raises:
+            InputRequired: The prompt cannot be empty.
+
+        Returns:
+            DashScopeAPIResponse: The image synthesis
+                task id in the response.
+        """
+        task_group, f = _get_task_group_and_task(__name__)
+        inputs, kwargs, task = ImageSynthesis._get_input(model, prompt, negative_prompt,
+                                               images, api_key, sketch_image_url,
+                                               ref_img, extra_input, task, function,
+                                               mask_image_url, base_image_url, **kwargs)
+        response = await super().async_call(model, inputs, task_group, task, f, api_key, workspace, **kwargs)
+        return ImageSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    async def fetch(cls,
+              task: Union[str, ImageSynthesisResponse],
+              api_key: str = None,
+              workspace: str = None,
+              **kwargs,) -> ImageSynthesisResponse:
+        """Fetch image(s) synthesis task status or result.
+
+        Args:
+            task (Union[str, ImageSynthesisResponse]): The task_id or
+                ImageSynthesisResponse return by async_call().
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            ImageSynthesisResponse: The task status or result.
+        """
+        response = await super().fetch(task, api_key=api_key, workspace=workspace)
+        return ImageSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    async def wait(cls,
+             task: Union[str, ImageSynthesisResponse],
+             api_key: str = None,
+             workspace: str = None,
+             **kwargs) -> ImageSynthesisResponse:
+        """Wait for image(s) synthesis task to complete, and return the result.
+
+        Args:
+            task (Union[str, ImageSynthesisResponse]): The task_id or
+                ImageSynthesisResponse return by async_call().
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            ImageSynthesisResponse: The task result.
+        """
+        response = await super().wait(task, api_key, workspace=workspace)
+        return ImageSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    async def cancel(cls,
+                task: Union[str, ImageSynthesisResponse],
+                api_key: str = None,
+                workspace: str = None,
+                **kwargs,) -> DashScopeAPIResponse:
+        """Cancel image synthesis task.
+        Only tasks whose status is PENDING can be canceled.
+
+        Args:
+            task (Union[str, ImageSynthesisResponse]): The task_id or
+                ImageSynthesisResponse return by async_call().
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            DashScopeAPIResponse: The response data.
+        """
+        return await super().cancel(task, api_key, workspace=workspace)
+
+    @classmethod
+    async def list(cls,
+             start_time: str = None,
+             end_time: str = None,
+             model_name: str = None,
+             api_key_id: str = None,
+             region: str = None,
+             status: str = None,
+             page_no: int = 1,
+             page_size: int = 10,
+             api_key: str = None,
+             workspace: str = None,
+             **kwargs) -> DashScopeAPIResponse:
+        """List async tasks.
+
+        Args:
+            start_time (str, optional): The tasks start time,
+                for example: 20230420000000. Defaults to None.
+            end_time (str, optional): The tasks end time,
+                for example: 20230420000000. Defaults to None.
+            model_name (str, optional): The tasks model name. Defaults to None.
+            api_key_id (str, optional): The tasks api-key-id. Defaults to None.
+            region (str, optional): The service region,
+                for example: cn-beijing. Defaults to None.
+            status (str, optional): The status of tasks[PENDING,
+                RUNNING, SUCCEEDED, FAILED, CANCELED]. Defaults to None.
+            page_no (int, optional): The page number. Defaults to 1.
+            page_size (int, optional): The page size. Defaults to 10.
+            api_key (str, optional): The user api-key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            DashScopeAPIResponse: The response data.
+        """
+        return await super().list(start_time=start_time,
+                            end_time=end_time,
+                            model_name=model_name,
+                            api_key_id=api_key_id,
+                            region=region,
+                            status=status,
+                            page_no=page_no,
+                            page_size=page_size,
+                            api_key=api_key,
+                            workspace=workspace,
+                            **kwargs)

backend_service/venv/lib/python3.13/site-packages/dashscope/aigc/multimodal_conversation.py

@@ -0,0 +1,371 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import copy
+from typing import AsyncGenerator, Generator, List, Union
+
+from dashscope.api_entities.dashscope_response import \
+    MultiModalConversationResponse
+from dashscope.client.base_api import BaseAioApi, BaseApi
+from dashscope.common.error import InputRequired, ModelRequired
+from dashscope.common.utils import _get_task_group_and_task
+from dashscope.utils.oss_utils import preprocess_message_element
+from dashscope.utils.param_utils import ParamUtil
+from dashscope.utils.message_utils import merge_multimodal_single_response
+
+
+class MultiModalConversation(BaseApi):
+    """MultiModal conversational robot interface.
+    """
+    task = 'multimodal-generation'
+    function = 'generation'
+
+    class Models:
+        qwen_vl_chat_v1 = 'qwen-vl-chat-v1'
+
+    @classmethod
+    def call(
+        cls,
+        model: str,
+        messages: List = None,
+        api_key: str = None,
+        workspace: str = None,
+        text: str = None,
+        voice: str = None,
+        language_type: str = None,
+        **kwargs
+    ) -> Union[MultiModalConversationResponse, Generator[
+            MultiModalConversationResponse, None, None]]:
+        """Call the conversation model service.
+
+        Args:
+            model (str): The requested model, such as 'qwen-multimodal-v1'
+            messages (list): The generation messages.
+                examples:
+                    [
+                        {
+                            "role": "system",
+                            "content": [
+                                {"text": "你是达摩院的生活助手机器人。"}
+                            ]
+                        },
+                        {
+                            "role": "user",
+                            "content": [
+                                {"image": "http://XXXX"},
+                                {"text": "这个图片是哪里？"},
+                            ]
+                        }
+                    ]
+            api_key (str, optional): The api api_key, can be None,
+                if None, will retrieve by rule [1].
+                [1]: https://help.aliyun.com/zh/dashscope/developer-reference/api-key-settings. # noqa E501
+            workspace (str): The dashscope workspace id.
+            text (str): The text to generate.
+            voice (str): The voice name of qwen tts, include 'Cherry'/'Ethan'/'Sunny'/'Dylan' and so on,
+                    you can get the total voice list : https://help.aliyun.com/zh/model-studio/qwen-tts.
+            language_type (str): The synthesized language type, default is 'auto', useful for [qwen3-tts].
+            **kwargs:
+                stream(bool, `optional`): Enable server-sent events
+                    (ref: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events)  # noqa E501
+                    the result will back partially[qwen-turbo,bailian-v1].
+                max_length(int, `optional`): The maximum length of tokens to
+                    generate. The token count of your prompt plus max_length
+                    cannot exceed the model's context length. Most models
+                    have a context length of 2000 tokens[qwen-turbo,bailian-v1]. # noqa E501
+                top_p(float, `optional`): A sampling strategy, called nucleus
+                    sampling, where the model considers the results of the
+                    tokens with top_p probability mass. So 0.1 means only
+                    the tokens comprising the top 10% probability mass are
+                    considered[qwen-turbo,bailian-v1].
+                top_k(float, `optional`):
+
+
+        Raises:
+            InvalidInput: The history and auto_history are mutually exclusive.
+
+        Returns:
+            Union[MultiModalConversationResponse,
+                  Generator[MultiModalConversationResponse, None, None]]: If
+            stream is True, return Generator, otherwise MultiModalConversationResponse.
+        """
+        if model is None or not model:
+            raise ModelRequired('Model is required!')
+        task_group, _ = _get_task_group_and_task(__name__)
+        input = {}
+        msg_copy = None
+
+        if messages is not None and messages:
+            msg_copy = copy.deepcopy(messages)
+            has_upload = cls._preprocess_messages(model, msg_copy, api_key)
+            if has_upload:
+                headers = kwargs.pop('headers', {})
+                headers['X-DashScope-OssResourceResolve'] = 'enable'
+                kwargs['headers'] = headers
+
+        if text is not None and text:
+            input.update({'text': text})
+        if voice is not None and voice:
+            input.update({'voice': voice})
+        if language_type is not None and language_type:
+            input.update({'language_type': language_type})
+        if msg_copy is not None:
+            input.update({'messages': msg_copy})
+
+        # Check if we need to merge incremental output
+        is_incremental_output = kwargs.get('incremental_output', None)
+        to_merge_incremental_output = False
+        is_stream = kwargs.get('stream', False)
+        if (ParamUtil.should_modify_incremental_output(model) and
+                is_stream and is_incremental_output is not None and is_incremental_output is False):
+            to_merge_incremental_output = True
+            kwargs['incremental_output'] = True
+
+        # Pass incremental_to_full flag via headers user-agent
+        if 'headers' not in kwargs:
+            kwargs['headers'] = {}
+        flag = '1' if to_merge_incremental_output else '0'
+        kwargs['headers']['user-agent'] = f'incremental_to_full/{flag}'
+
+        response = super().call(model=model,
+                                task_group=task_group,
+                                task=MultiModalConversation.task,
+                                function=MultiModalConversation.function,
+                                api_key=api_key,
+                                input=input,
+                                workspace=workspace,
+                                **kwargs)
+        if is_stream:
+            if to_merge_incremental_output:
+                # Extract n parameter for merge logic
+                n = kwargs.get('n', 1)
+                return cls._merge_multimodal_response(response, n)
+            else:
+                return (MultiModalConversationResponse.from_api_response(rsp)
+                        for rsp in response)
+        else:
+            return MultiModalConversationResponse.from_api_response(response)
+
+    @classmethod
+    def _preprocess_messages(cls, model: str, messages: List[dict],
+                             api_key: str):
+        """
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {"image": ""},
+                        {"text": ""},
+                    ]
+                }
+            ]
+        """
+        has_upload = False
+        for message in messages:
+            content = message['content']
+            for elem in content:
+                if not isinstance(elem,
+                                  (int, float, bool, str, bytes, bytearray)):
+                    is_upload = preprocess_message_element(
+                        model, elem, api_key)
+                    if is_upload and not has_upload:
+                        has_upload = True
+        return has_upload
+
+    @classmethod
+    def _merge_multimodal_response(cls, response, n=1) -> Generator[MultiModalConversationResponse, None, None]:
+        """Merge incremental response chunks to simulate non-incremental output."""
+        accumulated_data = {}
+
+        for rsp in response:
+            parsed_response = MultiModalConversationResponse.from_api_response(rsp)
+            result = merge_multimodal_single_response(parsed_response, accumulated_data, n)
+            if result is True:
+                yield parsed_response
+            elif isinstance(result, list):
+                # Multiple responses to yield (for n>1 non-stop cases)
+                for resp in result:
+                    yield resp
+
+
+class AioMultiModalConversation(BaseAioApi):
+    """Async MultiModal conversational robot interface.
+    """
+    task = 'multimodal-generation'
+    function = 'generation'
+
+    class Models:
+        qwen_vl_chat_v1 = 'qwen-vl-chat-v1'
+
+    @classmethod
+    async def call(
+        cls,
+        model: str,
+        messages: List = None,
+        api_key: str = None,
+        workspace: str = None,
+        text: str = None,
+        voice: str = None,
+        language_type: str = None,
+        **kwargs
+    ) -> Union[MultiModalConversationResponse, AsyncGenerator[
+            MultiModalConversationResponse, None]]:
+        """Call the conversation model service asynchronously.
+
+        Args:
+            model (str): The requested model, such as 'qwen-multimodal-v1'
+            messages (list): The generation messages.
+                examples:
+                    [
+                        {
+                            "role": "system",
+                            "content": [
+                                {"text": "你是达摩院的生活助手机器人。"}
+                            ]
+                        },
+                        {
+                            "role": "user",
+                            "content": [
+                                {"image": "http://XXXX"},
+                                {"text": "这个图片是哪里？"},
+                            ]
+                        }
+                    ]
+            api_key (str, optional): The api api_key, can be None,
+                if None, will retrieve by rule [1].
+                [1]: https://help.aliyun.com/zh/dashscope/developer-reference/api-key-settings. # noqa E501
+            workspace (str): The dashscope workspace id.
+            text (str): The text to generate.
+            voice (str): The voice name of qwen tts, include 'Cherry'/'Ethan'/'Sunny'/'Dylan' and so on,
+                    you can get the total voice list : https://help.aliyun.com/zh/model-studio/qwen-tts.
+            language_type (str): The synthesized language type, default is 'auto', useful for [qwen3-tts].
+            **kwargs:
+                stream(bool, `optional`): Enable server-sent events
+                    (ref: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events)  # noqa E501
+                    the result will back partially[qwen-turbo,bailian-v1].
+                max_length(int, `optional`): The maximum length of tokens to
+                    generate. The token count of your prompt plus max_length
+                    cannot exceed the model's context length. Most models
+                    have a context length of 2000 tokens[qwen-turbo,bailian-v1]. # noqa E501
+                top_p(float, `optional`): A sampling strategy, called nucleus
+                    sampling, where the model considers the results of the
+                    tokens with top_p probability mass. So 0.1 means only
+                    the tokens comprising the top 10% probability mass are
+                    considered[qwen-turbo,bailian-v1].
+                top_k(float, `optional`):
+
+        Raises:
+            InvalidInput: The history and auto_history are mutually exclusive.
+
+        Returns:
+            Union[MultiModalConversationResponse,
+                  AsyncGenerator[MultiModalConversationResponse, None]]: If
+            stream is True, return AsyncGenerator, otherwise MultiModalConversationResponse.
+        """
+        if model is None or not model:
+            raise ModelRequired('Model is required!')
+        task_group, _ = _get_task_group_and_task(__name__)
+        input = {}
+        msg_copy = None
+
+        if messages is not None and messages:
+            msg_copy = copy.deepcopy(messages)
+            has_upload = cls._preprocess_messages(model, msg_copy, api_key)
+            if has_upload:
+                headers = kwargs.pop('headers', {})
+                headers['X-DashScope-OssResourceResolve'] = 'enable'
+                kwargs['headers'] = headers
+
+        if text is not None and text:
+            input.update({'text': text})
+        if voice is not None and voice:
+            input.update({'voice': voice})
+        if language_type is not None and language_type:
+            input.update({'language_type': language_type})
+        if msg_copy is not None:
+            input.update({'messages': msg_copy})
+
+        # Check if we need to merge incremental output
+        is_incremental_output = kwargs.get('incremental_output', None)
+        to_merge_incremental_output = False
+        is_stream = kwargs.get('stream', False)
+        if (ParamUtil.should_modify_incremental_output(model) and
+                is_stream and is_incremental_output is not None and is_incremental_output is False):
+            to_merge_incremental_output = True
+            kwargs['incremental_output'] = True
+
+        # Pass incremental_to_full flag via headers user-agent
+        if 'headers' not in kwargs:
+            kwargs['headers'] = {}
+        flag = '1' if to_merge_incremental_output else '0'
+        kwargs['headers']['user-agent'] = (
+            kwargs['headers'].get('user-agent', '') +
+            f'; incremental_to_full/{flag}'
+        )
+
+        response = await super().call(model=model,
+                                      task_group=task_group,
+                                      task=AioMultiModalConversation.task,
+                                      function=AioMultiModalConversation.function,
+                                      api_key=api_key,
+                                      input=input,
+                                      workspace=workspace,
+                                      **kwargs)
+        if is_stream:
+            if to_merge_incremental_output:
+                # Extract n parameter for merge logic
+                n = kwargs.get('n', 1)
+                return cls._merge_multimodal_response(response, n)
+            else:
+                return cls._stream_responses(response)
+        else:
+            return MultiModalConversationResponse.from_api_response(response)
+
+    @classmethod
+    def _preprocess_messages(cls, model: str, messages: List[dict],
+                             api_key: str):
+        """
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {"image": ""},
+                        {"text": ""},
+                    ]
+                }
+            ]
+        """
+        has_upload = False
+        for message in messages:
+            content = message['content']
+            for elem in content:
+                if not isinstance(elem,
+                                  (int, float, bool, str, bytes, bytearray)):
+                    is_upload = preprocess_message_element(
+                        model, elem, api_key)
+                    if is_upload and not has_upload:
+                        has_upload = True
+        return has_upload
+
+    @classmethod
+    async def _stream_responses(cls, response) -> AsyncGenerator[MultiModalConversationResponse, None]:
+        """Convert async response stream to MultiModalConversationResponse stream."""
+        # Type hint: when stream=True, response is actually an AsyncIterable
+        async for rsp in response:  # type: ignore
+            yield MultiModalConversationResponse.from_api_response(rsp)
+
+    @classmethod
+    async def _merge_multimodal_response(cls, response, n=1) -> AsyncGenerator[MultiModalConversationResponse, None]:
+        """Async version of merge incremental response chunks."""
+        accumulated_data = {}
+
+        async for rsp in response:
+            parsed_response = MultiModalConversationResponse.from_api_response(rsp)
+            result = merge_multimodal_single_response(parsed_response, accumulated_data, n)
+            if result is True:
+                yield parsed_response
+            elif isinstance(result, list):
+                # Multiple responses to yield (for n>1 non-stop cases)
+                for resp in result:
+                    yield resp
+
+

backend_service/venv/lib/python3.13/site-packages/dashscope/aigc/video_synthesis.py

@@ -0,0 +1,573 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+from typing import Any, Dict, Union
+
+from dashscope.api_entities.dashscope_response import (DashScopeAPIResponse,
+                                                       VideoSynthesisResponse)
+from dashscope.client.base_api import BaseAsyncApi, BaseAsyncAioApi
+from dashscope.common.constants import PROMPT
+from dashscope.common.utils import _get_task_group_and_task
+from dashscope.utils.oss_utils import check_and_upload_local
+
+
+class VideoSynthesis(BaseAsyncApi):
+    task = 'video-generation'
+    """API for video synthesis.
+    """
+    class Models:
+        """@deprecated, use wanx2.1-t2v-plus instead"""
+        wanx_txt2video_pro = 'wanx-txt2video-pro'
+        """@deprecated, use wanx2.1-i2v-plus instead"""
+        wanx_img2video_pro = 'wanx-img2video-pro'
+
+        wanx_2_1_t2v_turbo = 'wanx2.1-t2v-turbo'
+        wanx_2_1_t2v_plus = 'wanx2.1-t2v-plus'
+
+        wanx_2_1_i2v_plus = 'wanx2.1-i2v-plus'
+        wanx_2_1_i2v_turbo = 'wanx2.1-i2v-turbo'
+
+        wanx_2_1_kf2v_plus = 'wanx2.1-kf2v-plus'
+        wanx_kf2v = 'wanx-kf2v'
+
+    @classmethod
+    def call(cls,
+             model: str,
+             prompt: Any = None,
+             # """@deprecated, use prompt_extend in parameters """
+             extend_prompt: bool = True,
+             negative_prompt: str = None,
+             template: str = None,
+             img_url: str = None,
+             audio_url: str = None,
+             api_key: str = None,
+             extra_input: Dict = None,
+             workspace: str = None,
+             task: str = None,
+             head_frame: str = None,
+             tail_frame: str = None,
+             first_frame_url: str = None,
+             last_frame_url: str = None,
+             **kwargs) -> VideoSynthesisResponse:
+        """Call video synthesis service and get result.
+
+        Args:
+            model (str): The model, reference ``Models``.
+            prompt (Any): The prompt for video synthesis.
+            extend_prompt (bool): @deprecated, use prompt_extend in parameters
+            negative_prompt (str): The negative prompt is the opposite of the prompt meaning.
+            template (str): LoRa input, such as gufeng, katong, etc.
+            img_url (str): The input image url, Generate the URL of the image referenced by the video.
+            audio_url (str): The input audio url
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+            extra_input (Dict): The extra input parameters.
+            task (str): The task of api, ref doc.
+            first_frame_url (str): The URL of the first frame image for generating the video.
+            last_frame_url (str): The URL of the last frame image for generating the video.
+            **kwargs:
+                size(str, `optional`): The output video size(width*height).
+                duration(int, optional): The duration. Duration of video generation. The default value is 5, in seconds.
+                seed(int, optional): The seed. The random seed for video generation. The default value is 5.
+
+        Raises:
+            InputRequired: The prompt cannot be empty.
+
+        Returns:
+            VideoSynthesisResponse: The video synthesis result.
+        """
+        return super().call(model,
+                            prompt,
+                            img_url=img_url,
+                            audio_url=audio_url,
+                            api_key=api_key,
+                            extend_prompt=extend_prompt,
+                            negative_prompt=negative_prompt,
+                            template=template,
+                            workspace=workspace,
+                            extra_input=extra_input,
+                            task=task,
+                            head_frame=head_frame,
+                            tail_frame=tail_frame,
+                            first_frame_url=first_frame_url,
+                            last_frame_url=last_frame_url,
+                            **kwargs)
+
+    @classmethod
+    def _get_input(cls,
+                   model: str,
+                   prompt: Any = None,
+                   img_url: str = None,
+                   audio_url: str = None,
+                   # """@deprecated, use prompt_extend in parameters """
+                   extend_prompt: bool = True,
+                   negative_prompt: str = None,
+                   template: str = None,
+                   api_key: str = None,
+                   extra_input: Dict = None,
+                   task: str = None,
+                   function: str = None,
+                   head_frame: str = None,
+                   tail_frame: str = None,
+                   first_frame_url: str = None,
+                   last_frame_url: str = None,
+                   **kwargs):
+
+        inputs = {PROMPT: prompt, 'extend_prompt': extend_prompt}
+        if negative_prompt:
+            inputs['negative_prompt'] = negative_prompt
+        if template:
+            inputs['template'] = template
+        if function:
+            inputs['function'] = function
+
+        has_upload = False
+
+        if img_url is not None and img_url:
+            is_upload, res_img_url = check_and_upload_local(
+                model, img_url, api_key)
+            if is_upload:
+                has_upload = True
+            inputs['img_url'] = res_img_url
+
+        if audio_url is not None and audio_url:
+            is_upload, res_audio_url = check_and_upload_local(
+                model, audio_url, api_key)
+            if is_upload:
+                has_upload = True
+            inputs['audio_url'] = res_audio_url
+
+        if head_frame is not None and head_frame:
+            is_upload, res_head_frame = check_and_upload_local(
+                model, head_frame, api_key)
+            if is_upload:
+                has_upload = True
+            inputs['head_frame'] = res_head_frame
+
+        if tail_frame is not None and tail_frame:
+            is_upload, res_tail_frame = check_and_upload_local(
+                model, tail_frame, api_key)
+            if is_upload:
+                has_upload = True
+            inputs['tail_frame'] = res_tail_frame
+
+        if first_frame_url is not None and first_frame_url:
+            is_upload, res_first_frame_url = check_and_upload_local(
+                model, first_frame_url, api_key)
+            if is_upload:
+                has_upload = True
+            inputs['first_frame_url'] = res_first_frame_url
+
+        if last_frame_url is not None and last_frame_url:
+            is_upload, res_last_frame_url = check_and_upload_local(
+                model, last_frame_url, api_key)
+            if is_upload:
+                has_upload = True
+            inputs['last_frame_url'] = res_last_frame_url
+
+        if extra_input is not None and extra_input:
+            inputs = {**inputs, **extra_input}
+        if has_upload:
+            headers = kwargs.pop('headers', {})
+            headers['X-DashScope-OssResourceResolve'] = 'enable'
+            kwargs['headers'] = headers
+
+        if task is None:
+            task = VideoSynthesis.task
+        if model is not None and model and 'kf2v' in model:
+            task = 'image2video'
+
+        return inputs, kwargs, task
+
+    @classmethod
+    def async_call(cls,
+                   model: str,
+                   prompt: Any = None,
+                   img_url: str = None,
+                   audio_url: str = None,
+                   # """@deprecated, use prompt_extend in parameters """
+                   extend_prompt: bool = True,
+                   negative_prompt: str = None,
+                   template: str = None,
+                   api_key: str = None,
+                   extra_input: Dict = None,
+                   workspace: str = None,
+                   task: str = None,
+                   head_frame: str = None,
+                   tail_frame: str = None,
+                   first_frame_url: str = None,
+                   last_frame_url: str = None,
+                   **kwargs) -> VideoSynthesisResponse:
+        """Create a video synthesis task, and return task information.
+
+        Args:
+            model (str): The model, reference ``Models``.
+            prompt (Any): The prompt for video synthesis.
+            extend_prompt (bool): @deprecated, use prompt_extend in parameters
+            negative_prompt (str): The negative prompt is the opposite of the prompt meaning.
+            template (str): LoRa input, such as gufeng, katong, etc.
+            img_url (str): The input image url, Generate the URL of the image referenced by the video.
+            audio_url (str): The input audio url.
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+            extra_input (Dict): The extra input parameters.
+            task (str): The task of api, ref doc.
+            first_frame_url (str): The URL of the first frame image for generating the video.
+            last_frame_url (str): The URL of the last frame image for generating the video.
+            **kwargs:
+                size(str, `optional`): The output video size(width*height).
+                duration(int, optional): The duration. Duration of video generation. The default value is 5, in seconds.
+                seed(int, optional): The seed. The random seed for video generation. The default value is 5.
+
+        Raises:
+            InputRequired: The prompt cannot be empty.
+
+        Returns:
+            DashScopeAPIResponse: The video synthesis
+                task id in the response.
+        """
+        task_group, function = _get_task_group_and_task(__name__)
+
+        inputs, kwargs, task = cls._get_input(
+            model, prompt, img_url, audio_url, extend_prompt, negative_prompt, template, api_key,
+            extra_input, task, function, head_frame, tail_frame,
+            first_frame_url, last_frame_url, **kwargs)
+
+        response = super().async_call(
+            model=model,
+            task_group=task_group,
+            task=VideoSynthesis.task if task is None else task,
+            function=function,
+            api_key=api_key,
+            input=inputs,
+            workspace=workspace,
+            **kwargs)
+        return VideoSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    def fetch(cls,
+              task: Union[str, VideoSynthesisResponse],
+              api_key: str = None,
+              workspace: str = None) -> VideoSynthesisResponse:
+        """Fetch video synthesis task status or result.
+
+        Args:
+            task (Union[str, VideoSynthesisResponse]): The task_id or
+                VideoSynthesisResponse return by async_call().
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            VideoSynthesisResponse: The task status or result.
+        """
+        response = super().fetch(task, api_key=api_key, workspace=workspace)
+        return VideoSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    def wait(cls,
+             task: Union[str, VideoSynthesisResponse],
+             api_key: str = None,
+             workspace: str = None) -> VideoSynthesisResponse:
+        """Wait for video synthesis task to complete, and return the result.
+
+        Args:
+            task (Union[str, VideoSynthesisResponse]): The task_id or
+                VideoSynthesisResponse return by async_call().
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            VideoSynthesisResponse: The task result.
+        """
+        response = super().wait(task, api_key, workspace=workspace)
+        return VideoSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    def cancel(cls,
+               task: Union[str, VideoSynthesisResponse],
+               api_key: str = None,
+               workspace: str = None) -> DashScopeAPIResponse:
+        """Cancel video synthesis task.
+        Only tasks whose status is PENDING can be canceled.
+
+        Args:
+            task (Union[str, VideoSynthesisResponse]): The task_id or
+                VideoSynthesisResponse return by async_call().
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            DashScopeAPIResponse: The response data.
+        """
+        return super().cancel(task, api_key, workspace=workspace)
+
+    @classmethod
+    def list(cls,
+             start_time: str = None,
+             end_time: str = None,
+             model_name: str = None,
+             api_key_id: str = None,
+             region: str = None,
+             status: str = None,
+             page_no: int = 1,
+             page_size: int = 10,
+             api_key: str = None,
+             workspace: str = None,
+             **kwargs) -> DashScopeAPIResponse:
+        """List async tasks.
+
+        Args:
+            start_time (str, optional): The tasks start time,
+                for example: 20230420000000. Defaults to None.
+            end_time (str, optional): The tasks end time,
+                for example: 20230420000000. Defaults to None.
+            model_name (str, optional): The tasks model name. Defaults to None.
+            api_key_id (str, optional): The tasks api-key-id. Defaults to None.
+            region (str, optional): The service region,
+                for example: cn-beijing. Defaults to None.
+            status (str, optional): The status of tasks[PENDING,
+                RUNNING, SUCCEEDED, FAILED, CANCELED]. Defaults to None.
+            page_no (int, optional): The page number. Defaults to 1.
+            page_size (int, optional): The page size. Defaults to 10.
+            api_key (str, optional): The user api-key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            DashScopeAPIResponse: The response data.
+        """
+        return super().list(start_time=start_time,
+                            end_time=end_time,
+                            model_name=model_name,
+                            api_key_id=api_key_id,
+                            region=region,
+                            status=status,
+                            page_no=page_no,
+                            page_size=page_size,
+                            api_key=api_key,
+                            workspace=workspace,
+                            **kwargs)
+
+class AioVideoSynthesis(BaseAsyncAioApi):
+    @classmethod
+    async def call(cls,
+                   model: str,
+                   prompt: Any = None,
+                   img_url: str = None,
+                   audio_url: str = None,
+                   # """@deprecated, use prompt_extend in parameters """
+                   extend_prompt: bool = True,
+                   negative_prompt: str = None,
+                   template: str = None,
+                   api_key: str = None,
+                   extra_input: Dict = None,
+                   workspace: str = None,
+                   task: str = None,
+                   head_frame: str = None,
+                   tail_frame: str = None,
+                   first_frame_url: str = None,
+                   last_frame_url: str = None,
+                   **kwargs) -> VideoSynthesisResponse:
+        """Call video synthesis service and get result.
+
+        Args:
+            model (str): The model, reference ``Models``.
+            prompt (Any): The prompt for video synthesis.
+            extend_prompt (bool): @deprecated, use prompt_extend in parameters
+            negative_prompt (str): The negative prompt is the opposite of the prompt meaning.
+            template (str): LoRa input, such as gufeng, katong, etc.
+            img_url (str): The input image url, Generate the URL of the image referenced by the video.
+            audio_url (str): The input audio url.
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+            extra_input (Dict): The extra input parameters.
+            task (str): The task of api, ref doc.
+            first_frame_url (str): The URL of the first frame image for generating the video.
+            last_frame_url (str): The URL of the last frame image for generating the video.
+            **kwargs:
+                size(str, `optional`): The output video size(width*height).
+                duration(int, optional): The duration. Duration of video generation. The default value is 5, in seconds.
+                seed(int, optional): The seed. The random seed for video generation. The default value is 5.
+
+        Raises:
+            InputRequired: The prompt cannot be empty.
+
+        Returns:
+            VideoSynthesisResponse: The video synthesis result.
+        """
+        task_group, f = _get_task_group_and_task(__name__)
+        inputs, kwargs, task = VideoSynthesis._get_input(
+            model, prompt, img_url, audio_url, extend_prompt, negative_prompt, template, api_key,
+            extra_input, task, f, head_frame, tail_frame,
+            first_frame_url, last_frame_url, **kwargs)
+        response = await super().call(model, inputs, task_group, task, f, api_key, workspace, **kwargs)
+        return VideoSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    async def async_call(cls,
+                   model: str,
+                   prompt: Any = None,
+                   img_url: str = None,
+                   audio_url: str = None,
+                   # """@deprecated, use prompt_extend in parameters """
+                   extend_prompt: bool = True,
+                   negative_prompt: str = None,
+                   template: str = None,
+                   api_key: str = None,
+                   extra_input: Dict = None,
+                   workspace: str = None,
+                   task: str = None,
+                   head_frame: str = None,
+                   tail_frame: str = None,
+                   first_frame_url: str = None,
+                   last_frame_url: str = None,
+                   **kwargs) -> VideoSynthesisResponse:
+        """Create a video synthesis task, and return task information.
+
+        Args:
+            model (str): The model, reference ``Models``.
+            prompt (Any): The prompt for video synthesis.
+            extend_prompt (bool): @deprecated, use prompt_extend in parameters
+            negative_prompt (str): The negative prompt is the opposite of the prompt meaning.
+            template (str): LoRa input, such as gufeng, katong, etc.
+            img_url (str): The input image url, Generate the URL of the image referenced by the video.
+            audio_url (str): The input audio url.
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+            extra_input (Dict): The extra input parameters.
+            task (str): The task of api, ref doc.
+            first_frame_url (str): The URL of the first frame image for generating the video.
+            last_frame_url (str): The URL of the last frame image for generating the video.
+            **kwargs:
+                size(str, `optional`): The output video size(width*height).
+                duration(int, optional): The duration. Duration of video generation. The default value is 5, in seconds.
+                seed(int, optional): The seed. The random seed for video generation. The default value is 5.
+
+        Raises:
+            InputRequired: The prompt cannot be empty.
+
+        Returns:
+            DashScopeAPIResponse: The video synthesis
+                task id in the response.
+        """
+        task_group, function = _get_task_group_and_task(__name__)
+
+        inputs, kwargs, task = VideoSynthesis._get_input(
+            model, prompt, img_url, audio_url, extend_prompt, negative_prompt, template, api_key,
+            extra_input, task, function, head_frame, tail_frame,
+            first_frame_url, last_frame_url, **kwargs)
+
+        response = await super().async_call(
+            model=model,
+            task_group=task_group,
+            task=VideoSynthesis.task if task is None else task,
+            function=function,
+            api_key=api_key,
+            input=inputs,
+            workspace=workspace,
+            **kwargs)
+        return VideoSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    async def fetch(cls,
+                    task: Union[str, VideoSynthesisResponse],
+                    api_key: str = None,
+                    workspace: str = None,
+                    **kwargs) -> VideoSynthesisResponse:
+        """Fetch video synthesis task status or result.
+
+        Args:
+            task (Union[str, VideoSynthesisResponse]): The task_id or
+                VideoSynthesisResponse return by async_call().
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            VideoSynthesisResponse: The task status or result.
+        """
+        response = await super().fetch(task, api_key=api_key, workspace=workspace)
+        return VideoSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    async def wait(cls,
+                task: Union[str, VideoSynthesisResponse],
+                api_key: str = None,
+                workspace: str = None,
+                **kwargs) -> VideoSynthesisResponse:
+        """Wait for video synthesis task to complete, and return the result.
+
+        Args:
+            task (Union[str, VideoSynthesisResponse]): The task_id or
+                VideoSynthesisResponse return by async_call().
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            VideoSynthesisResponse: The task result.
+        """
+        response = await super().wait(task, api_key, workspace=workspace)
+        return VideoSynthesisResponse.from_api_response(response)
+
+    @classmethod
+    async def cancel(cls,
+                   task: Union[str, VideoSynthesisResponse],
+                   api_key: str = None,
+                   workspace: str = None,
+                   **kwargs) -> DashScopeAPIResponse:
+        """Cancel video synthesis task.
+        Only tasks whose status is PENDING can be canceled.
+
+        Args:
+            task (Union[str, VideoSynthesisResponse]): The task_id or
+                VideoSynthesisResponse return by async_call().
+            api_key (str, optional): The api api_key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            DashScopeAPIResponse: The response data.
+        """
+        return await super().cancel(task, api_key, workspace=workspace)
+
+    @classmethod
+    async def list(cls,
+             start_time: str = None,
+             end_time: str = None,
+             model_name: str = None,
+             api_key_id: str = None,
+             region: str = None,
+             status: str = None,
+             page_no: int = 1,
+             page_size: int = 10,
+             api_key: str = None,
+             workspace: str = None,
+             **kwargs) -> DashScopeAPIResponse:
+        """List async tasks.
+
+        Args:
+            start_time (str, optional): The tasks start time,
+                for example: 20230420000000. Defaults to None.
+            end_time (str, optional): The tasks end time,
+                for example: 20230420000000. Defaults to None.
+            model_name (str, optional): The tasks model name. Defaults to None.
+            api_key_id (str, optional): The tasks api-key-id. Defaults to None.
+            region (str, optional): The service region,
+                for example: cn-beijing. Defaults to None.
+            status (str, optional): The status of tasks[PENDING,
+                RUNNING, SUCCEEDED, FAILED, CANCELED]. Defaults to None.
+            page_no (int, optional): The page number. Defaults to 1.
+            page_size (int, optional): The page size. Defaults to 10.
+            api_key (str, optional): The user api-key. Defaults to None.
+            workspace (str): The dashscope workspace id.
+
+        Returns:
+            DashScopeAPIResponse: The response data.
+        """
+        return await super().list(start_time=start_time,
+                            end_time=end_time,
+                            model_name=model_name,
+                            api_key_id=api_key_id,
+                            region=region,
+                            status=status,
+                            page_no=page_no,
+                            page_size=page_size,
+                            api_key=api_key,
+                            workspace=workspace,
+                            **kwargs)