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# 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 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
# type: ignore
def call( # pylint: disable=arguments-renamed,too-many-branches
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 # pylint: disable=line-too-long
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, # pylint: disable=line-too-long
you can get the total voice list : https://help.aliyun.com/zh/model-studio/qwen-tts. # pylint: disable=line-too-long
language_type (str): The synthesized language type, default is 'auto', useful for [qwen3-tts]. # pylint: disable=line-too-long
**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 # pylint: disable=line-too-long
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 = {} # pylint: disable=redefined-builtin
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}) # type: ignore
# 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
upload_certificate = None
for message in messages:
if message.get("role", "") == "user":
content = message["content"]
for elem in content:
if not isinstance(
elem,
(int, float, bool, str, bytes, bytearray),
):
(
is_upload,
upload_certificate,
) = preprocess_message_element(
model,
elem,
api_key,
upload_certificate, # type: ignore[arg-type]
)
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.""" # noqa: E501
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 # type: ignore
async def call( # pylint: disable=arguments-renamed,too-many-branches
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 # pylint: disable=line-too-long
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, # pylint: disable=line-too-long
you can get the total voice list : https://help.aliyun.com/zh/model-studio/qwen-tts. # pylint: disable=line-too-long
language_type (str): The synthesized language type, default is 'auto', useful for [qwen3-tts]. # pylint: disable=line-too-long
**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 # pylint: disable=line-too-long
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 = {} # pylint: disable=redefined-builtin
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}) # type: ignore
# 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
upload_certificate = None
for message in messages:
if message.get("role", "") == "user":
content = message["content"]
for elem in content:
if not isinstance(
elem,
(int, float, bool, str, bytes, bytearray),
):
(
is_upload,
upload_certificate,
) = preprocess_message_element(
model,
elem,
api_key,
upload_certificate, # type: ignore[arg-type]
)
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.""" # noqa: E501
# 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
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