Add files using upload-large-folder tool

cbc00ce verified 6 months ago

7.42 kB

# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.

from dataclasses import dataclass
from typing import List

from dashscope.api_entities.dashscope_response import (
    DashScopeAPIResponse,
    DictMixin,
)
from dashscope.client.base_api import BaseApi, BaseAioApi
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


@dataclass(init=False)
class MultiModalEmbeddingItemBase(DictMixin):
    factor: float

    def __init__(self, factor: float, **kwargs):
        super().__init__(factor=factor, **kwargs)


@dataclass(init=False)
class MultiModalEmbeddingItemText(MultiModalEmbeddingItemBase):
    text: str

    def __init__(self, text: str, factor: float, **kwargs):
        super().__init__(factor, **kwargs)
        self.text = text


@dataclass(init=False)
class MultiModalEmbeddingItemImage(MultiModalEmbeddingItemBase):
    image: str

    def __init__(self, image: str, factor: float, **kwargs):
        super().__init__(factor, **kwargs)
        self.image = image


@dataclass(init=False)
class MultiModalEmbeddingItemAudio(MultiModalEmbeddingItemBase):
    audio: str

    def __init__(self, audio: str, factor: float, **kwargs):
        super().__init__(factor, **kwargs)
        self.audio = audio


class MultiModalEmbedding(BaseApi):
    task = "multimodal-embedding"

    class Models:
        multimodal_embedding_one_peace_v1 = "multimodal-embedding-one-peace-v1"

    @classmethod
    def call(  # type: ignore[override]
        cls,
        model: str,
        # pylint: disable=redefined-builtin
        input: List[MultiModalEmbeddingItemBase],
        api_key: str = None,
        workspace: str = None,
        **kwargs,
    ) -> DashScopeAPIResponse:
        """Get embedding multimodal contents..
        Args:
            model (str): The embedding model name.
            input (List[MultiModalEmbeddingElement]): The embedding
                elements, every element include data, modal, factor field.
            workspace (str): The dashscope workspace id.
            **kwargs:
                auto_truncation(bool, `optional`): Automatically truncate
                audio longer than 15 seconds or text longer than 70 words.
                Default to false(Too long input will result in failure).
        Returns:
            DashScopeAPIResponse: The embedding result.
        """
        if input is None or not input:
            raise InputRequired("prompt is required!")
        if model is None or not model:
            raise ModelRequired("Model is required!")
        embedding_input = {}
        has_upload = cls._preprocess_message_inputs(
            model,
            input,  # type: ignore[arg-type]
            api_key,
        )  # noqa: E501
        if has_upload:
            headers = kwargs.pop("headers", {})
            headers["X-DashScope-OssResourceResolve"] = "enable"
            kwargs["headers"] = headers
        embedding_input["contents"] = input
        kwargs.pop("stream", False)  # not support streaming output.
        task_group, function = _get_task_group_and_task(__name__)
        return super().call(
            model=model,
            input=embedding_input,
            task_group=task_group,
            task=MultiModalEmbedding.task,
            function=function,
            api_key=api_key,
            workspace=workspace,
            **kwargs,
        )

    @classmethod
    def _preprocess_message_inputs(
        cls,
        model: str,
        input_data: List[dict],
        api_key: str,
    ):
        """preprocess following inputs
        input = [{'factor': 1, 'text': 'hello'},
                {'factor': 2, 'audio': ''},
                {'factor': 3, 'image': ''}]
        """
        has_upload = False
        upload_certificate = None
        for elem in input_data:
            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


class AioMultiModalEmbedding(BaseAioApi):
    task = "multimodal-embedding"

    class Models:
        multimodal_embedding_one_peace_v1 = "multimodal-embedding-one-peace-v1"

    @classmethod
    async def call(  # type: ignore[override]
        cls,
        model: str,
        # pylint: disable=redefined-builtin
        input: List[MultiModalEmbeddingItemBase],
        api_key: str = None,
        workspace: str = None,
        **kwargs,
    ) -> DashScopeAPIResponse:
        """Get embedding multimodal contents..
        Args:
            model (str): The embedding model name.
            input (List[MultiModalEmbeddingElement]): The embedding
                elements, every element include data, modal, factor field.
            workspace (str): The dashscope workspace id.
            **kwargs:
                auto_truncation(bool, `optional`): Automatically truncate
                audio longer than 15 seconds or text longer than 70 words.
                Default to false(Too long input will result in failure).
        Returns:
            DashScopeAPIResponse: The embedding result.
        """
        if input is None or not input:
            raise InputRequired("prompt is required!")
        if model is None or not model:
            raise ModelRequired("Model is required!")
        embedding_input = {}
        has_upload = cls._preprocess_message_inputs(
            model,
            input,  # type: ignore[arg-type]
            api_key,
        )  # noqa: E501
        if has_upload:
            headers = kwargs.pop("headers", {})
            headers["X-DashScope-OssResourceResolve"] = "enable"
            kwargs["headers"] = headers
        embedding_input["contents"] = input
        kwargs.pop("stream", False)  # not support streaming output.
        task_group, function = _get_task_group_and_task(__name__)
        response = await super().call(
            model=model,
            input=embedding_input,
            task_group=task_group,
            task=MultiModalEmbedding.task,
            function=function,
            api_key=api_key,
            workspace=workspace,
            **kwargs,
        )
        return response

    @classmethod
    def _preprocess_message_inputs(
        cls,
        model: str,
        input_data: List[dict],
        api_key: str,
    ):
        """preprocess following inputs
        input = [{'factor': 1, 'text': 'hello'},
                {'factor': 2, 'audio': ''},
                {'factor': 3, 'image': ''}]
        """
        has_upload = False
        upload_certificate = None
        for elem in input_data:
            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

	# -- coding: utf-8 --
	# Copyright (c) Alibaba, Inc. and its affiliates.

	from dataclasses import dataclass
	from typing import List

	from dashscope.api_entities.dashscope_response import (
	DashScopeAPIResponse,
	DictMixin,
	)
	from dashscope.client.base_api import BaseApi, BaseAioApi
	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


	@dataclass(init=False)
	class MultiModalEmbeddingItemBase(DictMixin):
	factor: float

	def __init__(self, factor: float, **kwargs):
	super().__init__(factor=factor, **kwargs)


	@dataclass(init=False)
	class MultiModalEmbeddingItemText(MultiModalEmbeddingItemBase):
	text: str

	def __init__(self, text: str, factor: float, **kwargs):
	super().__init__(factor, **kwargs)
	self.text = text


	@dataclass(init=False)
	class MultiModalEmbeddingItemImage(MultiModalEmbeddingItemBase):
	image: str

	def __init__(self, image: str, factor: float, **kwargs):
	super().__init__(factor, **kwargs)
	self.image = image


	@dataclass(init=False)
	class MultiModalEmbeddingItemAudio(MultiModalEmbeddingItemBase):
	audio: str

	def __init__(self, audio: str, factor: float, **kwargs):
	super().__init__(factor, **kwargs)
	self.audio = audio


	class MultiModalEmbedding(BaseApi):
	task = "multimodal-embedding"

	class Models:
	multimodal_embedding_one_peace_v1 = "multimodal-embedding-one-peace-v1"

	@classmethod
	def call( # type: ignore[override]
	cls,
	model: str,
	# pylint: disable=redefined-builtin
	input: List[MultiModalEmbeddingItemBase],
	api_key: str = None,
	workspace: str = None,
	**kwargs,
	) -> DashScopeAPIResponse:
	"""Get embedding multimodal contents..

	Args:
	model (str): The embedding model name.
	input (List[MultiModalEmbeddingElement]): The embedding
	elements, every element include data, modal, factor field.
	workspace (str): The dashscope workspace id.
	**kwargs:
	auto_truncation(bool, `optional`): Automatically truncate
	audio longer than 15 seconds or text longer than 70 words.
	Default to false(Too long input will result in failure).

	Returns:
	DashScopeAPIResponse: The embedding result.
	"""
	if input is None or not input:
	raise InputRequired("prompt is required!")
	if model is None or not model:
	raise ModelRequired("Model is required!")
	embedding_input = {}
	has_upload = cls._preprocess_message_inputs(
	model,
	input, # type: ignore[arg-type]
	api_key,
	) # noqa: E501
	if has_upload:
	headers = kwargs.pop("headers", {})
	headers["X-DashScope-OssResourceResolve"] = "enable"
	kwargs["headers"] = headers
	embedding_input["contents"] = input
	kwargs.pop("stream", False) # not support streaming output.
	task_group, function = _get_task_group_and_task(__name__)
	return super().call(
	model=model,
	input=embedding_input,
	task_group=task_group,
	task=MultiModalEmbedding.task,
	function=function,
	api_key=api_key,
	workspace=workspace,
	**kwargs,
	)

	@classmethod
	def _preprocess_message_inputs(
	cls,
	model: str,
	input_data: List[dict],
	api_key: str,
	):
	"""preprocess following inputs
	input = [{'factor': 1, 'text': 'hello'},
	{'factor': 2, 'audio': ''},
	{'factor': 3, 'image': ''}]
	"""
	has_upload = False
	upload_certificate = None
	for elem in input_data:
	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


	class AioMultiModalEmbedding(BaseAioApi):
	task = "multimodal-embedding"

	class Models:
	multimodal_embedding_one_peace_v1 = "multimodal-embedding-one-peace-v1"

	@classmethod
	async def call( # type: ignore[override]
	cls,
	model: str,
	# pylint: disable=redefined-builtin
	input: List[MultiModalEmbeddingItemBase],
	api_key: str = None,
	workspace: str = None,
	**kwargs,
	) -> DashScopeAPIResponse:
	"""Get embedding multimodal contents..

	Args:
	model (str): The embedding model name.
	input (List[MultiModalEmbeddingElement]): The embedding
	elements, every element include data, modal, factor field.
	workspace (str): The dashscope workspace id.
	**kwargs:
	auto_truncation(bool, `optional`): Automatically truncate
	audio longer than 15 seconds or text longer than 70 words.
	Default to false(Too long input will result in failure).

	Returns:
	DashScopeAPIResponse: The embedding result.
	"""
	if input is None or not input:
	raise InputRequired("prompt is required!")
	if model is None or not model:
	raise ModelRequired("Model is required!")
	embedding_input = {}
	has_upload = cls._preprocess_message_inputs(
	model,
	input, # type: ignore[arg-type]
	api_key,
	) # noqa: E501
	if has_upload:
	headers = kwargs.pop("headers", {})
	headers["X-DashScope-OssResourceResolve"] = "enable"
	kwargs["headers"] = headers
	embedding_input["contents"] = input
	kwargs.pop("stream", False) # not support streaming output.
	task_group, function = _get_task_group_and_task(__name__)
	response = await super().call(
	model=model,
	input=embedding_input,
	task_group=task_group,
	task=MultiModalEmbedding.task,
	function=function,
	api_key=api_key,
	workspace=workspace,
	**kwargs,
	)
	return response

	@classmethod
	def _preprocess_message_inputs(
	cls,
	model: str,
	input_data: List[dict],
	api_key: str,
	):
	"""preprocess following inputs
	input = [{'factor': 1, 'text': 'hello'},
	{'factor': 2, 'audio': ''},
	{'factor': 3, 'image': ''}]
	"""
	has_upload = False
	upload_certificate = None
	for elem in input_data:
	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