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