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# 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 # pylint: disable=line-too-long
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 # pylint: disable=line-too-long
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 # pylint: disable=line-too-long
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 # pylint: disable=line-too-long
noted that the length generated by the model will only be less than max_tokens, # noqa E501 # pylint: disable=line-too-long
not necessarily equal to it. If max_tokens is set too large, the service will # noqa E501 # pylint: disable=line-too-long
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 # pylint: disable=line-too-long
Increasing repetition_penalty can reduce the duplication of model generation. # noqa E501 # pylint: disable=line-too-long
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 # type: ignore[index]
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
# type: ignore[override]
async def call( # pylint: disable=arguments-renamed # type: ignore[override] # noqa: E501
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 # pylint: disable=line-too-long
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 # pylint: disable=line-too-long
For example, when the value is 50, only the 50 highest-scoring tokens # noqa E501 # pylint: disable=line-too-long
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 # pylint: disable=line-too-long
If true, the subsequent output will include the previously input content. # noqa E501 # pylint: disable=line-too-long
Otherwise, the subsequent output will not include the previously output # noqa E501 # pylint: disable=line-too-long
content. Default false.
stop(list[str] or list[list[int]], `optional`): Used to control the generation to stop # noqa E501 # pylint: disable=line-too-long
when encountering setting str or token ids, the result will not include # noqa E501 # pylint: disable=line-too-long
stop words or tokens.
max_tokens(int, `optional`): The maximum token num expected to be output. It should be # noqa E501 # pylint: disable=line-too-long
noted that the length generated by the model will only be less than max_tokens, # noqa E501 # pylint: disable=line-too-long
not necessarily equal to it. If max_tokens is set too large, the service will # noqa E501 # pylint: disable=line-too-long
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 # pylint: disable=line-too-long
Increasing repetition_penalty can reduce the duplication of model generation. # noqa E501 # pylint: disable=line-too-long
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
# pylint: disable=protected-access
(
input, # pylint: disable=redefined-builtin
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 ( # type: ignore[return-value]
GenerationResponse.from_api_response(rsp)
async for rsp in response
)
else:
return GenerationResponse.from_api_response(response)
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