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# 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
# type: ignore[override]
def call( # pylint: disable=arguments-renamed # type: ignore[override]
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
(
input, # pylint: disable=redefined-builtin
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 = {} # pylint: disable=redefined-builtin
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(
f"customized_model_id is required for {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.""" # noqa: E501
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"
# type: ignore[override]
@classmethod
async def call( # type: ignore[override] # pylint: disable=arguments-renamed # noqa: E501
# type: ignore[override]
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 # 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,
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
# pylint: disable=protected-access
(
input, # pylint: disable=redefined-builtin
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
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