# Copyright (c) 2023 Baidu, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from typing import (
Any,
AsyncIterator,
Dict,
Iterator,
List,
Optional,
Union,
)
from qianfan.consts import DefaultLLMModel, DefaultValue
from qianfan.resources.llm.base import (
UNSPECIFIED_MODEL,
BaseResource,
BatchRequestFuture,
)
from qianfan.resources.llm.chat_completion import ChatCompletion
from qianfan.resources.typing import JsonBody, QfLLMInfo, QfResponse
[docs]class Completion(BaseResource):
"""
QianFan Completion is an agent for calling QianFan completion API.
"""
@classmethod
def _supported_models(cls) -> Dict[str, QfLLMInfo]:
"""
preset model list of Completions
Args:
None
Returns:
a dict which key is preset model and value is the endpoint
"""
return {
"ERNIE-Bot-turbo": QfLLMInfo(
endpoint="/chat/eb-instant",
required_keys={"messages"},
optional_keys={
"stream",
"temperature",
"top_p",
"penalty_score",
"user_id",
"tools",
"tool_choice",
"system",
},
),
"ERNIE-Bot": QfLLMInfo(
endpoint="/chat/completions",
required_keys={"messages"},
optional_keys={
"stream",
"temperature",
"top_p",
"penalty_score",
"user_id",
"system",
"stop",
"disable_search",
"enable_citation",
"max_output_tokens",
},
),
"ERNIE-Bot-4": QfLLMInfo(
endpoint="/chat/completions_pro",
required_keys={"messages"},
optional_keys={
"stream",
"temperature",
"top_p",
"penalty_score",
"user_id",
"system",
"stop",
"disable_search",
"enable_citation",
"max_output_tokens",
},
),
"ERNIE-Bot-8k": QfLLMInfo(
endpoint="/chat/ernie_bot_8k",
required_keys={"messages"},
optional_keys={
"functions",
"temperature",
"top_p",
"penalty_score",
"stream",
"system",
"stop",
"disable_search",
"enable_citation",
"user_id",
},
),
"ERNIE-Speed": QfLLMInfo(
endpoint="/chat/ernie_speed",
required_keys={"messages"},
optional_keys={
"stream",
"temperature",
"top_p",
"penalty_score",
"user_id",
"tools",
"tool_choice",
"system",
},
),
"EB-turbo-AppBuilder": QfLLMInfo(
endpoint="/chat/ai_apaas",
required_keys={"messages"},
optional_keys={
"stream",
"temperature",
"top_p",
"penalty_score",
"system",
"user_id",
"tools",
"tool_choice",
},
),
"BLOOMZ-7B": QfLLMInfo(
endpoint="/chat/bloomz_7b1",
required_keys={"messages"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
"Llama-2-7b-chat": QfLLMInfo(
endpoint="/chat/llama_2_7b",
required_keys={"messages"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
"Llama-2-13b-chat": QfLLMInfo(
endpoint="/chat/llama_2_13b",
required_keys={"messages"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
"Llama-2-70b-chat": QfLLMInfo(
endpoint="/chat/llama_2_70b",
required_keys={"messages"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
"Qianfan-BLOOMZ-7B-compressed": QfLLMInfo(
endpoint="/chat/qianfan_bloomz_7b_compressed",
required_keys={"messages"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
"Qianfan-Chinese-Llama-2-7B": QfLLMInfo(
endpoint="/chat/qianfan_chinese_llama_2_7b",
required_keys={"messages"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
"ChatGLM2-6B-32K": QfLLMInfo(
endpoint="/chat/chatglm2_6b_32k",
required_keys={"messages"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
"AquilaChat-7B": QfLLMInfo(
endpoint="/chat/aquilachat_7b",
required_keys={"messages"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
"XuanYuan-70B-Chat-4bit": QfLLMInfo(
endpoint="/chat/xuanyuan_70b_chat",
required_keys={"messages"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
"Qianfan-Chinese-Llama-2-13B": QfLLMInfo(
endpoint="/chat/qianfan_chinese_llama_2_13b",
required_keys={"messages"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
"ChatLaw": QfLLMInfo(
endpoint="/chat/chatlaw",
required_keys={"messages", "extra_parameters"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_p",
"tools",
"tool_choice",
},
),
"SQLCoder-7B": QfLLMInfo(
endpoint="/completions/sqlcoder_7b",
required_keys={"prompt"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
"CodeLlama-7b-Instruct": QfLLMInfo(
endpoint="/completions/codellama_7b_instruct",
required_keys={"prompt"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
"Mixtral-8x7B-Instruct": QfLLMInfo(
endpoint="/chat/mixtral_8x7b_instruct",
required_keys={"messages"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
"Yi-34B-Chat": QfLLMInfo(
endpoint="/chat/yi_34b_chat",
required_keys={"messages"},
optional_keys={
"stream",
"user_id",
"temperature",
"top_k",
"top_p",
"penalty_score",
"stop",
"tools",
"tool_choice",
},
),
UNSPECIFIED_MODEL: QfLLMInfo(
endpoint="",
required_keys={"prompt"},
optional_keys=set(),
),
}
@classmethod
def _default_model(cls) -> str:
"""
default model of Completion: ERNIE-Bot-turbo
Args:
None
Returns:
ERNIE-Bot-turbo
"""
return DefaultLLMModel.Completion
def _generate_body(
self, model: Optional[str], endpoint: str, stream: bool, **kwargs: Any
) -> JsonBody:
"""
generate body
"""
if endpoint[1:].startswith("chat"):
# is using chat to simulate completion
kwargs["messages"] = [{"role": "user", "content": kwargs["prompt"]}]
del kwargs["prompt"]
body = super()._generate_body(model, endpoint, stream, **kwargs)
return body
def _data_postprocess(self, data: QfResponse) -> QfResponse:
"""
to compat with completions api, when using chat completion to mock,
the response needs to be modified
"""
if data.body is not None:
data.body["object"] = "completion"
return super()._data_postprocess(data)
def _convert_endpoint(self, model: Optional[str], endpoint: str) -> str:
"""
convert endpoint to Completion API endpoint
"""
if model is not None and model in ChatCompletion._supported_models():
return ChatCompletion()._convert_endpoint(model, endpoint)
return f"/completions/{endpoint}"
[docs] def do(
self,
prompt: str,
model: Optional[str] = None,
endpoint: Optional[str] = None,
stream: bool = False,
retry_count: int = DefaultValue.RetryCount,
request_timeout: float = DefaultValue.RetryTimeout,
request_id: Optional[str] = None,
backoff_factor: float = DefaultValue.RetryBackoffFactor,
**kwargs: Any,
) -> Union[QfResponse, Iterator[QfResponse]]:
"""
Generate a completion based on the user-provided prompt.
Parameters:
prompt (str):
The input prompt to generate the continuation from.
model (Optional[str]):
The name or identifier of the language model to use. If not specified, the
default model is used(ERNIE-Bot-turbo).
endpoint (Optional[str]):
The endpoint for making API requests. If not provided, the default endpoint
is used.
stream (bool):
If set to True, the responses are streamed back as an iterator. If False, a
single response is returned.
retry_count (int):
The number of times to retry the request in case of failure.
request_timeout (float):
The maximum time (in seconds) to wait for a response from the model.
backoff_factor (float):
A factor to increase the waiting time between retry attempts.
kwargs (Any):
Additional keyword arguments that can be passed to customize the request.
Additional parameters like `temperature` will vary depending on the model,
please refer to the API documentation. The additional parameters can be passed
as follows:
```
Completion().do(prompt = ..., temperature = 0.2, top_p = 0.5)
```
"""
kwargs["prompt"] = prompt
if request_id is not None:
kwargs["request_id"] = request_id
return self._do(
model,
endpoint,
stream,
retry_count,
request_timeout,
backoff_factor,
**kwargs,
)
[docs] async def ado(
self,
prompt: str,
model: Optional[str] = None,
endpoint: Optional[str] = None,
stream: bool = False,
retry_count: int = DefaultValue.RetryCount,
request_timeout: float = DefaultValue.RetryTimeout,
request_id: Optional[str] = None,
backoff_factor: float = DefaultValue.RetryBackoffFactor,
**kwargs: Any,
) -> Union[QfResponse, AsyncIterator[QfResponse]]:
"""
Async generate a completion based on the user-provided prompt.
Parameters:
prompt (str):
The input prompt to generate the continuation from.
model (Optional[str]):
The name or identifier of the language model to use. If not specified, the
default model is used(ERNIE-Bot-turbo).
endpoint (Optional[str]):
The endpoint for making API requests. If not provided, the default endpoint
is used.
stream (bool):
If set to True, the responses are streamed back as an iterator. If False, a
single response is returned.
retry_count (int):
The number of times to retry the request in case of failure.
request_timeout (float):
The maximum time (in seconds) to wait for a response from the model.
backoff_factor (float):
A factor to increase the waiting time between retry attempts.
kwargs (Any):
Additional keyword arguments that can be passed to customize the request.
Additional parameters like `temperature` will vary depending on the model,
please refer to the API documentation. The additional parameters can be passed
as follows:
```
Completion().do(prompt = ..., temperature = 0.2, top_p = 0.5)
```
"""
kwargs["prompt"] = prompt
if request_id is not None:
kwargs["request_id"] = request_id
return await self._ado(
model,
endpoint,
stream,
retry_count,
request_timeout,
backoff_factor,
**kwargs,
)
[docs] def batch_do(
self,
prompt_list: List[str],
worker_num: Optional[int] = None,
**kwargs: Any,
) -> BatchRequestFuture:
"""
Batch generate a completion based on the user-provided prompt.
Parameters:
prompt_list (List[str]):
The input prompt list to generate the continuation from.
worker_num (Optional[int]):
The number of prompts to process at the same time, default to None,
which means this number will be decided dynamically.
kwargs (Any):
Please refer to `Completion.do` for other parameters such as `model`,
`endpoint`, `retry_count`, etc.
```
response_list = Completion().batch_do(["...", "..."], worker_num = 10)
for response in response_list:
# return QfResponse if succeed, or exception will be raised
print(response.result())
# or
while response_list.finished_count() != response_list.task_count():
time.sleep(1)
print(response_list.results())
```
"""
task_list = [
partial(self.do, prompt=prompt, **kwargs) for prompt in prompt_list
]
return self._batch_request(task_list, worker_num)
[docs] async def abatch_do(
self,
prompt_list: List[str],
worker_num: Optional[int] = None,
**kwargs: Any,
) -> List[Union[QfResponse, AsyncIterator[QfResponse]]]:
"""
Async batch generate a completion based on the user-provided prompt.
Parameters:
prompt_list (List[str]):
The input prompt list to generate the continuation from.
worker_num (Optional[int]):
The number of prompts to process at the same time, default to None,
which means this number will be decided dynamically.
kwargs (Any):
Please refer to `Completion.ado` for other parameters such as `model`,
`endpoint`, `retry_count`, etc.
```
response_list = await Completion().abatch_do([...], worker_num = 10)
for response in response_list:
# response is `QfResponse` if succeed, or response will be exception
print(response)
```
"""
tasks = [self.ado(prompt=prompt, **kwargs) for prompt in prompt_list]
return await self._abatch_request(tasks, worker_num)