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# http://www.apache.org/licenses/LICENSE-2.0
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# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
from __future__ import annotations
from typing import Any, Dict, Optional, Sequence, Union
from openai._types import NOT_GIVEN, NotGiven
from pydantic import Field
from camel.configs.base_config import BaseConfig
[docs]
class SambaFastAPIConfig(BaseConfig):
r"""Defines the parameters for generating chat completions using the
SambaNova Fast API.
Args:
max_tokens (Optional[int], optional): the maximum number of tokens to
generate, e.g. 100.
(default: :obj:`2048`)
stop (Optional[Union[str,list[str]]]): Stop generation if this token
is detected. Or if one of these tokens is detected when providing
a string list.
(default: :obj:`None`)
stream (Optional[bool]): If True, partial message deltas will be sent
as data-only server-sent events as they become available.
Currently SambaNova Fast API only support stream mode.
(default: :obj:`True`)
stream_options (Optional[Dict]): Additional options for streaming.
(default: :obj:`{"include_usage": True}`)
"""
max_tokens: Optional[int] = 2048
stop: Optional[Union[str, list[str]]] = None
stream: Optional[bool] = True
stream_options: Optional[Dict] = {"include_usage": True} # noqa: RUF012
[docs]
def as_dict(self) -> dict[str, Any]:
config_dict = super().as_dict()
if "tools" in config_dict:
del config_dict["tools"] # SambaNova does not support tool calling
return config_dict
SAMBA_FAST_API_PARAMS = {
param for param in SambaFastAPIConfig().model_fields.keys()
}
[docs]
class SambaVerseAPIConfig(BaseConfig):
r"""Defines the parameters for generating chat completions using the
SambaVerse API.
Args:
temperature (float, optional): Sampling temperature to use, between
:obj:`0` and :obj:`2`. Higher values make the output more random,
while lower values make it more focused and deterministic.
(default: :obj:`0.7`)
top_p (float, optional): An alternative to sampling with temperature,
called nucleus sampling, where the model considers the results of
the tokens with top_p probability mass. So :obj:`0.1` means only
the tokens comprising the top 10% probability mass are considered.
(default: :obj:`0.95`)
top_k (int, optional): Only sample from the top K options for each
subsequent token. Used to remove "long tail" low probability
responses.
(default: :obj:`50`)
max_tokens (Optional[int], optional): The maximum number of tokens to
generate, e.g. 100.
(default: :obj:`2048`)
repetition_penalty (Optional[float], optional): The parameter for
repetition penalty. 1.0 means no penalty.
(default: :obj:`1.0`)
stop (Optional[Union[str,list[str]]]): Stop generation if this token
is detected. Or if one of these tokens is detected when providing
a string list.
(default: :obj:`""`)
stream (Optional[bool]): If True, partial message deltas will be sent
as data-only server-sent events as they become available.
Currently SambaVerse API doesn't support stream mode.
(default: :obj:`False`)
"""
temperature: Optional[float] = 0.7
top_p: Optional[float] = 0.95
top_k: Optional[int] = 50
max_tokens: Optional[int] = 2048
repetition_penalty: Optional[float] = 1.0
stop: Optional[Union[str, list[str]]] = ""
stream: Optional[bool] = False
[docs]
def as_dict(self) -> dict[str, Any]:
config_dict = super().as_dict()
if "tools" in config_dict:
del config_dict["tools"] # SambaNova does not support tool calling
return config_dict
SAMBA_VERSE_API_PARAMS = {
param for param in SambaVerseAPIConfig().model_fields.keys()
}
[docs]
class SambaCloudAPIConfig(BaseConfig):
r"""Defines the parameters for generating chat completions using the
OpenAI API.
Args:
temperature (float, optional): Sampling temperature to use, between
:obj:`0` and :obj:`2`. Higher values make the output more random,
while lower values make it more focused and deterministic.
(default: :obj:`0.2`)
top_p (float, optional): An alternative to sampling with temperature,
called nucleus sampling, where the model considers the results of
the tokens with top_p probability mass. So :obj:`0.1` means only
the tokens comprising the top 10% probability mass are considered.
(default: :obj:`1.0`)
n (int, optional): How many chat completion choices to generate for
each input message. (default: :obj:`1`)
response_format (object, optional): An object specifying the format
that the model must output. Compatible with GPT-4 Turbo and all
GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106. Setting to
{"type": "json_object"} enables JSON mode, which guarantees the
message the model generates is valid JSON. Important: when using
JSON mode, you must also instruct the model to produce JSON
yourself via a system or user message. Without this, the model
may generate an unending stream of whitespace until the generation
reaches the token limit, resulting in a long-running and seemingly
"stuck" request. Also note that the message content may be
partially cut off if finish_reason="length", which indicates the
generation exceeded max_tokens or the conversation exceeded the
max context length.
stream (bool, optional): If True, partial message deltas will be sent
as data-only server-sent events as they become available.
(default: :obj:`False`)
stop (str or list, optional): Up to :obj:`4` sequences where the API
will stop generating further tokens. (default: :obj:`None`)
max_tokens (int, optional): The maximum number of tokens to generate
in the chat completion. The total length of input tokens and
generated tokens is limited by the model's context length.
(default: :obj:`None`)
presence_penalty (float, optional): Number between :obj:`-2.0` and
:obj:`2.0`. Positive values penalize new tokens based on whether
they appear in the text so far, increasing the model's likelihood
to talk about new topics. See more information about frequency and
presence penalties. (default: :obj:`0.0`)
frequency_penalty (float, optional): Number between :obj:`-2.0` and
:obj:`2.0`. Positive values penalize new tokens based on their
existing frequency in the text so far, decreasing the model's
likelihood to repeat the same line verbatim. See more information
about frequency and presence penalties. (default: :obj:`0.0`)
logit_bias (dict, optional): Modify the likelihood of specified tokens
appearing in the completion. Accepts a json object that maps tokens
(specified by their token ID in the tokenizer) to an associated
bias value from :obj:`-100` to :obj:`100`. Mathematically, the bias
is added to the logits generated by the model prior to sampling.
The exact effect will vary per model, but values between:obj:` -1`
and :obj:`1` should decrease or increase likelihood of selection;
values like :obj:`-100` or :obj:`100` should result in a ban or
exclusive selection of the relevant token. (default: :obj:`{}`)
user (str, optional): A unique identifier representing your end-user,
which can help OpenAI to monitor and detect abuse.
(default: :obj:`""`)
tools (list[OpenAIFunction], optional): A list of tools the model may
call. Currently, only functions are supported as a tool. Use this
to provide a list of functions the model may generate JSON inputs
for. A max of 128 functions are supported.
tool_choice (Union[dict[str, str], str], optional): Controls which (if
any) tool is called by the model. :obj:`"none"` means the model
will not call any tool and instead generates a message.
:obj:`"auto"` means the model can pick between generating a
message or calling one or more tools. :obj:`"required"` means the
model must call one or more tools. Specifying a particular tool
via {"type": "function", "function": {"name": "my_function"}}
forces the model to call that tool. :obj:`"none"` is the default
when no tools are present. :obj:`"auto"` is the default if tools
are present.
"""
temperature: float = 0.2 # openai default: 1.0
top_p: float = 1.0
n: int = 1
stream: bool = False
stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
max_tokens: Union[int, NotGiven] = NOT_GIVEN
presence_penalty: float = 0.0
response_format: Union[dict, NotGiven] = NOT_GIVEN
frequency_penalty: float = 0.0
logit_bias: dict = Field(default_factory=dict)
user: str = ""
tool_choice: Optional[Union[dict[str, str], str]] = None
SAMBA_CLOUD_API_PARAMS = {
param for param in SambaCloudAPIConfig().model_fields.keys()
}
