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from typing import Optional, Tuple, Union
import litellm
from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
from litellm.secret_managers.main import get_secret_str
class DeepInfraConfig(OpenAIGPTConfig):
"""
Reference: https://deepinfra.com/docs/advanced/openai_api
The class `DeepInfra` provides configuration for the DeepInfra's Chat Completions API interface. Below are the parameters:
"""
frequency_penalty: Optional[int] = None
function_call: Optional[Union[str, dict]] = None
functions: Optional[list] = None
logit_bias: Optional[dict] = None
max_tokens: Optional[int] = None
n: Optional[int] = None
presence_penalty: Optional[int] = None
stop: Optional[Union[str, list]] = None
temperature: Optional[int] = None
top_p: Optional[int] = None
response_format: Optional[dict] = None
tools: Optional[list] = None
tool_choice: Optional[Union[str, dict]] = None
def __init__(
self,
frequency_penalty: Optional[int] = None,
function_call: Optional[Union[str, dict]] = None,
functions: Optional[list] = None,
logit_bias: Optional[dict] = None,
max_tokens: Optional[int] = None,
n: Optional[int] = None,
presence_penalty: Optional[int] = None,
stop: Optional[Union[str, list]] = None,
temperature: Optional[int] = None,
top_p: Optional[int] = None,
response_format: Optional[dict] = None,
tools: Optional[list] = None,
tool_choice: Optional[Union[str, dict]] = None,
) -> None:
locals_ = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@classmethod
def get_config(cls):
return super().get_config()
def get_supported_openai_params(self, model: str):
return [
"stream",
"frequency_penalty",
"function_call",
"functions",
"logit_bias",
"max_tokens",
"max_completion_tokens",
"n",
"presence_penalty",
"stop",
"temperature",
"top_p",
"response_format",
"tools",
"tool_choice",
]
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool,
) -> dict:
supported_openai_params = self.get_supported_openai_params(model=model)
for param, value in non_default_params.items():
if (
param == "temperature"
and value == 0
and model == "mistralai/Mistral-7B-Instruct-v0.1"
): # this model does no support temperature == 0
value = 0.0001 # close to 0
if param == "tool_choice":
if (
value != "auto" and value != "none"
): # https://deepinfra.com/docs/advanced/function_calling
## UNSUPPORTED TOOL CHOICE VALUE
if litellm.drop_params is True or drop_params is True:
value = None
else:
raise litellm.utils.UnsupportedParamsError(
message="Deepinfra doesn't support tool_choice={}. To drop unsupported openai params from the call, set `litellm.drop_params = True`".format(
value
),
status_code=400,
)
elif param == "max_completion_tokens":
optional_params["max_tokens"] = value
elif param in supported_openai_params:
if value is not None:
optional_params[param] = value
return optional_params
def _get_openai_compatible_provider_info(
self, api_base: Optional[str], api_key: Optional[str]
) -> Tuple[Optional[str], Optional[str]]:
# deepinfra is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.endpoints.anyscale.com/v1
api_base = (
api_base
or get_secret_str("DEEPINFRA_API_BASE")
or "https://api.deepinfra.com/v1/openai"
)
dynamic_api_key = api_key or get_secret_str("DEEPINFRA_API_KEY")
return api_base, dynamic_api_key
|
