litellm-mirror/litellm/llms/base_llm/chat/transformation.py

"""
Common base config for all LLM providers
"""

import types
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, List, Optional, Union

import httpx

from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import ModelResponse

if TYPE_CHECKING:
    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj

    LiteLLMLoggingObj = _LiteLLMLoggingObj
else:
    LiteLLMLoggingObj = Any


class BaseLLMException(Exception):
    def __init__(
        self,
        status_code: int,
        message: str,
        headers: Optional[Union[dict, httpx.Headers]] = None,
        request: Optional[httpx.Request] = None,
        response: Optional[httpx.Response] = None,
    ):
        self.status_code = status_code
        self.message: str = message
        self.headers = headers
        if request:
            self.request = request
        else:
            self.request = httpx.Request(
                method="POST", url="https://docs.litellm.ai/docs"
            )
        if response:
            self.response = response
        else:
            self.response = httpx.Response(
                status_code=status_code, request=self.request
            )
        super().__init__(
            self.message
        )  # Call the base class constructor with the parameters it needs


class BaseConfig(ABC):
    def __init__(self):
        pass

    @classmethod
    def get_config(cls):
        return {
            k: v
            for k, v in cls.__dict__.items()
            if not k.startswith("__")
            and not k.startswith("_abc")
            and not isinstance(
                v,
                (
                    types.FunctionType,
                    types.BuiltinFunctionType,
                    classmethod,
                    staticmethod,
                ),
            )
            and v is not None
        }

    def should_fake_stream(
        self,
        model: Optional[str],
        stream: Optional[bool],
        custom_llm_provider: Optional[str] = None,
    ) -> bool:
        """
        Returns True if the model/provider should fake stream
        """
        return False

    def should_retry_llm_api_inside_llm_translation_on_http_error(
        self, e: httpx.HTTPStatusError, litellm_params: dict
    ) -> bool:
        """
        Returns True if the model/provider should retry the LLM API on UnprocessableEntityError

        Overriden by azure ai - where different models support different parameters
        """
        return False

    def transform_request_on_unprocessable_entity_error(
        self, e: httpx.HTTPStatusError, request_data: dict
    ) -> dict:
        """
        Transform the request data on UnprocessableEntityError
        """
        return request_data

    @property
    def max_retry_on_unprocessable_entity_error(self) -> int:
        """
        Returns the max retry count for UnprocessableEntityError

        Used if `should_retry_llm_api_inside_llm_translation_on_http_error` is True
        """
        return 0

    @abstractmethod
    def get_supported_openai_params(self, model: str) -> list:
        pass

    @abstractmethod
    def map_openai_params(
        self,
        non_default_params: dict,
        optional_params: dict,
        model: str,
        drop_params: bool,
    ) -> dict:
        pass

    @abstractmethod
    def validate_environment(
        self,
        headers: dict,
        model: str,
        messages: List[AllMessageValues],
        optional_params: dict,
        api_key: Optional[str] = None,
        api_base: Optional[str] = None,
    ) -> dict:
        pass

    def get_complete_url(
        self,
        api_base: str,
        model: str,
        optional_params: dict,
        stream: Optional[bool] = None,
    ) -> str:
        """
        OPTIONAL

        Get the complete url for the request

        Some providers need `model` in `api_base`
        """
        return api_base

    @abstractmethod
    def transform_request(
        self,
        model: str,
        messages: List[AllMessageValues],
        optional_params: dict,
        litellm_params: dict,
        headers: dict,
    ) -> dict:
        pass

    @abstractmethod
    def transform_response(
        self,
        model: str,
        raw_response: httpx.Response,
        model_response: ModelResponse,
        logging_obj: LiteLLMLoggingObj,
        request_data: dict,
        messages: List[AllMessageValues],
        optional_params: dict,
        litellm_params: dict,
        encoding: Any,
        api_key: Optional[str] = None,
        json_mode: Optional[bool] = None,
    ) -> ModelResponse:
        pass

    @abstractmethod
    def get_error_class(
        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
    ) -> BaseLLMException:
        pass

    def get_model_response_iterator(
        self,
        streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
        sync_stream: bool,
        json_mode: Optional[bool] = False,
    ) -> Any:
        pass