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# Source code for langchain_community.llms.cerebriumai

    import logging
    from typing import Any, Dict, List, Mapping, Optional, cast

    import requests
    from langchain_core.callbacks import CallbackManagerForLLMRun
    from langchain_core.language_models.llms import LLM
    from langchain_core.pydantic_v1 import Extra, Field, SecretStr, root_validator
    from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

    from langchain_community.llms.utils import enforce_stop_tokens

    logger = logging.getLogger(__name__)

    class CerebriumAI(LLM):
        """CerebriumAI large language models.

        To use, you should have the ``cerebrium`` python package installed.
        You should also have the environment variable ``CEREBRIUMAI_API_KEY``
        set with your API key or pass it as a named argument in the constructor.

        Any parameters that are valid to be passed to the call can be passed
        in, even if not explicitly saved on this class.

        Example:
            .. code-block:: python

                from langchain_community.llms import CerebriumAI
                cerebrium = CerebriumAI(endpoint_url="", cerebriumai_api_key="my-api-key")

        """

        endpoint_url: str = ""
        """model endpoint to use"""

        model_kwargs: Dict[str, Any] = Field(default_factory=dict)
        """Holds any model parameters valid for `create` call not
        explicitly specified."""

        cerebriumai_api_key: Optional[SecretStr] = None

        class Config:
            """Configuration for this pydantic config."""

            extra = Extra.forbid

        @root_validator(pre=True)
        def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
            """Build extra kwargs from additional params that were passed in."""
            all_required_field_names = {field.alias for field in cls.__fields__.values()}

            extra = values.get("model_kwargs", {})
            for field_name in list(values):
                if field_name not in all_required_field_names:
                    if field_name in extra:
                        raise ValueError(f"Found {field_name} supplied twice.")
                    logger.warning(
                        f"""{field_name} was transferred to model_kwargs.
                        Please confirm that {field_name} is what you intended."""
                    )
                    extra[field_name] = values.pop(field_name)
            values["model_kwargs"] = extra
            return values

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate that api key and python package exists in environment."""
            cerebriumai_api_key = convert_to_secret_str(
                get_from_dict_or_env(values, "cerebriumai_api_key", "CEREBRIUMAI_API_KEY")
            )
            values["cerebriumai_api_key"] = cerebriumai_api_key
            return values

        @property
        def _identifying_params(self) -> Mapping[str, Any]:
            """Get the identifying parameters."""
            return {
                **{"endpoint_url": self.endpoint_url},
                **{"model_kwargs": self.model_kwargs},
            }

        @property
        def _llm_type(self) -> str:
            """Return type of llm."""
            return "cerebriumai"

        def _call(
            self,
            prompt: str,
            stop: Optional[List[str]] = None,
            run_manager: Optional[CallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> str:
            headers: Dict = {
                "Authorization": cast(
                    SecretStr, self.cerebriumai_api_key
                ).get_secret_value(),
                "Content-Type": "application/json",
            }
            params = self.model_kwargs or {}
            payload = {"prompt": prompt, **params, **kwargs}
            response = requests.post(self.endpoint_url, json=payload, headers=headers)
            if response.status_code == 200:
                data = response.json()
                text = data["result"]
                if stop is not None:
                    # I believe this is required since the stop tokens
                    # are not enforced by the model parameters
                    text = enforce_stop_tokens(text, stop)
                return text
            else:
                response.raise_for_status()
            return ""