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# Source code for langchain_community.llms.pipelineai

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

    from langchain_core.callbacks import CallbackManagerForLLMRun
    from langchain_core.language_models.llms import LLM
    from langchain_core.pydantic_v1 import (
        BaseModel,
        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 PipelineAI(LLM, BaseModel):
        """PipelineAI large language models.

        To use, you should have the ``pipeline-ai`` python package installed,
        and the environment variable ``PIPELINE_API_KEY`` set with your API key.

        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 PipelineAI
                pipeline = PipelineAI(pipeline_key="")
        """

        pipeline_key: str = ""
        """The id or tag of the target pipeline"""

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

        pipeline_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("pipeline_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 pipeline_kwargs.
                        Please confirm that {field_name} is what you intended."""
                    )
                    extra[field_name] = values.pop(field_name)
            values["pipeline_kwargs"] = extra
            return values

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

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

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

        def _call(
            self,
            prompt: str,
            stop: Optional[List[str]] = None,
            run_manager: Optional[CallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> str:
            """Call to Pipeline Cloud endpoint."""
            try:
                from pipeline import PipelineCloud
            except ImportError:
                raise ImportError(
                    "Could not import pipeline-ai python package. "
                    "Please install it with `pip install pipeline-ai`."
                )
            client = PipelineCloud(token=self.pipeline_api_key.get_secret_value())  # type: ignore[union-attr]
            params = self.pipeline_kwargs or {}
            params = {**params, **kwargs}

            run = client.run_pipeline(self.pipeline_key, [prompt, params])
            try:
                text = run.result_preview[0][0]
            except AttributeError:
                raise AttributeError(
                    f"A pipeline run should have a `result_preview` attribute."
                    f"Run was: {run}"
                )
            if stop is not None:
                # I believe this is required since the stop tokens
                # are not enforced by the pipeline parameters
                text = enforce_stop_tokens(text, stop)
            return text