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# Source code for langchain_community.llms.baidu_qianfan_endpoint

    from __future__ import annotations

    import logging
    from typing import (
        Any,
        AsyncIterator,
        Dict,
        Iterator,
        List,
        Optional,
    )

    from langchain_core.callbacks import (
        AsyncCallbackManagerForLLMRun,
        CallbackManagerForLLMRun,
    )
    from langchain_core.language_models.llms import LLM
    from langchain_core.outputs import GenerationChunk
    from langchain_core.pydantic_v1 import Field, root_validator
    from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

    logger = logging.getLogger(__name__)

    class QianfanLLMEndpoint(LLM):
        """Baidu Qianfan hosted open source or customized models.

        To use, you should have the ``qianfan`` python package installed, and
        the environment variable ``qianfan_ak`` and ``qianfan_sk`` set with
        your API key and Secret Key.

        ak, sk are required parameters which you could get from
        https://cloud.baidu.com/product/wenxinworkshop

        Example:
            .. code-block:: python

                from langchain_community.llms import QianfanLLMEndpoint
                qianfan_model = QianfanLLMEndpoint(model="ERNIE-Bot",
                    endpoint="your_endpoint", qianfan_ak="your_ak", qianfan_sk="your_sk")
        """

        init_kwargs: Dict[str, Any] = Field(default_factory=dict)
        """init kwargs for qianfan client init, such as `query_per_second` which is
            associated with qianfan resource object to limit QPS"""

        model_kwargs: Dict[str, Any] = Field(default_factory=dict)
        """extra params for model invoke using with `do`."""

        client: Any

        qianfan_ak: Optional[str] = None
        qianfan_sk: Optional[str] = None

        streaming: Optional[bool] = False
        """Whether to stream the results or not."""

        model: str = "ERNIE-Bot-turbo"
        """Model name.
        you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu

        preset models are mapping to an endpoint.
        `model` will be ignored if `endpoint` is set
        """

        endpoint: Optional[str] = None
        """Endpoint of the Qianfan LLM, required if custom model used."""

        request_timeout: Optional[int] = 60
        """request timeout for chat http requests"""

        top_p: Optional[float] = 0.8
        temperature: Optional[float] = 0.95
        penalty_score: Optional[float] = 1
        """Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo.
        In the case of other model, passing these params will not affect the result.
        """

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            values["qianfan_ak"] = convert_to_secret_str(
                get_from_dict_or_env(
                    values,
                    "qianfan_ak",
                    "QIANFAN_AK",
                    default="",
                )
            )
            values["qianfan_sk"] = convert_to_secret_str(
                get_from_dict_or_env(
                    values,
                    "qianfan_sk",
                    "QIANFAN_SK",
                    default="",
                )
            )

            params = {
                **values.get("init_kwargs", {}),
                "model": values["model"],
            }
            if values["qianfan_ak"].get_secret_value() != "":
                params["ak"] = values["qianfan_ak"].get_secret_value()
            if values["qianfan_sk"].get_secret_value() != "":
                params["sk"] = values["qianfan_sk"].get_secret_value()
            if values["endpoint"] is not None and values["endpoint"] != "":
                params["endpoint"] = values["endpoint"]
            try:
                import qianfan

                values["client"] = qianfan.Completion(**params)
            except ImportError:
                raise ImportError(
                    "qianfan package not found, please install it with "
                    "`pip install qianfan`"
                )
            return values

        @property
        def _identifying_params(self) -> Dict[str, Any]:
            return {
                **{"endpoint": self.endpoint, "model": self.model},
                **super()._identifying_params,
            }

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

        @property
        def _default_params(self) -> Dict[str, Any]:
            """Get the default parameters for calling Qianfan API."""
            normal_params = {
                "model": self.model,
                "endpoint": self.endpoint,
                "stream": self.streaming,
                "request_timeout": self.request_timeout,
                "top_p": self.top_p,
                "temperature": self.temperature,
                "penalty_score": self.penalty_score,
            }

            return {**normal_params, **self.model_kwargs}

        def _convert_prompt_msg_params(
            self,
            prompt: str,
            **kwargs: Any,
        ) -> dict:
            if "streaming" in kwargs:
                kwargs["stream"] = kwargs.pop("streaming")
            return {
                **{"prompt": prompt, "model": self.model},
                **self._default_params,
                **kwargs,
            }

        def _call(
            self,
            prompt: str,
            stop: Optional[List[str]] = None,
            run_manager: Optional[CallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> str:
            """Call out to an qianfan models endpoint for each generation with a prompt.
            Args:
                prompt: The prompt to pass into the model.
                stop: Optional list of stop words to use when generating.
            Returns:
                The string generated by the model.

            Example:
                .. code-block:: python
                    response = qianfan_model.invoke("Tell me a joke.")
            """
            if self.streaming:
                completion = ""
                for chunk in self._stream(prompt, stop, run_manager, **kwargs):
                    completion += chunk.text
                return completion
            params = self._convert_prompt_msg_params(prompt, **kwargs)
            params["stop"] = stop
            response_payload = self.client.do(**params)

            return response_payload["result"]

        async def _acall(
            self,
            prompt: str,
            stop: Optional[List[str]] = None,
            run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> str:
            if self.streaming:
                completion = ""
                async for chunk in self._astream(prompt, stop, run_manager, **kwargs):
                    completion += chunk.text
                return completion

            params = self._convert_prompt_msg_params(prompt, **kwargs)
            params["stop"] = stop
            response_payload = await self.client.ado(**params)

            return response_payload["result"]

        def _stream(
            self,
            prompt: str,
            stop: Optional[List[str]] = None,
            run_manager: Optional[CallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> Iterator[GenerationChunk]:
            params = self._convert_prompt_msg_params(prompt, **{**kwargs, "stream": True})
            params["stop"] = stop
            for res in self.client.do(**params):
                if res:
                    chunk = GenerationChunk(text=res["result"])
                    if run_manager:
                        run_manager.on_llm_new_token(chunk.text)
                    yield chunk

        async def _astream(
            self,
            prompt: str,
            stop: Optional[List[str]] = None,
            run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> AsyncIterator[GenerationChunk]:
            params = self._convert_prompt_msg_params(prompt, **{**kwargs, "stream": True})
            params["stop"] = stop
            async for res in await self.client.ado(**params):
                if res:
                    chunk = GenerationChunk(text=res["result"])
                    if run_manager:
                        await run_manager.on_llm_new_token(chunk.text)
                    yield chunk