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# Source code for langchain_community.llms.together

    """Wrapper around Together AI's Completion API."""
    import logging
    from typing import Any, Dict, List, Optional

    from aiohttp import ClientSession
    from langchain_core._api.deprecation import deprecated
    from langchain_core.callbacks import (
        AsyncCallbackManagerForLLMRun,
        CallbackManagerForLLMRun,
    )
    from langchain_core.language_models.llms import LLM
    from langchain_core.pydantic_v1 import Extra, SecretStr, root_validator
    from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

    from langchain_community.utilities.requests import Requests

    logger = logging.getLogger(__name__)

    @deprecated(
        since="0.0.12", removal="0.3", alternative_import="langchain_together.Together"
    )
    class Together(LLM):
        """LLM models from `Together`.

        To use, you'll need an API key which you can find here:
        https://api.together.xyz/settings/api-keys. This can be passed in as init param
        ``together_api_key`` or set as environment variable ``TOGETHER_API_KEY``.

        Together AI API reference: https://docs.together.ai/reference/inference
        """

        base_url: str = "https://api.together.xyz/inference"
        """Base inference API URL."""
        together_api_key: SecretStr
        """Together AI API key. Get it here: https://api.together.xyz/settings/api-keys"""
        model: str
        """Model name. Available models listed here:
            https://docs.together.ai/docs/inference-models
        """
        temperature: Optional[float] = None
        """Model temperature."""
        top_p: Optional[float] = None
        """Used to dynamically adjust the number of choices for each predicted token based
            on the cumulative probabilities. A value of 1 will always yield the same
            output. A temperature less than 1 favors more correctness and is appropriate
            for question answering or summarization. A value greater than 1 introduces more
            randomness in the output.
        """
        top_k: Optional[int] = None
        """Used to limit the number of choices for the next predicted word or token. It
            specifies the maximum number of tokens to consider at each step, based on their
            probability of occurrence. This technique helps to speed up the generation
            process and can improve the quality of the generated text by focusing on the
            most likely options.
        """
        max_tokens: Optional[int] = None
        """The maximum number of tokens to generate."""
        repetition_penalty: Optional[float] = None
        """A number that controls the diversity of generated text by reducing the
            likelihood of repeated sequences. Higher values decrease repetition.
        """
        logprobs: Optional[int] = None
        """An integer that specifies how many top token log probabilities are included in
            the response for each token generation step.
        """

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

            extra = Extra.forbid

        @root_validator(pre=True)
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate that api key exists in environment."""
            values["together_api_key"] = convert_to_secret_str(
                get_from_dict_or_env(values, "together_api_key", "TOGETHER_API_KEY")
            )
            return values

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

        def _format_output(self, output: dict) -> str:
            return output["output"]["choices"][0]["text"]

        @staticmethod
        def get_user_agent() -> str:
            from langchain_community import __version__

            return f"langchain/{__version__}"

        @property
        def default_params(self) -> Dict[str, Any]:
            return {
                "model": self.model,
                "temperature": self.temperature,
                "top_p": self.top_p,
                "top_k": self.top_k,
                "max_tokens": self.max_tokens,
                "repetition_penalty": self.repetition_penalty,
            }

        def _call(
            self,
            prompt: str,
            stop: Optional[List[str]] = None,
            run_manager: Optional[CallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> str:
            """Call out to Together's text generation endpoint.

            Args:
                prompt: The prompt to pass into the model.

            Returns:
                The string generated by the model..
            """

            headers = {
                "Authorization": f"Bearer {self.together_api_key.get_secret_value()}",
                "Content-Type": "application/json",
            }
            stop_to_use = stop[0] if stop and len(stop) == 1 else stop
            payload: Dict[str, Any] = {
                **self.default_params,
                "prompt": prompt,
                "stop": stop_to_use,
                **kwargs,
            }

            # filter None values to not pass them to the http payload
            payload = {k: v for k, v in payload.items() if v is not None}
            request = Requests(headers=headers)
            response = request.post(url=self.base_url, data=payload)

            if response.status_code >= 500:
                raise Exception(f"Together Server: Error {response.status_code}")
            elif response.status_code >= 400:
                raise ValueError(f"Together received an invalid payload: {response.text}")
            elif response.status_code != 200:
                raise Exception(
                    f"Together returned an unexpected response with status "
                    f"{response.status_code}: {response.text}"
                )

            data = response.json()
            if data.get("status") != "finished":
                err_msg = data.get("error", "Undefined Error")
                raise Exception(err_msg)

            output = self._format_output(data)

            return output

        async def _acall(
            self,
            prompt: str,
            stop: Optional[List[str]] = None,
            run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> str:
            """Call Together model to get predictions based on the prompt.

            Args:
                prompt: The prompt to pass into the model.

            Returns:
                The string generated by the model.
            """
            headers = {
                "Authorization": f"Bearer {self.together_api_key.get_secret_value()}",
                "Content-Type": "application/json",
            }
            stop_to_use = stop[0] if stop and len(stop) == 1 else stop
            payload: Dict[str, Any] = {
                **self.default_params,
                "prompt": prompt,
                "stop": stop_to_use,
                **kwargs,
            }

            # filter None values to not pass them to the http payload
            payload = {k: v for k, v in payload.items() if v is not None}
            async with ClientSession() as session:
                async with session.post(
                    self.base_url, json=payload, headers=headers
                ) as response:
                    if response.status >= 500:
                        raise Exception(f"Together Server: Error {response.status}")
                    elif response.status >= 400:
                        raise ValueError(
                            f"Together received an invalid payload: {response.text}"
                        )
                    elif response.status != 200:
                        raise Exception(
                            f"Together returned an unexpected response with status "
                            f"{response.status}: {response.text}"
                        )

                    response_json = await response.json()

                    if response_json.get("status") != "finished":
                        err_msg = response_json.get("error", "Undefined Error")
                        raise Exception(err_msg)

                    output = self._format_output(response_json)
                    return output