langchain/llms/cohere.py

"""Wrapper around Cohere APIs."""
import logging
from typing import Any, Dict, List, Optional

from pydantic import BaseModel, Extra, root_validator

from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env

logger = logging.getLogger(__name__)


class Cohere(LLM, BaseModel):
    """Wrapper around Cohere large language models.

    To use, you should have the ``cohere`` python package installed, and the
    environment variable ``COHERE_API_KEY`` set with your API key, or pass
    it as a named parameter to the constructor.

    Example:
        .. code-block:: python

            from langchain.llms import Cohere
            cohere = Cohere(model="gptd-instruct-tft", cohere_api_key="my-api-key")
    """

    client: Any  #: :meta private:
    model: Optional[str] = None
    """Model name to use."""

    max_tokens: int = 256
    """Denotes the number of tokens to predict per generation."""

    temperature: float = 0.75
    """A non-negative float that tunes the degree of randomness in generation."""

    k: int = 0
    """Number of most likely tokens to consider at each step."""

    p: int = 1
    """Total probability mass of tokens to consider at each step."""

    frequency_penalty: float = 0.0
    """Penalizes repeated tokens according to frequency. Between 0 and 1."""

    presence_penalty: float = 0.0
    """Penalizes repeated tokens. Between 0 and 1."""

    truncate: Optional[str] = None
    """Specify how the client handles inputs longer than the maximum token
    length: Truncate from START, END or NONE"""

    cohere_api_key: Optional[str] = None

    stop: Optional[List[str]] = None

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

        extra = Extra.forbid

    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        cohere_api_key = get_from_dict_or_env(
            values, "cohere_api_key", "COHERE_API_KEY"
        )
        try:
            import cohere

            values["client"] = cohere.Client(cohere_api_key)
        except ImportError:
            raise ValueError(
                "Could not import cohere python package. "
                "Please it install it with `pip install cohere`."
            )
        return values

    @property
    def _default_params(self) -> Dict[str, Any]:
        """Get the default parameters for calling Cohere API."""
        return {
            "max_tokens": self.max_tokens,
            "temperature": self.temperature,
            "k": self.k,
            "p": self.p,
            "frequency_penalty": self.frequency_penalty,
            "presence_penalty": self.presence_penalty,
            "truncate": self.truncate,
        }

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

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

    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
        """Call out to Cohere's generate endpoint.

        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 = cohere("Tell me a joke.")
        """
        params = self._default_params
        if self.stop is not None and stop is not None:
            raise ValueError("`stop` found in both the input and default params.")
        elif self.stop is not None:
            params["stop_sequences"] = self.stop
        else:
            params["stop_sequences"] = stop

        response = self.client.generate(model=self.model, prompt=prompt, **params)
        text = response.generations[0].text
        # If stop tokens are provided, Cohere's endpoint returns them.
        # In order to make this consistent with other endpoints, we strip them.
        if stop is not None or self.stop is not None:
            text = enforce_stop_tokens(text, params["stop_sequences"])
        return text