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133 lines
4.2 KiB
Python
133 lines
4.2 KiB
Python
"""Wrapper around Cohere APIs."""
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import logging
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel, Extra, root_validator
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from langchain.llms.base import LLM
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from langchain.llms.utils import enforce_stop_tokens
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from langchain.utils import get_from_dict_or_env
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logger = logging.getLogger(__name__)
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class Cohere(LLM, BaseModel):
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"""Wrapper around Cohere large language models.
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To use, you should have the ``cohere`` python package installed, and the
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environment variable ``COHERE_API_KEY`` set with your API key, or pass
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it as a named parameter to the constructor.
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Example:
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.. code-block:: python
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from langchain.llms import Cohere
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cohere = Cohere(model="gptd-instruct-tft", cohere_api_key="my-api-key")
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"""
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client: Any #: :meta private:
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model: Optional[str] = None
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"""Model name to use."""
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max_tokens: int = 256
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"""Denotes the number of tokens to predict per generation."""
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temperature: float = 0.75
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"""A non-negative float that tunes the degree of randomness in generation."""
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k: int = 0
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"""Number of most likely tokens to consider at each step."""
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p: int = 1
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"""Total probability mass of tokens to consider at each step."""
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frequency_penalty: int = 0
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"""Penalizes repeated tokens according to frequency."""
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presence_penalty: int = 0
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"""Penalizes repeated tokens."""
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truncate: Optional[str] = None
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"""Specify how the client handles inputs longer than the maximum token
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length: Truncate from START, END or NONE"""
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cohere_api_key: Optional[str] = None
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stop: Optional[List[str]] = None
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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cohere_api_key = get_from_dict_or_env(
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values, "cohere_api_key", "COHERE_API_KEY"
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)
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try:
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import cohere
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values["client"] = cohere.Client(cohere_api_key)
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except ImportError:
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raise ValueError(
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"Could not import cohere python package. "
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"Please it install it with `pip install cohere`."
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)
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return values
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling Cohere API."""
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return {
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"k": self.k,
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"p": self.p,
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"frequency_penalty": self.frequency_penalty,
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"presence_penalty": self.presence_penalty,
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"truncate": self.truncate,
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}
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {**{"model": self.model}, **self._default_params}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "cohere"
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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"""Call out to Cohere's generate endpoint.
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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response = cohere("Tell me a joke.")
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"""
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params = self._default_params
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if self.stop is not None and stop is not None:
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raise ValueError("`stop` found in both the input and default params.")
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elif self.stop is not None:
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params["stop_sequences"] = self.stop
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else:
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params["stop_sequences"] = stop
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response = self.client.generate(model=self.model, prompt=prompt, **params)
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text = response.generations[0].text
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# If stop tokens are provided, Cohere's endpoint returns them.
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# In order to make this consistent with other endpoints, we strip them.
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if stop is not None or self.stop is not None:
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text = enforce_stop_tokens(text, params["stop_sequences"])
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return text
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