Source code for langchain_community.llms.gooseai

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 Extra, Field, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

logger = logging.getLogger(__name__)

[docs]class GooseAI(LLM): """GooseAI large language models. To use, you should have the ``openai`` python package installed, and the environment variable ``GOOSEAI_API_KEY`` set with your API key. Any parameters that are valid to be passed to the openai.create call can be passed in, even if not explicitly saved on this class. Example: .. code-block:: python from langchain_community.llms import GooseAI gooseai = GooseAI(model_name="gpt-neo-20b") """ client: Any model_name: str = "gpt-neo-20b" """Model name to use""" temperature: float = 0.7 """What sampling temperature to use""" max_tokens: int = 256 """The maximum number of tokens to generate in the completion. -1 returns as many tokens as possible given the prompt and the models maximal context size.""" top_p: float = 1 """Total probability mass of tokens to consider at each step.""" min_tokens: int = 1 """The minimum number of tokens to generate in the completion.""" frequency_penalty: float = 0 """Penalizes repeated tokens according to frequency.""" presence_penalty: float = 0 """Penalizes repeated tokens.""" n: int = 1 """How many completions to generate for each prompt.""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `create` call not explicitly specified.""" logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict) """Adjust the probability of specific tokens being generated.""" gooseai_api_key: Optional[SecretStr] = None class Config: """Configuration for this pydantic config.""" extra = Extra.ignore @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("model_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"""WARNING! {field_name} is not default parameter. {field_name} was transferred to model_kwargs. Please confirm that {field_name} is what you intended.""" ) extra[field_name] = values.pop(field_name) values["model_kwargs"] = extra return values @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" gooseai_api_key = convert_to_secret_str( get_from_dict_or_env(values, "gooseai_api_key", "GOOSEAI_API_KEY") ) values["gooseai_api_key"] = gooseai_api_key try: import openai openai.api_key = gooseai_api_key.get_secret_value() openai.api_base = "" values["client"] = openai.Completion except ImportError: raise ImportError( "Could not import openai python package. " "Please install it with `pip install openai`." ) return values @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling GooseAI API.""" normal_params = { "temperature": self.temperature, "max_tokens": self.max_tokens, "top_p": self.top_p, "min_tokens": self.min_tokens, "frequency_penalty": self.frequency_penalty, "presence_penalty": self.presence_penalty, "n": self.n, "logit_bias": self.logit_bias, } return {**normal_params, **self.model_kwargs} @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return {**{"model_name": self.model_name}, **self._default_params} @property def _llm_type(self) -> str: """Return type of llm.""" return "gooseai" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call the GooseAI API.""" params = self._default_params if stop is not None: if "stop" in params: raise ValueError("`stop` found in both the input and default params.") params["stop"] = stop params = {**params, **kwargs} response = self.client.create(engine=self.model_name, prompt=prompt, **params) text = response.choices[0].text return text