Source code for langchain_community.llms.predictionguard

import logging
from typing import Any, Dict, List, Optional

from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.pydantic_v1 import Extra, root_validator
from langchain_core.utils import get_from_dict_or_env

from langchain_community.llms.utils import enforce_stop_tokens

logger = logging.getLogger(__name__)


[docs]class PredictionGuard(LLM): """Prediction Guard large language models. To use, you should have the ``predictionguard`` python package installed, and the environment variable ``PREDICTIONGUARD_TOKEN`` set with your access token, or pass it as a named parameter to the constructor. To use Prediction Guard's API along with OpenAI models, set the environment variable ``OPENAI_API_KEY`` with your OpenAI API key as well. Example: .. code-block:: python pgllm = PredictionGuard(model="MPT-7B-Instruct", token="my-access-token", output={ "type": "boolean" }) """ client: Any #: :meta private: model: Optional[str] = "MPT-7B-Instruct" """Model name to use.""" output: Optional[Dict[str, Any]] = None """The output type or structure for controlling the LLM output.""" 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.""" token: Optional[str] = None """Your Prediction Guard access token.""" 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 the access token and python package exists in environment.""" token = get_from_dict_or_env(values, "token", "PREDICTIONGUARD_TOKEN") try: import predictionguard as pg values["client"] = pg.Client(token=token) except ImportError: raise ImportError( "Could not import predictionguard python package. " "Please install it with `pip install predictionguard`." ) return values @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling the Prediction Guard API.""" return { "max_tokens": self.max_tokens, "temperature": self.temperature, } @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 "predictionguard" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to Prediction Guard's model API. Args: prompt: The prompt to pass into the model. Returns: The string generated by the model. Example: .. code-block:: python response = pgllm.invoke("Tell me a joke.") """ import predictionguard as pg 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 = pg.Completion.create( model=self.model, prompt=prompt, output=self.output, temperature=params["temperature"], max_tokens=params["max_tokens"], **kwargs, ) text = response["choices"][0]["text"] # If stop tokens are provided, Prediction Guard'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