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110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
"""Wrapper around Prediction Guard 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 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 PredictionGuard(LLM):
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"""Wrapper around Prediction Guard large language models.
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To use, you should have the ``predictionguard`` python package installed, and the
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environment variable ``PREDICTIONGUARD_TOKEN`` set with your access token, 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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pgllm = PredictionGuard(name="text-gen-proxy-name", token="my-access-token")
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"""
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client: Any #: :meta private:
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name: Optional[str] = "default-text-gen"
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"""Proxy 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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token: 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 the access token and python package exists in environment."""
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token = get_from_dict_or_env(values, "token", "PREDICTIONGUARD_TOKEN")
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try:
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import predictionguard as pg
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values["client"] = pg.Client(token=token)
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except ImportError:
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raise ValueError(
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"Could not import predictionguard python package. "
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"Please install it with `pip install predictionguard`."
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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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}
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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 {**{"name": self.name}, **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 "predictionguard"
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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"""Call out to Prediction Guard's model proxy.
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Args:
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prompt: The prompt to pass into the model.
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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 = pgllm("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.predict(
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name=self.name,
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data={
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"prompt": prompt,
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"max_tokens": params["max_tokens"],
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"temperature": params["temperature"],
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},
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)
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text = response["text"]
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# If stop tokens are provided, Prediction Guard'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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