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114 lines
3.5 KiB
Python
114 lines
3.5 KiB
Python
"""Wrapper around ForefrontAI APIs."""
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from typing import Any, Dict, List, Mapping, Optional
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import requests
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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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class ForefrontAI(LLM, BaseModel):
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"""Wrapper around ForefrontAI large language models.
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To use, you should have the environment variable ``FOREFRONTAI_API_KEY``
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set with your API key.
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Example:
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.. code-block:: python
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from langchain.llms import ForefrontAI
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forefrontai = ForefrontAI(endpoint_url="")
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"""
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endpoint_url: str = ""
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"""Model name to use."""
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temperature: float = 0.7
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"""What sampling temperature to use."""
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length: int = 256
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"""The maximum number of tokens to generate in the completion."""
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top_p: float = 1.0
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"""Total probability mass of tokens to consider at each step."""
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top_k: int = 40
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"""The number of highest probability vocabulary tokens to
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keep for top-k-filtering."""
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repetition_penalty: int = 1
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"""Penalizes repeated tokens according to frequency."""
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forefrontai_api_key: Optional[str] = None
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base_url: Optional[str] = None
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"""Base url to use, if None decides based on model name."""
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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 exists in environment."""
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forefrontai_api_key = get_from_dict_or_env(
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values, "forefrontai_api_key", "FOREFRONTAI_API_KEY"
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)
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values["forefrontai_api_key"] = forefrontai_api_key
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return values
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@property
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def _default_params(self) -> Mapping[str, Any]:
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"""Get the default parameters for calling ForefrontAI API."""
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return {
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"temperature": self.temperature,
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"length": self.length,
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"top_p": self.top_p,
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"top_k": self.top_k,
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"repetition_penalty": self.repetition_penalty,
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}
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {**{"endpoint_url": self.endpoint_url}, **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 "forefrontai"
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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"""Call out to ForefrontAI's complete 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 = ForefrontAI("Tell me a joke.")
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"""
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response = requests.post(
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url=self.endpoint_url,
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headers={
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"Authorization": f"Bearer {self.forefrontai_api_key}",
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"Content-Type": "application/json",
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},
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json={"text": prompt, **self._default_params},
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)
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response_json = response.json()
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text = response_json["result"][0]["completion"]
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if stop is not None:
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# I believe this is required since the stop tokens
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# are not enforced by the model parameters
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text = enforce_stop_tokens(text, stop)
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return text
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