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131 lines
4.3 KiB
Python
131 lines
4.3 KiB
Python
"""Wrapper around StochasticAI APIs."""
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import logging
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import time
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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, Field, 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 StochasticAI(LLM, BaseModel):
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"""Wrapper around StochasticAI large language models.
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To use, you should have the environment variable ``STOCHASTICAI_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 StochasticAI
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stochasticai = StochasticAI(api_url="")
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"""
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api_url: str = ""
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"""Model name to use."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not
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explicitly specified."""
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stochasticai_api_key: Optional[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(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = {field.alias for field in cls.__fields__.values()}
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name not in all_required_field_names:
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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logger.warning(
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f"""{field_name} was transfered to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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values["model_kwargs"] = extra
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return values
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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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stochasticai_api_key = get_from_dict_or_env(
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values, "stochasticai_api_key", "STOCHASTICAI_API_KEY"
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)
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values["stochasticai_api_key"] = stochasticai_api_key
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return values
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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 {
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**{"endpoint_url": self.api_url},
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**{"model_kwargs": self.model_kwargs},
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}
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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 "stochasticai"
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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"""Call out to StochasticAI'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 = StochasticAI("Tell me a joke.")
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"""
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params = self.model_kwargs or {}
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response_post = requests.post(
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url=self.api_url,
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json={"prompt": prompt, "params": params},
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headers={
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"apiKey": f"{self.stochasticai_api_key}",
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"Accept": "application/json",
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"Content-Type": "application/json",
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},
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)
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response_post.raise_for_status()
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response_post_json = response_post.json()
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completed = False
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while not completed:
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response_get = requests.get(
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url=response_post_json["data"]["responseUrl"],
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headers={
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"apiKey": f"{self.stochasticai_api_key}",
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"Accept": "application/json",
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"Content-Type": "application/json",
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},
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)
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response_get.raise_for_status()
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response_get_json = response_get.json()["data"]
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text = response_get_json.get("completion")
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completed = text is not None
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time.sleep(0.5)
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text = text[0]
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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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