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104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
"""Wrapper around CerebriumAI API."""
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import logging
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from typing import Any, Dict, List, Mapping, Optional
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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 CerebriumAI(LLM, BaseModel):
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"""Wrapper around CerebriumAI large language models.
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To use, you should have the ``cerebrium`` python package installed, and the
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environment variable ``CEREBRIUMAI_API_KEY`` set with your API key.
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Any parameters that are valid to be passed to the call can be passed
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in, even if not explicitly saved on this class.
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Example:
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.. code-block:: python
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from langchain.llms import CerebriumAI
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cerebrium = CerebriumAI(endpoint_url="")
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"""
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endpoint_url: str = ""
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"""model endpoint 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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cerebriumai_api_key: Optional[str] = None
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class Config:
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"""Configuration for this pydantic config."""
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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 and python package exists in environment."""
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cerebriumai_api_key = get_from_dict_or_env(
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values, "cerebriumai_api_key", "CEREBRIUMAI_API_KEY"
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)
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values["cerebriumai_api_key"] = cerebriumai_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.endpoint_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 "cerebriumai"
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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"""Call to CerebriumAI endpoint."""
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try:
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from cerebrium import model_api_request
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except ImportError:
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raise ValueError(
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"Could not import cerebrium python package. "
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"Please install it with `pip install cerebrium`."
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)
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params = self.model_kwargs or {}
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response = model_api_request(
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self.endpoint_url, {"prompt": prompt, **params}, self.cerebriumai_api_key
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)
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text = response["data"]["result"]
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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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