eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]#
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool#
Return whether or not the class is serializable.
pydantic model langchain.llms.CerebriumAI[source]#
Wrapper around CerebriumAI large language models.
To use, you should have the cerebrium python package installed, and the
environment variable CEREBRIUMAI_API_KEY set with your API key.
Any parameters that are valid to be passed to the call can be passed
in, even if not explicitly saved on this class.
Example
from langchain.llms import CerebriumAI
cerebrium = CerebriumAI(endpoint_url="")
Validators
build_extra » all fields
raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields
field endpoint_url: str = ''#
model endpoint to use
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not
explicitly specified.
field tags: Optional[List[str]] = None#
Tags to add to the run trace.
field verbose: bool [Optional]#
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → str#
Check Cache and run the LLM on the given prompt and input.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/llms.html