serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]#
Return the namespace of the langchain object.
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.PipelineAI[source]#
Wrapper around PipelineAI large language models.
To use, you should have the pipeline-ai python package installed,
and the environment variable PIPELINE_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 import PipelineAI
pipeline = PipelineAI(pipeline_key="")
Validators
build_extra » all fields
raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields
field pipeline_key: str = ''#
The id or tag of the target pipeline
field pipeline_kwargs: Dict[str, Any] [Optional]#
Holds any pipeline 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