Metadata to add to the run trace.
param model_name: str = 'yandexgpt-lite'¶
Model name to use.
param model_uri: str = ''¶
Model uri to use.
param model_version: str = 'latest'¶
Model version to use.
param stop: Optional[List[str]] = None¶
Sequences when completion generation will stop.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: float = 0.6¶
What sampling temperature to use.
Should be a double number between 0 (inclusive) and 1 (inclusive).
param url: str = 'llm.api.cloud.yandex.net:443'¶
The url of the API.
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(messages: List[BaseMessage], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → BaseMessage¶
Call self as a function.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.