Similar to top_p, but select instead only the top_k most likely tokens.
Higher value = higher range of possible random results.
param top_p: Optional[float] = 0.1¶
If not set to 1, select tokens with probabilities adding up to less than this
number. Higher value = higher range of possible random results.
param truncation_length: Optional[int] = 2048¶
Truncate the prompt up to this length. The leftmost tokens are removed if
the prompt exceeds this length. Most models require this to be at most 2048.
param typical_p: Optional[float] = 1¶
If not set to 1, select only tokens that are at least this much more likely to
appear than random tokens, given the prior text.
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶
Check Cache and run the LLM on the given prompt and input.
async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.