langchain.agents.output_parsers.self_ask.SelfAskOutputParser¶
class langchain.agents.output_parsers.self_ask.SelfAskOutputParser[source]¶
Bases: AgentOutputParser
Parses self-ask style LLM calls.
Expects output to be in one of two formats.
If the output signals that an action should be taken,
should be in the below format. This will result in an AgentAction
being returned.
`
Thoughts go here...
Follow up: what is the temperature in SF?
`
If the output signals that a final answer should be given,
should be in the below format. This will result in an AgentFinish
being returned.
`
Thoughts go here...
So the final answer is: The temperature is 100 degrees
`
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param finish_string: str = 'So the final answer is: '¶
param followups: Sequence[str] = ('Follow up:', 'Followup:')¶
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.
async ainvoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.config.RunnableConfig | None = None, **kwargs: Optional[Any]) → T¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
async aparse(text: str) → T¶