motleycrew.applications.research_agent.question_answerer

Functions

create_answer_question_langchain_tool(graph, ...)
get_subquestions(graph, question)

Classes

AnswerSubQuestionTool(graph, answer_length)	Tool to answer a question based on the notes and sub-questions.
QuestionAnswererInput(*, question)

class motleycrew.applications.research_agent.question_answerer.AnswerSubQuestionTool(graph: MotleyGraphStore, answer_length: int, prompt: str | BasePromptTemplate = None, llm: BaseLanguageModel | None = None)

Bases: MotleyTool

Tool to answer a question based on the notes and sub-questions.

__init__(graph: MotleyGraphStore, answer_length: int, prompt: str | BasePromptTemplate = None, llm: BaseLanguageModel | None = None)

Initialize the MotleyTool.

Parameters:

• name – Name of the tool (required if tool is None).

• description – Description of the tool (required if tool is None).

• args_schema – Schema of the tool arguments (required if tool is None).

• return_direct – If True, the tool’s output will be returned directly to the user.

• handle_exceptions –

  Whether to handle exceptions (return their message as output).

  If True, the tool will return any raised exception’s message as its output.

  If a list of exceptions is provided, only these exceptions will be handled.

  If False, the tool will raise the exception.

  If return_direct is True, the tool will always handle InvalidOutput exceptions, as the tool is considered an output handler.

• retry_config – Configuration for retry behavior. If None, exceptions will not be retried.

• tool – Langchain BaseTool to wrap. Usually not needed, as the tool is created from the run method.

class motleycrew.applications.research_agent.question_answerer.QuestionAnswererInput(*, question: Question)

Bases: BaseModel

question: Question

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

motleycrew.applications.research_agent.question_answerer.get_subquestions(graph: MotleyGraphStore, question: Question) → list[Question]

motleycrew.applications.research_agent.question_answerer.create_answer_question_langchain_tool(graph: MotleyGraphStore, answer_length: int, prompt: str | BasePromptTemplate = None, llm: BaseLanguageModel | None = None) → Tool