Source code for sherpa_ai.agents.physicist

from typing import List

from sherpa_ai.actions import Deliberation, GoogleSearch, SynthesizeOutput
from sherpa_ai.actions.base import BaseAction
from sherpa_ai.agents.base import BaseAgent
from sherpa_ai.memory.belief import Belief
from sherpa_ai.policies import ReactPolicy

PHYSICIST_DESCRIPTION = "You are a physicist with a deep-rooted expertise in understanding and analyzing the fundamental principles of the universe, spanning from the tiniest subatomic particles to vast cosmic phenomena. Your primary role is to assist individuals, organizations, and researchers in navigating and resolving complex physics-related challenges, using your knowledge to guide decisions and ensure the accuracy and reliability of outcomes."  # noqa: E501

ACTION_PLAN_DESCRIPTION = "Given your specialized expertise, historical context, and your mission to facilitate physics-based solutions, determine which action and its corresponding arguments would be the most scientifically sound and efficient approach to achieve the described task."  # noqa: E501




class Physicist(BaseAgent):
    """
    The physicist agent answers questions or research about physics-related topics
    """

    name: str = "Physicist"
    description: str = PHYSICIST_DESCRIPTION
    num_runs: int = 3

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        if self.belief is None:
            self.belief = Belief()

        if self.policy is None:
            self.policy = ReactPolicy(
                role_description=self.description,
                output_instruction=ACTION_PLAN_DESCRIPTION,
                llm=self.llm,
            )



    def create_actions(self) -> List[BaseAction]:
        return [
            Deliberation(role_description=self.description, llm=self.llm),
            GoogleSearch(
                role_description=self.description,
                task=self.belief.current_task.content,
                llm=self.llm,
            ),
        ]




    def synthesize_output(self) -> str:
        synthesize_action = SynthesizeOutput(
            role_description=self.description, llm=self.llm
        )
        result = synthesize_action.execute(
            self.belief.current_task.content,
            self.belief.get_context(self.llm.get_num_tokens),
            self.belief.get_internal_history(self.llm.get_num_tokens),
        )

        return result