Source code for langchain_experimental.plan_and_execute.schema
from abc import abstractmethod
from typing import List, Tuple
from langchain.schema import BaseOutputParser
from langchain_experimental.pydantic_v1 import BaseModel, Field
[docs]class Step(BaseModel):
    """Step."""
    value: str
    """The value."""
[docs]class Plan(BaseModel):
    """Plan."""
    steps: List[Step]
    """The steps."""
[docs]class StepResponse(BaseModel):
    """Step response."""
    response: str
    """The response."""
[docs]class BaseStepContainer(BaseModel):
    """Base step container."""
[docs]    @abstractmethod
    def add_step(self, step: Step, step_response: StepResponse) -> None:
        """Add step and step response to the container."""
[docs]    @abstractmethod
    def get_final_response(self) -> str:
        """Return the final response based on steps taken."""
[docs]class ListStepContainer(BaseStepContainer):
    """List step container."""
    steps: List[Tuple[Step, StepResponse]] = Field(default_factory=list)
    """The steps."""
[docs]    def add_step(self, step: Step, step_response: StepResponse) -> None:
        self.steps.append((step, step_response))
[docs]    def get_steps(self) -> List[Tuple[Step, StepResponse]]:
        return self.steps
[docs]    def get_final_response(self) -> str:
        return self.steps[-1][1].response
[docs]class PlanOutputParser(BaseOutputParser):
    """Plan output parser."""
[docs]    @abstractmethod
    def parse(self, text: str) -> Plan:
        """Parse into a plan."""