Source code for langchain_experimental.plan_and_execute.agent_executor
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
from langchain_experimental.plan_and_execute.executors.base import BaseExecutor
from langchain_experimental.plan_and_execute.planners.base import BasePlanner
from langchain_experimental.plan_and_execute.schema import (
    BaseStepContainer,
    ListStepContainer,
)
from langchain_experimental.pydantic_v1 import Field
[docs]class PlanAndExecute(Chain):
    """Plan and execute a chain of steps."""
    planner: BasePlanner
    """The planner to use."""
    executor: BaseExecutor
    """The executor to use."""
    step_container: BaseStepContainer = Field(default_factory=ListStepContainer)
    """The step container to use."""
    input_key: str = "input"
    output_key: str = "output"
    @property
    def input_keys(self) -> List[str]:
        return [self.input_key]
    @property
    def output_keys(self) -> List[str]:
        return [self.output_key]
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        plan = self.planner.plan(
            inputs,
            callbacks=run_manager.get_child() if run_manager else None,
        )
        if run_manager:
            run_manager.on_text(str(plan), verbose=self.verbose)
        for step in plan.steps:
            _new_inputs = {
                "previous_steps": self.step_container,
                "current_step": step,