Source code for langchain_experimental.plan_and_execute.agent_executor

from typing import Any, Dict, List, Optional

from langchain.chains.base import Chain
from langchain_core.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)

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, "objective": inputs[self.input_key], } new_inputs = {**_new_inputs, **inputs} response = self.executor.step( new_inputs, callbacks=run_manager.get_child() if run_manager else None, ) if run_manager: run_manager.on_text( f"*****\n\nStep: {step.value}", verbose=self.verbose ) run_manager.on_text( f"\n\nResponse: {response.response}", verbose=self.verbose ) self.step_container.add_step(step, response) return {self.output_key: self.step_container.get_final_response()} async def _acall( self, inputs: Dict[str, Any], run_manager: Optional[AsyncCallbackManagerForChainRun] = None, ) -> Dict[str, Any]: plan = await self.planner.aplan( inputs, callbacks=run_manager.get_child() if run_manager else None, ) if run_manager: await run_manager.on_text(str(plan), verbose=self.verbose) for step in plan.steps: _new_inputs = { "previous_steps": self.step_container, "current_step": step, "objective": inputs[self.input_key], } new_inputs = {**_new_inputs, **inputs} response = await self.executor.astep( new_inputs, callbacks=run_manager.get_child() if run_manager else None, ) if run_manager: await run_manager.on_text( f"*****\n\nStep: {step.value}", verbose=self.verbose ) await run_manager.on_text( f"\n\nResponse: {response.response}", verbose=self.verbose ) self.step_container.add_step(step, response) return {self.output_key: self.step_container.get_final_response()}