PlanWriterFlow.py

from typing import Dict, Any
import os

from flow_modules.aiflows.ContentWriterFlowModule import ContentWriterFlow
from aiflows.base_flows import CircularFlow


class PlanWriterFlow(ContentWriterFlow):
    """This flow inherits from ContentWriterFlow.
    In the subflow of the executor, we specify the InteractivePlanGneFlow (https://huggingface.co/aiflows/InteractivePlanGenFlowModule)

    *Input Interface*:
    - `goal`

    *Output Interface*:
    - `plan`
    - `result`
    - `summary`
    - `status`

    *Configuration Parameters*:
    - Also refer to superclass ContentWriterFlow (https://huggingface.co/aiflows/ContentWriterFlowModule)
    - `input_interface`: the input interface of the flow
    - `output_interface`: the output interface of the flow
    - `subflows_config`: the configuration of the subflows
    - `early_exit_key`: the key in the flow state that indicates the early exit condition
    - `topology`: the topology of the flow

    """
    def _on_reach_max_round(self):
        """This function is called when the flow reaches the maximum amount of rounds.
        It decides whether to terminate the flow or continue running.
        """
        self._state_update_dict({
            "plan": "The maximum amount of rounds was reached before the model generated the plan.",
            "status": "unfinished"
        })

    @CircularFlow.output_msg_payload_processor
    def detect_finish_or_continue(self, output_payload: Dict[str, Any], src_flow) -> Dict[str, Any]:
        """This function is called when the subflow finishes running.
        configured in the topology of the subflow config.
        :param output_payload: the output payload of the subflow
        :type output_payload: Dict[str, Any]
        :param src_flow: the subflow that generates the output payload
        :type src_flow: Flow
        :return: the processed output payload
        :rtype: Dict[str, Any]
        """
        command = output_payload["command"]
        if command == "finish":
            # ~~~ fetch temp file location, plan content, memory file (of upper level flow e.g. ExtLib) from flow state
            keys_to_fetch_from_state = ["temp_plan_file_location", "plan", "memory_files"]
            fetched_state = self._fetch_state_attributes_by_keys(keys=keys_to_fetch_from_state)
            temp_plan_file_location = fetched_state["temp_plan_file_location"]
            plan_content = fetched_state["plan"]
            plan_file_location = fetched_state["memory_files"]["plan"]

            # ~~~ delete the temp plan file ~~~
            if os.path.exists(temp_plan_file_location):
                os.remove(temp_plan_file_location)

            # ~~~ write plan content to plan file ~~~
            with open(plan_file_location, 'w') as file:
                file.write(plan_content)

            # ~~~ return the plan content ~~~
            return {
                "EARLY_EXIT": True,
                "plan": plan_content,
                "summary": "ExtendLibrary/PlanWriter: " + output_payload["command_args"]["summary"],
                "status": "finished"
            }
        elif command == "manual_finish":
            # ~~~ delete the temp plan file ~~~
            keys_to_fetch_from_state = ["temp_plan_file_location"]
            fetched_state = self._fetch_state_attributes_by_keys(keys=keys_to_fetch_from_state)
            temp_plan_file_location = fetched_state["temp_plan_file_location"]
            if os.path.exists(temp_plan_file_location):
                os.remove(temp_plan_file_location)
            # ~~~ return the manual quit status ~~~
            return {
                "EARLY_EXIT": True,
                "plan": "no plan was generated",
                "summary": "ExtendLibrary/PlanWriter: PlanWriter was terminated explicitly by the user, process is unfinished",
                "status": "unfinished"
            }
        elif command == "write_plan":
            keys_to_fetch_from_state = ["memory_files"]
            fetched_state = self._fetch_state_attributes_by_keys(keys=keys_to_fetch_from_state)
            plan_file_location = fetched_state["memory_files"]["plan"]
            output_payload["command_args"]["plan_file_location"] = plan_file_location
            return output_payload
        else:
            return output_payload

    def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        This function runs the flow.
        :param input_data: the input data of the flow
        :type input_data: Dict[str, Any]
        :return: the output data of the flow
        :rtype: Dict[str, Any]
        """
        # ~~~ sets the input_data in the flow_state dict ~~~
        self._state_update_dict(update_data=input_data)

        max_rounds = self.flow_config.get("max_rounds", 1)
        if max_rounds is None:
            log.info(f"Running {self.flow_config['name']} without `max_rounds` until the early exit condition is met.")

        self._sequential_run(max_rounds=max_rounds)

        output = self._get_output_from_state()

        self.reset(full_reset=True, recursive=True, src_flow=self)

        return output