PlanGeneratorAtomicFlow.py

import json
from copy import deepcopy
from typing import Any, Dict
from flow_modules.aiflows.ChatFlowModule import ChatAtomicFlow



class PlanGeneratorAtomicFlow(ChatAtomicFlow):
    """Generates one function with docstrings to finish the given goal (from the controller).
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.hint_for_model = """
        Make sure your response is in the following format:
              Response Format:
              {
              "plan": "A step-by-step plan to finish the given goal, each step of plan should contain full information about writing a function",
              }
        """

    @classmethod
    def instantiate_from_config(cls, config):
        flow_config = deepcopy(config)

        kwargs = {"flow_config": flow_config}

        # ~~~ Set up prompts ~~~
        kwargs.update(cls._set_up_prompts(flow_config))

        # ~~~ Set up backend ~~~
        kwargs.update(cls._set_up_backend(flow_config))

        # ~~~ Instantiate flow ~~~
        return cls(**kwargs)

    def _update_prompts_and_input(self, input_data: Dict[str, Any]):
        if 'goal' in input_data:
            input_data['goal'] += self.hint_for_model

    def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        self._update_prompts_and_input(input_data)
        api_output = super().run(input_data)["api_output"].strip()
        try:
            response = json.loads(api_output)
            return response
        except json.decoder.JSONDecodeError:
            new_goal = f"Here is your previous response {api_output}, it cannot be parsed with json.loads, please fix this issue."
            new_input_data = input_data.copy()
            new_input_data['goal'] = new_goal
            new_api_output = super().run(new_input_data)["api_output"].strip()
            return json.loads(new_api_output)