PlanGeneratorAtomicFlow.py

import json
from copy import deepcopy
from typing import Any, Dict, List
from flow_modules.aiflows.OpenAIChatFlowModule import OpenAIChatAtomicFlow

from dataclasses import dataclass
class PlanGeneratorAtomicFlow(OpenAIChatAtomicFlow):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    @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))
        kwargs.update(cls._set_up_backend(flow_config))

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

    def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        hint_for_model = """
        Make sure your response is in the following format:
              Response Format:
              {
              "plan": "Python printable string of the plan corresponding to the goal",
              }
        """
        if 'goal' in input_data:
            input_data['goal'] += hint_for_model
        api_output = super().run(input_data)["api_output"].strip()
        try:
            response = json.loads(api_output)
            return response
        except json.decoder.JSONDecodeError:
            new_input_data = input_data.copy()
            new_input_data['goal'] += ("The previous respond cannot be parsed with json.loads, Make sure your next "
                                       "response is solely in JSON format.")
            new_api_output = super().run(new_input_data)["api_output"].strip()
            return json.loads(new_api_output)