| | import json |
| | from copy import deepcopy |
| | from typing import Any, Dict |
| | from flow_modules.aiflows.ChatFlowModule import ChatAtomicFlow |
| |
|
| |
|
| |
|
| | class PlanGeneratorAtomicFlow(ChatAtomicFlow): |
| | """This class wraps around the Chat API to generate plan from a goal. |
| | *Input Interface Non Initialized*: |
| | - `goal` |
| | *Input Interface Initialized*: |
| | - `goal` |
| | *Output Interface*: |
| | - `plan` |
| | """ |
| | 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} |
| |
|
| | |
| | kwargs.update(cls._set_up_prompts(flow_config)) |
| |
|
| | |
| | kwargs.update(cls._set_up_backend(flow_config)) |
| |
|
| | |
| | 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) |
| | while True: |
| | api_output = super().run(input_data)["api_output"].strip() |
| | try: |
| | response = json.loads(api_output) |
| | return response |
| | except (json.decoder.JSONDecodeError, json.JSONDecodeError): |
| | new_goal = "The previous respond cannot be parsed with json.loads. Next time, do not provide any comments or code blocks. Make sure your next response is purely json parsable." |
| | new_input_data = input_data.copy() |
| | new_input_data['goal'] = new_goal |
| | input_data = new_input_data |