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import json |
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from copy import deepcopy |
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from typing import Any, Dict |
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from flow_modules.aiflows.ChatFlowModule import ChatAtomicFlow |
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class PlanGeneratorAtomicFlow(ChatAtomicFlow): |
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"""This class wraps around the Chat API to generate plan from a goal. |
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*Input Interface Non Initialized*: |
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- `goal` |
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*Input Interface Initialized*: |
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- `goal` |
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*Output Interface*: |
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- `plan` |
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""" |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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self.hint_for_model = """ |
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Make sure your response is in the following format: |
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Response Format: |
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{ |
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"plan": "A step-by-step plan to finish the given goal, each step of plan should contain full information about writing a function", |
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} |
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""" |
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@classmethod |
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def instantiate_from_config(cls, config): |
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flow_config = deepcopy(config) |
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kwargs = {"flow_config": flow_config} |
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kwargs.update(cls._set_up_prompts(flow_config)) |
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kwargs.update(cls._set_up_backend(flow_config)) |
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return cls(**kwargs) |
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def _update_prompts_and_input(self, input_data: Dict[str, Any]): |
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if 'goal' in input_data: |
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input_data['goal'] += self.hint_for_model |
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def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]: |
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self._update_prompts_and_input(input_data) |
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while True: |
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api_output = super().run(input_data)["api_output"].strip() |
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try: |
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response = json.loads(api_output) |
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return response |
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except (json.decoder.JSONDecodeError, json.JSONDecodeError): |
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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." |
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new_input_data = input_data.copy() |
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new_input_data['goal'] = new_goal |
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input_data = new_input_data |