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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`
*Configuration Parameters*:
- Also refer to ChatAtomicFlow (https://huggingface.co/aiflows/ChatFlowModule/blob/main/ChatAtomicFlow.py)
- `input_interface_non_initialized`: The input interface when the conversation is not initialized.
- `input_interface_initialized`: The input interface when the conversation is initialized.
- `output_interface`: The output interface.
- `backend`: The backend to use for the Chat API.
- `system_message_prompt_template`: The template for the system message prompt.
- `human_message_prompt_template`: The template for the human message prompt.
- `init_human_message_prompt_template`: The initial human message prompt.
"""
def __init__(self, **kwargs):
"""
This function instantiates the class.
:param kwargs: The configuration parameters.
:type kwargs: Dict[str, Any]
"""
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):
"""
This function instantiates the class from a configuration.
:param config: The configuration.
:type config: Dict[str, Any]
:return: The instantiated class.
:rtype: ChatAtomicFlow
"""
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]):
"""
This function updates the prompts and input data.
:param input_data: The input data.
:type input_data: Dict[str, Any]
:return: None
:rtype: None
"""
if 'goal' in input_data:
input_data['goal'] += self.hint_for_model
def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
"""
This function runs the flow.
:param input_data: The input data.
:type input_data: Dict[str, Any]
:return: The output data.
:rtype: 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 |