| from .base_agent import BaseAgent |
| from prompt.template import PROBLEM_MODELING_PROMPT, PROBLEM_MODELING_CRITIQUE_PROMPT, PROBLEM_MODELING_IMPROVEMENT_PROMPT |
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| class ProblemModeling(BaseAgent): |
| def __init__(self, llm): |
| super().__init__(llm) |
| |
| def modeling_actor(self, modeling_problem: str, problem_analysis: str, modeling_methods: str, user_prompt: str=''): |
| prompt = PROBLEM_MODELING_PROMPT.format(modeling_methods=modeling_methods, modeling_problem=modeling_problem, problem_analysis=problem_analysis, user_prompt=user_prompt).strip() |
| return self.llm.generate(prompt) |
|
|
| def modeling_critic(self, modeling_problem: str, problem_analysis: str, modeling_solution: str): |
| prompt = PROBLEM_MODELING_CRITIQUE_PROMPT.format(modeling_problem=modeling_problem, problem_analysis=problem_analysis, modeling_solution=modeling_solution).strip() |
| return self.llm.generate(prompt) |
|
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| def modeling_improvement(self, modeling_problem: str, problem_analysis: str, modeling_solution: str, modeling_solution_critique: str, user_prompt: str=''): |
| prompt = PROBLEM_MODELING_IMPROVEMENT_PROMPT.format(modeling_problem=modeling_problem, problem_analysis=problem_analysis, modeling_solution=modeling_solution, modeling_solution_critique=modeling_solution_critique, user_prompt=user_prompt).strip() |
| return self.llm.generate(prompt) |
|
|
| def modeling(self, modeling_problem: str, problem_analysis: str, modeling_methods: str, round: int = 3, user_prompt: str = ''): |
| modeling_solution = self.modeling_actor(modeling_problem, problem_analysis, modeling_methods, user_prompt) |
| for i in range(round): |
| print(f'Problem Modeling Round {i+1}') |
| modeling_solution_critique = self.modeling_critic(modeling_problem, problem_analysis, modeling_solution) |
| modeling_solution_improvement = self.modeling_improvement(modeling_problem, problem_analysis, modeling_solution, modeling_solution_critique, user_prompt) |
| modeling_solution = modeling_solution_improvement |
| return modeling_solution |