import evoagentx.workflow.operators as operator import examples.aflow.mbpp_new.optimized.round_2.prompt as prompt_custom from evoagentx.models.model_configs import LLMConfig from evoagentx.benchmark.benchmark import Benchmark from evoagentx.models.model_utils import create_llm_instance class Workflow: def __init__( self, name: str, llm_config: LLMConfig, benchmark: Benchmark ): self.name = name self.llm = create_llm_instance(llm_config) self.benchmark = benchmark self.custom = operator.Custom(self.llm) self.custom_code_generate = operator.CustomCodeGenerate(self.llm) self.test = operator.Test(self.llm) # Keeping testing functionality self.sc_ensemble = operator.ScEnsemble(self.llm) # Adding ensemble method async def __call__(self, problem: str, entry_point: str): """ Implementation of the workflow Custom operator to generate anything you want. But when you want to get standard code, you should use custom_code_generate operator. """ solution_list = [] for _ in range(3): # Generate multiple solutions for better results solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT) solution_list.append(solution['response']) # Voting mechanism to find the best solution using ensemble final_solution = await self.sc_ensemble(solutions=solution_list, problem=problem) # Testing the selected solution test_result = await self.test(problem=problem, solution=final_solution['response'], entry_point=entry_point, benchmark=self.benchmark) if not test_result['result']: # Generate modifications if test fails modifications = await self.custom(input=final_solution['response'], instruction=prompt_custom.MODIFY_CODE_PROMPT) return modifications['response'] return final_solution['response']