| import evoagentx.workflow.operators as operator |
| import examples.aflow.mbpp_new.optimized.round_7.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) |
| self.sc_ensemble = operator.ScEnsemble(self.llm) |
|
|
| async def __call__(self, problem: str, entry_point: str): |
| solution_list = [] |
| error_logs = [] |
| for attempt in range(3): |
| solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT) |
| if 'error' in solution: |
| error_logs.append(solution['error']) |
| continue |
| solution_list.append(solution['response']) |
| |
| if not solution_list: |
| return "All attempts failed. Errors: " + ', '.join(error_logs) |
|
|
| final_solution = await self.sc_ensemble(solutions=solution_list, problem=problem) |
|
|
| test_result = await self.test(problem=problem, solution=final_solution['response'], entry_point=entry_point, benchmark=self.benchmark) |
| if not test_result['result']: |
| modifications = await self.custom(input=final_solution['response'], instruction=prompt_custom.MODIFY_CODE_PROMPT) |
| return modifications['response'] |
| |
| return final_solution['response'] |
|
|
