| import evoagentx.workflow.operators as operator |
| import examples.aflow.livecodebench.optimized.round_1.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): |
| """ |
| Implementation of the workflow |
| Custom operator to generate initial insights about the problem. |
| """ |
| insights = await self.custom(input=f"The following coding problem is provided: {problem}. Please provide detailed insights, including potential pitfalls, testing strategies, and relevant examples to clarify the approach. ", instruction="Provide enhanced insights for the problem.") |
|
|
| solutions = [] |
| for _ in range(5): |
| solution = await self.custom_code_generate(problem=problem+f" Insights:{insights['response']}", entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT) |
| solutions.append(solution['response']) |
|
|
| best_solution = await self.sc_ensemble(solutions=solutions, problem=problem) |
|
|
| test_result = await self.test(problem=problem, solution=best_solution['response'], entry_point=entry_point, benchmark=self.benchmark) |
| if not test_result['result']: |
| specific_feedback = f"Solution failed for the problem: {problem}. Errors encountered: {test_result['solution']}" |
| return specific_feedback |
|
|
| return best_solution['response'] |
|
|
