| import evoagentx.workflow.operators as operator |
| import examples.aflow.scicode.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) |
| self.ensemble = operator.ScEnsemble(self.llm) |
|
|
| 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. |
| """ |
| |
| solutions = [] |
| for _ in range(3): |
| response = await self.custom(input=problem + " Generate a solution, ensure it is functional.", instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT) |
| solutions.append(response['response']) |
|
|
| |
| test_results = [] |
| for solution in solutions: |
| test_result = await self.test(problem=problem, solution=solution, entry_point=entry_point, benchmark=self.benchmark) |
| test_results.append(test_result) |
|
|
| |
| successful_solutions = [result['solution'] for result in test_results if result['result']] |
| if successful_solutions: |
| return await self.ensemble(solutions=successful_solutions, problem=problem) |
|
|
| return "No valid solutions found." |
|
|