| | 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." |
| |
|