| | import evoagentx.workflow.operators as operator |
| | import examples.aflow.mbpp_new_full.optimized.round_6.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 |
| | """ |
| | solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT) |
| | test_result = await self.test(problem=problem, solution=solution['response'], entry_point=entry_point, benchmark=self.benchmark) |
| | if not test_result['result']: |
| | |
| | alternate_solutions = await self.custom(input=problem, instruction=prompt_custom.ALTERNATE_SOLUTIONS_PROMPT) |
| | selected_solution = await self.sc_ensemble(solutions=alternate_solutions['response'], problem=problem) |
| | revision_response = await self.custom(input=problem+f" Current Solution: {selected_solution['response']}", instruction=prompt_custom.REVISE_PROMPT) |
| | |
| | test_result_revised = await self.test(problem=problem, solution=revision_response['response'], entry_point=entry_point, benchmark=self.benchmark) |
| | if test_result_revised['result']: |
| | return revision_response['response'] |
| | else: |
| | return "Revised solution still failed the tests." |
| | return solution['response'] |
| |
|