| | import evoagentx.workflow.operators as operator |
| | import examples.aflow.mbpp_new_full.optimized.round_8.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) |
| | self.revise_custom = operator.Custom(self.llm) |
| |
|
| | async def __call__(self, problem: str, entry_point: str): |
| | solution_candidates = [] |
| | for _ in range(3): |
| | try: |
| | solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT) |
| | if len(solution_candidates) < 5: |
| | solution_candidates.append(solution['response']) |
| | except Exception as e: |
| | print(f"Error during code generation: {e}") |
| |
|
| | final_solution = await self.sc_ensemble(solutions=solution_candidates, 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']: |
| | revision_response = await self.revise_custom(input=problem + " Current Solution: " + final_solution['response'], instruction=prompt_custom.REVISE_PROMPT) |
| | return revision_response['response'] |
| | return final_solution['response'] |
| |
|
