| import evoagentx.workflow.operators as operator |
| import examples.aflow.molqa.optimized_molqa.round_9.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.answer_generate = operator.AnswerGenerate(self.llm) |
| self.sc_ensemble = operator.QAScEnsemble(self.llm) |
| self.review = operator.Custom(self.llm) |
| |
| async def __call__(self, problem: str): |
| """ |
| Implementation of the workflow |
| """ |
| solution = await self.answer_generate(input=problem) |
| |
| validation = await self.custom(input=solution['answer'], instruction="Validate the following answer for correctness:") |
| if validation['response'] == "valid": |
| review = await self.review(input=solution['answer'], instruction="Review the following answer for accuracy:") |
| ensemble_response = await self.sc_ensemble(solutions=[solution['answer'], review['response']]) |
| return ensemble_response['response'] |
| else: |
| return "The generated answer is not valid." |
|
|
