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import evoagentx.workflow.operators as operator
import examples.aflow.pubmedqa.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.answer_generate = operator.AnswerGenerate(self.llm)
self.qas_ensemble = operator.QAScEnsemble(self.llm) # Added QAScEnsemble operator
async def __call__(self, problem: str):
"""
Implementation of the workflow
"""
solution = await self.answer_generate(input=problem)
# Collect multiple solutions for ensemble
solutions = [solution['answer']] # Store the initial answer
# Generate additional answers for better selection
for _ in range(2): # Generate two more answers
additional_solution = await self.answer_generate(input=problem)
solutions.append(additional_solution['answer'])
# Use QAScEnsemble to select the best solution
ensemble_result = await self.qas_ensemble(solutions=solutions)
return ensemble_result['response'] # Return the best selected answer
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