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import evoagentx.workflow.operators as operator
import examples.aflow.pubmedxqa.optimized.round_13.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) # Initialize QAScEnsemble operator
async def __call__(self, problem: str):
"""
Implementation of the workflow
"""
# Generate multiple answers for the problem
solutions = [await self.answer_generate(input=problem) for _ in range(5)] # Generate 5 answers
# Aggregate the answers using QAScEnsemble
ensemble_response = await self.qas_ensemble(solutions=[sol['answer'] for sol in solutions])
# Use custom method to refine the final answer with additional context
refined_response = await self.custom(input=problem + " Context: " + ensemble_response['response'], instruction="Refine the answer: ")
return refined_response['response'] # Return the refined answer
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