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import evoagentx.workflow.operators as operator
import examples.aflow.pubmedxqa.optimized.round_19.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(10)] # Generate 10 answers
# Aggregate the answers using QAScEnsemble
ensemble_response = await self.qas_ensemble(solutions=[sol['answer'] for sol in solutions])
# Self-consistency check
if ensemble_response['response'] in [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 based on the provided context.")
else:
# If no consistency, return ensemble response and add a note for further review
refined_response = {'response': ensemble_response['response'] + " Note: Further review may be needed."}
return refined_response['response'] # Return the refined answer
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