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import evoagentx.workflow.operators as operator
import examples.aflow.pubmedqa.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)
self.summary = operator.Custom(self.llm) # Added summary operator for better context
async def __call__(self, problem: str):
"""
Implementation of the workflow
"""
# Generate initial answer
solution = await self.answer_generate(input=problem)
solutions = [solution['answer']]
# Generate additional answers
for _ in range(2):
additional_solution = await self.answer_generate(input=problem)
solutions.append(additional_solution['answer'])
# Generate a summary of the answers for better context
summary_response = await self.summary(input=" ".join(solutions), instruction="Summarize these answers.")
# Use QAScEnsemble to select the best solution
ensemble_result = await self.qas_ensemble(solutions=solutions + [summary_response['response']])
return ensemble_result['response'] # Return the best selected answer
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