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import evoagentx.workflow.operators as operator
import examples.aflow.molqa.optimized_molqa.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.ensemble = operator.QAScEnsemble(self.llm) # Added QAScEnsemble for better solution selection
async def __call__(self, problem: str):
"""
Implementation of the workflow
"""
# Generate multiple solutions
solutions = []
for _ in range(3): # Generate three solutions for self-consistency
solution = await self.answer_generate(input=problem)
solutions.append(solution['answer'])
# Use ensemble to select the best solution
ensemble_result = await self.ensemble.sc_ensemble(solutions)
# Review the selected answer before final output
review_input = f"Review this answer: {ensemble_result['response']}"
review_response = await self.custom(input=review_input, instruction=prompt_custom.REVIEW_PROMPT)
return review_response['response'] # Return the reviewed answer
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