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import evoagentx.workflow.operators as operator
import examples.aflow.humaneval.optimized.round_6.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.custom_code_generate = operator.CustomCodeGenerate(self.llm)
self.test = operator.Test(self.llm)
self.ensemble = operator.ScEnsemble(self.llm) # New operator added for selecting the best solution
async def __call__(self, problem: str, entry_point: str):
solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
# Testing the solution before returning
test_result = await self.test(problem=problem, solution=solution['response'], entry_point=entry_point, benchmark=self.benchmark)
if test_result['result']:
return test_result['solution']
else:
# Implementing ensemble for generating alternative solutions and improving selection
alternative_solutions = await self.custom(problem=problem, instruction=prompt_custom.GENERATE_ALTERNATIVE_SOLUTIONS_PROMPT)
final_solution = await self.ensemble(solutions=[test_result['solution']] + alternative_solutions['response'], problem=problem)
return final_solution['response'] # Return the best aggregated solution based on ensemble
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