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import evoagentx.workflow.operators as operator
import examples.aflow.scicode_full.optimized.round_4.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 = operator.Test(self.llm) # Initialized the test operator
self.sc_ensemble = operator.ScEnsemble(self.llm) # Added ensemble operator
async def __call__(self, problem: str, entry_point: str):
"""
Implementation of the workflow
Custom operator to generate anything you want.
But when you want to get standard code, you should use custom_code_generate operator.
"""
# Generate multiple potential solutions
solution_1 = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
solution_2 = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT) # Generating a second solution
solutions = [solution_1['response'], solution_2['response']] # Collecting both solutions
# Use ScEnsemble to select the best solution from the generated solutions
best_solution = await self.sc_ensemble.sc_ensemble(solutions=solutions, problem=problem)
# Test the ensemble solution
test_result = await self.test_operator.test(problem=problem, solution=best_solution['response'], entry_point=entry_point, benchmark=self.benchmark)
if not test_result['result']:
return {"success": False, "current_solution": test_result['solution']}
return {"success": True, "final_solution": best_solution['response']} # Return the final verified solution
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