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import evoagentx.workflow.operators as operator
import examples.aflow.humanevalplus_update.optimized.round_7.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) # Initialize the Test operator
self.sc_ensemble = operator.ScEnsemble(self.llm) # Initialize the ScEnsemble 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 solutions
solutions = []
for _ in range(3): # Generate three solutions for ensemble
solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
solutions.append(solution['response'])
# Review the solutions before selection
review_results = []
for solution in solutions:
review = await self.custom(input=solution, instruction=prompt_custom.REVIEW_SOLUTION_PROMPT)
review_results.append(review['response'])
# Use ScEnsemble to select the best solution based on reviews
best_solution = await self.sc_ensemble(solutions=review_results, problem=problem)
test_result = await self.test(problem=problem, solution=best_solution['response'], entry_point=entry_point, benchmark=self.benchmark) # Validate the solution
if test_result['result']:
return best_solution['response']
else:
return "Solution failed the tests." # Provide feedback if the solution fails
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