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5374a2d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | import evoagentx.workflow.operators as operator
import examples.aflow.mbpp_new_full.optimized.round_9.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.sc_ensemble = operator.ScEnsemble(self.llm)
self.revise_custom = operator.Custom(self.llm) # Added to allow for contextual revising
async def __call__(self, problem: str, entry_point: str):
solution_candidates = []
for _ in range(3):
try:
solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
solution_candidates.append(solution['response'])
except Exception as e:
# Handle or log error details as needed
print(f"Error during code generation: {e}")
# Validate each solution candidate before proceeding to ensemble
validated_solutions = []
for candidate in solution_candidates:
test_result = await self.test(problem=problem, solution=candidate, entry_point=entry_point, benchmark=self.benchmark)
if test_result['result']:
validated_solutions.append(candidate)
final_solution = await self.sc_ensemble(solutions=validated_solutions, problem=problem)
if not final_solution:
revision_response = await self.revise_custom(input=problem + " Current Solution: " + final_solution['response'], instruction=prompt_custom.REVISE_PROMPT)
return revision_response['response']
return final_solution['response']
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