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import evoagentx.workflow.operators as operator
import examples.aflow.scicode.optimized.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.custom_code_generate = operator.CustomCodeGenerate(self.llm)
self.test = operator.Test(self.llm)
self.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 initial solutions
solutions = []
for _ in range(3): # Generating three solutions
response = await self.custom(input=problem + " Generate a solution, ensure it is functional.", instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
solutions.append(response['response'])
# Test the solutions for errors
test_results = []
for solution in solutions:
test_result = await self.test(problem=problem, solution=solution, entry_point=entry_point, benchmark=self.benchmark)
test_results.append(test_result)
# Select best solution using ensemble if errors detected
successful_solutions = [result['solution'] for result in test_results if result['result']]
if successful_solutions:
return await self.ensemble(solutions=successful_solutions, problem=problem) # Applying ensemble on successful solutions
return "No valid solutions found." # Handling case where no tests pass
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