MetaGPT/optimize.py

# -*- coding: utf-8 -*-
# @Date    : 8/23/2024 20:00 PM
# @Author  : didi
# @Desc    : Entrance of AFlow.

from examples.aflow.scripts.optimizer import Optimizer
from examples.aflow.data.download_data import download
from metagpt.configs.models_config import ModelsConfig
from typing import Literal

# DatasetType, QuestionType, and OptimizerType definitions
DatasetType = Literal["HumanEval", "MBPP", "GSM8K", "MATH", "HotpotQA", "DROP"]
QuestionType = Literal["math", "code", "qa"]
OptimizerType = Literal["Graph", "Test"]

# When you fisrt use, please download the datasets and initial rounds; If you want to get a look of the results, please download the results.
# download(["datasets", "results", "initial_rounds"])

# Crucial Parameters
dataset: DatasetType = "GSM8K"  # Ensure the type is consistent with DatasetType
sample: int = 4  # Sample Count, which means how many workflows will be resampled from generated workflows
question_type: QuestionType = "code"  # Ensure the type is consistent with QuestionType
optimized_path: str = "examples/aflow/scripts/optimized"  # Optimized Result Save Path
initial_round: int = 1  # Corrected the case from Initial_round to initial_round
max_rounds: int = 20
check_convergence: bool = True

# Config llm model, you can modify `config/config2.yaml` to use more llms.
mini_llm_config = ModelsConfig.default().get("gpt-4o-mini")
claude_llm_config = ModelsConfig.default().get("claude-3-5-sonnet-20240620")

# Config operators.
operators = [
    "Custom",                       # It's basic unit of a fixed node. optimizer can modify its prompt to get vairous nodes.
    # "AnswerGenerate"              # It's for qa
    # "CustomCodeGenerate",         # It's for code
    "ScEnsemble",                 # It's for code, math and qa
    # "Test",                       # It's for code
    "Programmer",                 # It's for math 
]

# Create an optimizer instance
optimizer = Optimizer(
    dataset=dataset,                        # Config dataset   
    question_type=question_type,            # Config Question Type
    opt_llm_config=claude_llm_config,       # Config Optimizer LLM
    exec_llm_config=mini_llm_config,        # Config Execution LLM
    check_convergence=check_convergence,    # Whether Early Stop 
    operators=operators,                    # Config Operators you want to use
    optimized_path=optimized_path,          # Config Optimized workflow's file path
    sample=sample,                          # Only Top(sample) rounds will be selected. 
    initial_round=initial_round,            # Optimize from initial round
    max_rounds=max_rounds                   # The max iteration of AFLOW.
)

if __name__ == "__main__":
    # Optimize workflow via setting the optimizer's mode to 'Graph'
    optimizer.optimize("Graph")
    # Test workflow via setting the optimizer's mode to 'Test'
    # optimizer.optimize("Test")