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didi 2024-10-22 12:37:08 +08:00
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commit 56d0af1e9e
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examples/aflow/optimize.py
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@ -3,11 +3,10 @@
# @Author : didi
# @Desc : Entrance of AFlow.
from metagpt.ext.aflow.scripts.optimizer import Optimizer
from metagpt.ext.aflow.scripts.optimizer import DatasetType, QuestionType, OptimizerType
from metagpt.ext.aflow.data.download_data import download
from metagpt.configs.models_config import ModelsConfig
from typing import Literal
from metagpt.ext.aflow.data.download_data import download
from metagpt.ext.aflow.scripts.optimizer import DatasetType, Optimizer, QuestionType
# DatasetType, QuestionType, and OptimizerType definitions
# DatasetType = Literal["HumanEval", "MBPP", "GSM8K", "MATH", "HotpotQA", "DROP"]
@ -32,30 +31,30 @@ 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.
"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
"ScEnsemble", # It's for code, math and qa
# "Test", # It's for code
"Programmer", # It's for math
"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.
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")
# optimizer.optimize("Test")