Remove Chinese comments and redundant code

metagpt/ext/spo/scripts/evaluator.py

@@ -13,7 +13,7 @@ from metagpt.ext.spo.scripts.utils.llm_client import SPO_LLM, extract_content
 
 class QuickExecute:
     """
-    完成不同数据集的评估。
+    Execute Prompt
     """
 
     def __init__(self, prompt: str):
@@ -41,7 +41,7 @@ class QuickExecute:
 
 class QuickEvaluate:
     """
-    Complete the evaluation for different datasets here.
+    Complete the evaluation for different answers here.
     """
 
     def __init__(self):
@@ -79,6 +79,5 @@ class QuickEvaluate:
 if __name__ == "__main__":
     execute = QuickExecute(prompt="Answer the Question，{question}")
 
-    # 使用asyncio.run来运行异步方法
     answers = asyncio.run(execute.prompt_evaluate())
     print(answers)

metagpt/ext/spo/scripts/optimizer.py

@@ -84,8 +84,6 @@ class Optimizer:
 
         logger.info(f"choose {sample['round']}")
 
-        prompt = sample['prompt']
-
         golden_answer = self.data_utils.list_to_markdown(qa)
         best_answer = self.data_utils.list_to_markdown(sample["answers"])
 
@@ -98,6 +96,9 @@ class Optimizer:
         response = await self.llm.responser(role="optimize", messages=[{"role": "user", "content": optimize_prompt}])
 
         modification = extract_content(response, "modification")
+
+        logger.info(f"Modification of this round: {modification}")
+
         prompt = extract_content(response, "prompt")
         if prompt:
             self.prompt = prompt

metagpt/ext/spo/scripts/utils/data_utils.py

@@ -70,39 +70,6 @@ class DataUtils:
 
         return self.top_scores
 
-    def set_file_name(name):
-        global FILE_NAME
-        FILE_NAME = name
-
-    def load_meta_data(k=SAMPLE_K):
-
-        # 读取 YAML 文件
-        config_path = os.path.join(os.path.dirname(__file__), '../settings', FILE_NAME)
-        with open(config_path, 'r', encoding='utf-8') as file:
-            data = yaml.safe_load(file)
-
-        qa = []
-
-        # 提取问题和答案
-        for item in data['faq']:
-            question = item['question']
-            answer = item['answer']
-            qa.append({'question': question, 'answer': answer})
-
-        prompt = data['prompt']
-        requirements = data['requirements']
-        count = data['count']
-
-        if isinstance(count, int):
-            count = f", within {count} words"
-        else:
-            count = ""
-
-        # 随机选择三组问答
-        random_qa = random.sample(qa, min(k, len(qa)))  # 确保不超过列表长度
-
-        return prompt, requirements, random_qa, count
-
     def list_to_markdown(self, questions_list):
         """
         Convert a list of question-answer dictionaries to a formatted Markdown string.

metagpt/ext/spo/scripts/utils/evaluation_utils.py

@@ -1,5 +1,3 @@
-import asyncio
-
 from metagpt.ext.spo.scripts.evaluator import QuickEvaluate, QuickExecute
 from metagpt.logs import logger
 import tiktoken

metagpt/ext/spo/scripts/utils/llm_client.py

@@ -55,23 +55,23 @@ def extract_content(xml_string, tag):
 
 
 async def spo():
-    # 在入口处初始化配置
+    # test LLM
     SPO_LLM.initialize(
-        optimize_kwargs={"model": "gpt-4o-mini", "temperature": 0.7},
+        optimize_kwargs={"model": "gpt-4o", "temperature": 0.7},
         evaluate_kwargs={"model": "gpt-4o-mini", "temperature": 0.3},
         execute_kwargs={"model": "gpt-4o-mini", "temperature": 0.3}
     )
 
     llm = SPO_LLM.get_instance()
 
-    # 测试消息
-    hello_msg = [{"role": "user", "content": "你是什么模型"}]
+    # test messages
+    hello_msg = [{"role": "user", "content": "hello"}]
     response = await llm.responser(role='execute', messages=hello_msg)
-    print(f"AI回复: {response}")
+    print(f"AI: {response}")
     response = await llm.responser(role='optimize', messages=hello_msg)
-    print(f"AI回复: {response}")
+    print(f"AI: {response}")
     response = await llm.responser(role='evaluate', messages=hello_msg)
-    print(f"AI回复: {response}")
+    print(f"AI: {response}")
 
 
 if __name__ == "__main__":

metagpt/ext/spo/scripts/utils/load.py

@@ -5,16 +5,6 @@ import os
 FILE_NAME = 'meta.yaml'
 SAMPLE_K = 3
 
-
-def load_llm():
-    # 读取上一级目录中的 YAML 配置文件
-    config_path = os.path.join(os.path.dirname(__file__), '..', 'config.yaml')
-    with open(config_path, 'r') as file:
-        config = yaml.safe_load(file)
-
-    return config
-
-
 def set_file_name(name):
     global FILE_NAME
     FILE_NAME = name
@@ -22,14 +12,13 @@ def set_file_name(name):
 
 def load_meta_data(k=SAMPLE_K):
 
-    # 读取 YAML 文件
+    # load yaml file
     config_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'settings', FILE_NAME)
     with open(config_path, 'r', encoding='utf-8') as file:
         data = yaml.safe_load(file)
 
     qa = []
 
-    # 提取问题和答案
     for item in data['faq']:
         question = item['question']
         answer = item['answer']
@@ -44,8 +33,7 @@ def load_meta_data(k=SAMPLE_K):
     else:
         count = ""
 
-    # 随机选择k组问答
-    random_qa = random.sample(qa, min(k, len(qa)))  # 确保不超过列表长度
+    random_qa = random.sample(qa, min(k, len(qa)))
 
     return prompt, requirements, random_qa, count
 

metagpt/ext/spo/scripts/utils/prompt_utils.py

@@ -1,9 +1,4 @@
-import json
 import os
-import re
-import time
-import traceback
-from typing import List
 from metagpt.logs import logger