217 lines
6.9 KiB
Python
217 lines
6.9 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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使用 singlestep 提示词对测试文件生成 WIKI
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使用环境变量中的 OPENAI_BASE_URL (阿里云 dashscope)
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"""
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import os
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import re
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import json
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from datetime import datetime
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import openai
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# 配置
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base_dir = os.getcwd()
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experiment_dir = os.path.join(base_dir, "tools", "experiments", "wiki-generation-compare")
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source_file = os.path.join(base_dir, "raw", "呼吸之间_李谨伯", "第一编 从身体入手.md")
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output_dir = os.path.join(experiment_dir, "output", "group-b-real")
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prompt_file = os.path.join(experiment_dir, "prompts", "singlestep.md")
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# 创建输出目录
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os.makedirs(output_dir, exist_ok=True)
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os.makedirs(os.path.join(output_dir, "wiki", "concepts"), exist_ok=True)
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os.makedirs(os.path.join(output_dir, "wiki", "methods"), exist_ok=True)
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os.makedirs(os.path.join(output_dir, "wiki", "entities"), exist_ok=True)
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# 记录开始时间
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start_time = datetime.now()
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start_time_str = start_time.strftime("%Y-%m-%d %H:%M:%S")
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print("=== B 组测试：Single-Step 模式 ===")
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print(f"开始时间: {start_time_str}")
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# 读取源文件
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print(f"\n读取源文件: {source_file}")
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with open(source_file, 'r', encoding='utf-8') as f:
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source_content = f.read()
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print(f" 源文件长度: {len(source_content)} 字符")
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# 读取 prompt
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print(f"\n读取 prompt: {prompt_file}")
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with open(prompt_file, 'r', encoding='utf-8') as f:
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template = f.read()
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# 替换 {SOURCE_CONTENT}
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full_prompt = template.replace("{SOURCE_CONTENT}", source_content)
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print(f" Prompt 长度: {len(full_prompt)} 字符")
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# 检查 API Key 和 Base URL
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api_key = os.environ.get('OPENAI_API_KEY')
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base_url = os.environ.get('OPENAI_BASE_URL', 'https://api.openai.com/v1')
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if not api_key:
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print("[ERROR] 未找到 OPENAI_API_KEY 环境变量")
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exit(1)
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print(f" API Key 长度: {len(api_key)}")
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print(f" Base URL: {base_url}")
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# 初始化客户端
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print("\n初始化 OpenAI 客户端...")
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client = openai.OpenAI(
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api_key=api_key,
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base_url=base_url,
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timeout=300
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)
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# 阿里云 dashscope 兼容的模型名称
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models_to_try = [
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"qwen-plus", # 通义千问 Plus
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"qwen-turbo", # 通义千问 Turbo
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"qwen-max", # 通义千问 Max
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"qwen-long", # 通义千问 Long
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"deepseek-r1", # DeepSeek R1
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"deepseek-v3", # DeepSeek V3
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"openai/gpt-4o-mini", # 兼容 OpenAI 模型
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"gpt-4o-mini",
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"openai/gpt-3.5-turbo",
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"gpt-3.5-turbo"
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]
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# 尝试获取可用模型
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print("\n查找可用模型...")
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selected_model = None
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for model_name in models_to_try:
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try:
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# 先尝试简单的 chat completion
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test_response = client.chat.completions.create(
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model=model_name,
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messages=[{"role": "user", "content": "hi"}],
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max_tokens=5
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)
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print(f" [OK] 模型可用: {model_name}")
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selected_model = model_name
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break
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except Exception as e:
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error_msg = str(e)[:100]
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print(f" [X] {model_name}: {error_msg}")
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if not selected_model:
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print("\n[ERROR] 所有模型都不可用，尝试使用 models 端点列出可用模型...")
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try:
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models = client.models.list()
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print(f" 可用模型: {[m.id for m in models.data[:10]]}")
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if models.data:
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selected_model = models.data[0].id
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print(f" 使用第一个模型: {selected_model}")
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except Exception as e:
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print(f" [ERROR] 无法列出模型: {e}")
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exit(1)
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if not selected_model:
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print("[ERROR] 未找到可用模型")
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exit(1)
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print(f"\n使用模型: {selected_model}")
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# 调用 LLM 生成
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print("\n调用 LLM 生成 Wiki 页面...")
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try:
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response = client.chat.completions.create(
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model=selected_model,
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messages=[
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{
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"role": "system",
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"content": "你是一位知识库构建专家。请直接从源文件中提取关键术语并生成 Wiki 页面。"
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},
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{
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"role": "user",
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"content": full_prompt
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}
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],
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temperature=0.5,
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max_tokens=16000
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)
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generation_result = response.choices[0].message.content
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print(f" [OK] LLM 生成完成")
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print(f" 生成内容长度: {len(generation_result)} 字符")
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# 保存原始生成结果
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raw_output_file = os.path.join(output_dir, "raw_generation.md")
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with open(raw_output_file, 'w', encoding='utf-8') as f:
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f.write(generation_result)
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print(f" [OK] 原始生成结果已保存到: {raw_output_file}")
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except Exception as e:
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print(f" [ERROR] LLM API 调用失败: {type(e).__name__}: {e}")
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exit(1)
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# 解析生成结果
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print("\n解析生成的 Wiki 页面...")
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sample_pages = []
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# 解析 ---FILE: ... ---END FILE--- 块
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file_blocks = re.findall(r'---FILE: (.*?)---(.*?)---END FILE---', generation_result, re.DOTALL)
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if not file_blocks:
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print(" [WARNING] 未找到标准文件块格式，尝试宽松匹配...")
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file_blocks = re.findall(r'FILE: (.*?)\n(.*?)(?=(?:FILE:|$))', generation_result, re.DOTALL)
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print(f" 解析到 {len(file_blocks)} 个文件块")
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for i, block in enumerate(file_blocks):
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if isinstance(block, tuple):
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file_path = block[0].strip()
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file_content = block[1].strip()
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else:
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continue
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# 创建目录（如果需要）
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full_path = os.path.join(output_dir, file_path)
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file_dir = os.path.dirname(full_path)
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os.makedirs(file_dir, exist_ok=True)
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# 写入文件
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with open(full_path, 'w', encoding='utf-8') as f:
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f.write(file_content)
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sample_pages.append(full_path)
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print(f" [OK] 生成 [{i+1}/{len(file_blocks)}]: {file_path}")
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# 记录结束时间
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end_time = datetime.now()
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end_time_str = end_time.strftime("%Y-%m-%d %H:%M:%S")
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duration = (end_time - start_time).total_seconds() / 60
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print(f"\n=== B 组测试完成 ===")
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print(f"结束时间: {end_time_str}")
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print(f"总耗时: {round(duration, 2)} 分钟")
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print(f"生成文件数: {len(sample_pages)}")
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# 保存元数据
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metadata = {
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"group": "B-real",
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"mode": "Single-Step",
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"sourceFile": "raw/呼吸之间_李谨伯/第一编 从身体入手.md",
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"startTime": start_time_str,
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"endTime": end_time_str,
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"durationMinutes": round(duration, 2),
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"pagesGenerated": len(sample_pages),
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"model": selected_model,
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"baseUrl": base_url,
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"rawContentLength": len(generation_result),
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"status": "success"
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}
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metadata_file = os.path.join(output_dir, "metadata.json")
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with open(metadata_file, 'w', encoding='utf-8') as f:
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json.dump(metadata, f, ensure_ascii=False, indent=2)
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print(f"\n[OK] 元数据已保存到: {metadata_file}")
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print(f"\n输出文件位置:")
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print(f" 原始生成: {raw_output_file}")
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print(f" Wiki 页面: {output_dir}/wiki/")
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print(f" 元数据: {metadata_file}")
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print(f"\n接下来可以运行评估脚本对比 A 组和 B 组的结果：")
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print(f" python tools/experiments/wiki-generation-compare/scripts/evaluate-real.py") |