chore(vault): backup 2026-07-03 21:40:41

2026-07-03 21:40:41 +08:00
parent 7635b2e377
commit f21c3a1662
4 changed files with 830 additions and 11 deletions
@@ -0,0 +1,199 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+真实 A 组实验（Two-Step 模式）- 使用真实 OpenAI API
+"""
+
+import json
+import os
+import requests
+from datetime import datetime
+import re
+
+# 配置
+base_dir = os.getcwd()
+experiment_dir = os.path.join(base_dir, "tools", "experiments", "wiki-generation-compare")
+source_file = os.path.join(base_dir, "raw", "呼吸之间_李谨伯", "第一编 从身体入手.md")
+output_dir = os.path.join(experiment_dir, "output", "group-a-real")
+
+# 创建输出目录
+os.makedirs(output_dir, exist_ok=True)
+os.makedirs(os.path.join(output_dir, "wiki", "concepts"), exist_ok=True)
+os.makedirs(os.path.join(output_dir, "wiki", "methods"), exist_ok=True)
+os.makedirs(os.path.join(output_dir, "wiki", "entities"), exist_ok=True)
+
+# 记录开始时间
+start_time = datetime.now()
+start_time_str = start_time.strftime("%Y-%m-%d %H:%M:%S")
+
+print("=== A 组真实实验开始：Two-Step 模式 ===")
+print(f"开始时间: {start_time_str}")
+
+# 检查 API Key
+api_key = os.environ.get('OPENAI_API_KEY')
+if not api_key:
+    print("[ERROR] 未找到 OPENAI_API_KEY 环境变量")
+    exit(1)
+
+headers = {
+    "Authorization": f"Bearer {api_key}",
+    "Content-Type": "application/json"
+}
+
+# Step 1: Analysis
+print("\nStep 1: Analysis 阶段...")
+
+# 读取源文件
+with open(source_file, 'r', encoding='utf-8') as f:
+    source_content = f.read()
+
+# 读取分析 prompt
+analysis_prompt_file = os.path.join(experiment_dir, "prompts", "twostep-analysis.md")
+with open(analysis_prompt_file, 'r', encoding='utf-8') as f:
+    analysis_template = f.read()
+
+analysis_full_prompt = analysis_template.replace("{SOURCE_CONTENT}", source_content)
+
+print("  调用 OpenAI API 进行分析...")
+analysis_body = {
+    "model": "gpt-4o-mini",
+    "messages": [
+        {
+            "role": "system",
+            "content": "你是一位知识库分析专家。请分析源文件并提取结构化信息，仅输出 JSON 格式。"
+        },
+        {
+            "role": "user",
+            "content": analysis_full_prompt
+        }
+    ],
+    "temperature": 0.3,
+    "max_tokens": 8000
+}
+
+try:
+    response = requests.post("https://api.openai.com/v1/chat/completions", 
+                            headers=headers, 
+                            json=analysis_body, 
+                            timeout=180)
+    response.raise_for_status()
+    analysis_result = response.json()['choices'][0]['message']['content']
+    
+    # 保存分析结果
+    analysis_output = os.path.join(output_dir, "analysis.json")
+    with open(analysis_output, 'w', encoding='utf-8') as f:
+        f.write(analysis_result)
+    
+    print(f"  [OK] Analysis 完成")
+    
+    # 解析 JSON
+    try:
+        gold_standard = json.loads(analysis_result)
+        recommended_count = len(gold_standard.get('recommended_pages', []))
+        print(f"  [OK] 识别到 {recommended_count} 个推荐页面")
+    except json.JSONDecodeError as e:
+        print(f"  [ERROR] JSON 解析失败: {e}")
+        print(f"  分析结果预览: {analysis_result[:200]}")
+        exit(1)
+    
+except Exception as e:
+    print(f"  [ERROR] LLM API 调用失败: {e}")
+    print("  退出实验")
+    exit(1)
+
+# Step 2: Generation
+print("\nStep 2: Generation 阶段...")
+
+# 读取生成 prompt
+generation_prompt_file = os.path.join(experiment_dir, "prompts", "twostep-generation.md")
+with open(generation_prompt_file, 'r', encoding='utf-8') as f:
+    generation_template = f.read()
+
+generation_full_prompt = generation_template.replace("{ANALYSIS_RESULT}", json.dumps(gold_standard, ensure_ascii=False, indent=2))
+
+print("  调用 OpenAI API 生成 Wiki 页面...")
+generation_body = {
+    "model": "gpt-4o-mini",
+    "messages": [
+        {
+            "role": "system",
+            "content": "你是一位知识库构建专家。请基于分析结果生成 Wiki 页面。"
+        },
+        {
+            "role": "user",
+            "content": generation_full_prompt
+        }
+    ],
+    "temperature": 0.5,
+    "max_tokens": 16000
+}
+
+try:
+    response = requests.post("https://api.openai.com/v1/chat/completions", 
+                            headers=headers, 
+                            json=generation_body, 
+                            timeout=300)
+    response.raise_for_status()
+    generation_result = response.json()['choices'][0]['message']['content']
+    
+    print("  [OK] LLM 生成完成")
+    
+except Exception as e:
+    print(f"  [ERROR] LLM API 调用失败: {e}")
+    exit(1)
+
+# 解析生成结果
+sample_pages = []
+
+# 解析 ---FILE: ... ---END FILE--- 块
+file_blocks = re.findall(r'---FILE: (.*?)---(.*?)---END FILE---', generation_result, re.DOTALL)
+
+if not file_blocks:
+    # 尝试宽松匹配
+    file_blocks = re.findall(r'FILE: (.*?)\n(.*?)(?=(FILE:|$))', generation_result, re.DOTALL)
+
+for block in file_blocks:
+    file_path = block[0].strip()
+    file_content = block[1].strip()
+    
+    # 创建目录（如果需要）
+    full_path = os.path.join(output_dir, file_path)
+    file_dir = os.path.dirname(full_path)
+    os.makedirs(file_dir, exist_ok=True)
+    
+    # 写入文件
+    with open(full_path, 'w', encoding='utf-8') as f:
+        f.write(file_content)
+    sample_pages.append(full_path)
+    print(f"  [OK] 生成: {file_path}")
+
+# 记录结束时间
+end_time = datetime.now()
+end_time_str = end_time.strftime("%Y-%m-%d %H:%M:%S")
+duration = (end_time - start_time).total_seconds() / 60
+
+print(f"\n=== A 组实验完成 ===")
+print(f"结束时间: {end_time_str}")
+print(f"总耗时: {round(duration, 2)} 分钟")
+print(f"生成文件数: {len(sample_pages)}")
+
+# 保存元数据
+metadata = {
+    "group": "A-real",
+    "mode": "Two-Step (Real API)",
+    "sourceFile": "raw/呼吸之间_李谨伯/第一编 从身体入手.md",
+    "startTime": start_time_str,
+    "endTime": end_time_str,
+    "durationMinutes": round(duration, 2),
+    "step1Status": "completed",
+    "step2Status": "completed",
+    "pagesGenerated": len(sample_pages),
+    "apiModel": "gpt-4o-mini",
+    "apiCost": f"Analysis (8K tokens) + Generation (16K tokens) ≈ ${round(0.15 * 24 / 1000000, 4)}"
+}
+
+metadata_file = os.path.join(output_dir, "metadata.json")
+with open(metadata_file, 'w', encoding='utf-8') as f:
+    json.dump(metadata, f, ensure_ascii=False, indent=2)
+
+print(f"[OK] 元数据已保存到 {metadata_file}")