修复了手机端无法双击的问题

.env.development

@@ -1,2 +1,3 @@
 VITE_TITLE=练习伴侣之八股宝典
-VITE_DATA_URL=http://fusb.top/data/java/result.json
+# VITE_DATA_URL=https://fusb.top/data/java/result.json
+VITE_DATA_URL=./result.json

.env.production

@@ -1,2 +1,3 @@
 VITE_TITLE=练习伴侣之八股宝典
-VITE_DATA_URL=http://fusb.top/data/java/result.json
+# VITE_DATA_URL=https://fusb.top/data/java/result.json
+VITE_DATA_URL=./result.json

merge_questions.py

@@ -0,0 +1,158 @@
+import json
+import re
+import os
+
+def parse_markdown_qa(file_path):
+    """解析大模型基础理论.md，提取有答案的问答"""
+    with open(file_path, 'r', encoding='utf-8') as f:
+        content = f.read()
+
+    questions = []
+    # 匹配 #### Qxx: 问题 ...
+    pattern = r'#### (Q\d+:.*?)(?=#### Q\d+:|$)'
+    matches = re.findall(pattern, content, re.DOTALL)
+
+    for i, block in enumerate(matches):
+        # 分离问题和内容
+        q_match = re.match(r'Q\d+:(.*?)\n', block)
+        if not q_match:
+            continue
+
+        q_text = q_match.group(1).strip()
+        # 剩余部分作为答案候选
+        answer_candidate = block[q_match.end():].strip()
+
+        # 过滤条件：必须包含“回答要点”、“标准答案”或“核心考点”之一
+        if any(keyword in answer_candidate for keyword in ["回答要点", "标准答案", "核心考点"]):
+            questions.append({
+                "id": i + 1,
+                "question": q_text,
+                "answer": answer_candidate
+            })
+
+    return questions
+
+def parse_chat_txt(file_path):
+    """解析 rag开发.txt 和 agent开发.txt"""
+    with open(file_path, 'r', encoding='utf-8') as f:
+        content = f.read()
+
+    questions = []
+    # 匹配 ## user 和 ## assistant 块
+    user_blocks = re.findall(r'## user\n(.*?)\n## assistant', content, re.DOTALL)
+    assistant_blocks = re.findall(r'## assistant\n(.*?)(?=\n## user|$)', content, re.DOTALL)
+
+    for i, (u, a) in enumerate(zip(user_blocks, assistant_blocks)):
+        questions.append({
+            "id": i + 1,
+            "question": u.strip(),
+            "answer": a.strip()
+        })
+
+    return questions
+
+def create_module(topic_name, questions, start_id=100):
+    """创建符合 result.json 结构的模块"""
+    # 简单地将所有问题放在一个分类下，或者根据问题内容进一步分类
+    # 这里为了简单，先放在一个通用分类下，或者尝试根据问题关键词分类
+    # 考虑到用户要求“分成三个模块”，我们可以每个文件一个大模块
+
+    module = {
+        "id": start_id,
+        "topicName": topic_name,
+        "categories": [
+            {
+                "id": start_id + 1,
+                "categoryName": "核心知识点",
+                "questions": questions
+            }
+        ]
+    }
+    return module
+
+def main():
+    base_dir = r'd:\program_github\practice-mate\public'
+    result_json_path = os.path.join(base_dir, 'result.json')
+
+    # 1. 读取现有数据
+    with open(result_json_path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+
+    # 清理可能存在的旧模块，防止重复添加
+    topics_to_remove = ["AI 大模型与 Agent 开发", "大模型基础理论", "RAG 开发实战", "Agent 开发实战"]
+    data = [item for item in data if item.get('topicName') not in topics_to_remove]
+
+    current_max_id = max([item.get('id', 0) for item in data]) if data else 0
+
+    all_questions = []
+    q_id_counter = 1
+
+    # 2. 处理大模型基础理论
+    md_path = os.path.join(base_dir, '大模型基础理论.md')
+    md_questions = parse_markdown_qa(md_path)
+    for q in md_questions:
+        q['id'] = q_id_counter
+        q_id_counter += 1
+        all_questions.append(q)
+    print(f"提取大模型理论问答: {len(md_questions)} 条")
+
+    # 3. 处理 RAG 开发
+    rag_path = os.path.join(base_dir, 'rag开发-20260405153109.txt')
+    rag_questions = parse_chat_txt(rag_path)
+    for q in rag_questions:
+        q['id'] = q_id_counter
+        q_id_counter += 1
+        all_questions.append(q)
+    print(f"提取 RAG 开发问答: {len(rag_questions)} 条")
+
+    # 4. 处理 Agent 开发
+    agent_path = os.path.join(base_dir, 'agent开发-20260405201455.txt')
+    agent_questions = parse_chat_txt(agent_path)
+    for q in agent_questions:
+        q['id'] = q_id_counter
+        q_id_counter += 1
+        all_questions.append(q)
+    print(f"提取 Agent 开发问答: {len(agent_questions)} 条")
+
+    # 5. 创建合并后的模块（包含三个分类）
+    if all_questions:
+        # 重新分配 ID，确保每个分类下的问题 ID 从 1 开始连续
+        md_qs = all_questions[:len(md_questions)]
+        rag_qs = all_questions[len(md_questions):len(md_questions)+len(rag_questions)]
+        agent_qs = all_questions[len(md_questions)+len(rag_questions):]
+
+        for i, q in enumerate(md_qs): q['id'] = i + 1
+        for i, q in enumerate(rag_qs): q['id'] = i + 1
+        for i, q in enumerate(agent_qs): q['id'] = i + 1
+
+        merged_module = {
+            "id": current_max_id + 1,
+            "topicName": "AI 大模型与 Agent 开发",
+            "categories": [
+                {
+                    "id": 1,
+                    "categoryName": "大模型基础理论",
+                    "questions": md_qs
+                },
+                {
+                    "id": 2,
+                    "categoryName": "RAG 开发实战",
+                    "questions": rag_qs
+                },
+                {
+                    "id": 3,
+                    "categoryName": "Agent 开发实战",
+                    "questions": agent_qs
+                }
+            ]
+        }
+        data.append(merged_module)
+
+    # 6. 写回文件
+    with open(result_json_path, 'w', encoding='utf-8') as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+
+    print(f"\n合并完成！共新增 {len(all_questions)} 条问答。")
+
+if __name__ == '__main__':
+    main()