diff --git a/.ai_cache/service_config.json b/.ai_cache/service_config.json index 504778e..48fbac7 100644 --- a/.ai_cache/service_config.json +++ b/.ai_cache/service_config.json @@ -6,5 +6,5 @@ "gemini" ], "summary_language": "zh", - "check_time": "2025-07-10T15:28:48.897543" + "check_time": "2025-07-15T23:27:54.141436" } \ No newline at end of file diff --git a/.cache/plugin/git-committers/page-authors.json b/.cache/plugin/git-committers/page-authors.json index 066b10a..0abfe56 100644 --- a/.cache/plugin/git-committers/page-authors.json +++ b/.cache/plugin/git-committers/page-authors.json @@ -1 +1 @@ -{"cache_date": "2025-07-12", "page_authors": {}} \ No newline at end of file +{"cache_date": "2025-07-15", "page_authors": {}} \ No newline at end of file diff --git a/docs/blog/websitebeauty/recommend.md b/docs/blog/websitebeauty/recommend.md new file mode 100644 index 0000000..e224b47 --- /dev/null +++ b/docs/blog/websitebeauty/recommend.md @@ -0,0 +1,534 @@ +--- +title: 为MKdocs页面添加相关文章推荐 +tags: + - Mkdocs +status: new +--- + +# 为MKdocs页面添加相关文章推荐 + +## 步骤 + +`mkdocs.yml`中需要覆写文件夹overrides(没有的话新建一个) + + + +```yaml +theme: + name: material + custom_dir: docs/overrides +``` + + +在docs/overrides/hooks/下新建一个`related_posts.py`文件即可，内容如下： + +具体配置根据自己仓库情况自行修改 + +```python +import os +import re +from collections import Counter, defaultdict +from textwrap import dedent +import hashlib +import yaml +from urllib.parse import urlparse + +# 存储所有文章的信息和索引 +article_index = {} +category_index = defaultdict(list) +keyword_index = defaultdict(set) + +# 配置：需要索引的目录 +INDEXED_DIRECTORIES = ['blog/', 'develop/'] + +# 配置：排除推荐的页面列表（支持精确匹配和模式匹配） +EXCLUDED_PAGES = { + # 精确路径匹配 + 'blog/index.md', + 'develop/index.md', + # 可以添加更多排除的页面 + # 'blog/special-page.md', +} + +# 配置：排除推荐的页面模式（支持通配符） +EXCLUDED_PATTERNS = [ + r'.*\/index\.md$', # 排除所有 index.md 文件 + r'.*\/archive\.md$', # 排除所有 archive.md 文件 + r'blog\/posts?\/.*', # 排除 blog/post/ 和 blog/posts/ 目录下的所有文章 + # 可以添加更多模式 + # r'blog\/draft\/.*', # 排除草稿目录 +] + +# 配置：相似度阈值和权重 +SIMILARITY_CONFIG = { + 'min_threshold': 0.15, # 提高最低相似度阈值 + 'weights': { + 'keywords': 0.35, # 关键词权重 + 'tags': 0.30, # 标签权重 + 'categories': 0.20, # 分类权重 + 'path': 0.10, # 路径分类权重 + 'source_dir': 0.05 # 源目录权重 + }, + 'title_similarity': 0.25 # 标题相似度权重 +} + +def is_page_excluded(file_path): + """检查页面是否应该排除推荐""" + # 精确匹配检查 + if file_path in EXCLUDED_PAGES: + return True + + # 模式匹配检查 + for pattern in EXCLUDED_PATTERNS: + if re.match(pattern, file_path): + return True + + return False + +def should_index_file(file_path): + """检查文件是否应该被索引""" + if not file_path.endswith('.md'): + return False + + # 先检查是否被排除 + if is_page_excluded(file_path): + return False + + # 检查是否在指定目录下 + for directory in INDEXED_DIRECTORIES: + if file_path.startswith(directory): + return True + + return False + +def extract_keywords(content, title): + """提取文章中的关键词，改进算法""" + # 移除YAML front matter + content = re.sub(r'^---\s*\n.*?\n---\s*\n', '', content, flags=re.DOTALL | re.MULTILINE) + + # 移除代码块 + content = re.sub(r'```.*?```', '', content, flags=re.DOTALL) + # 移除HTML标签 + content = re.sub(r'<.*?>', '', content) + # 移除链接 + content = re.sub(r'\[.*?\]\(.*?\)', '', content) + # 移除标题标记 + content = re.sub(r'^#+\s+', '', content, flags=re.MULTILINE) + + # 合并标题和内容，标题权重更高 + title_words = re.findall(r'\b\w+\b', title.lower()) * 4 # 增加标题权重 + content_words = re.findall(r'\b\w+\b', content.lower()) + all_words = title_words + content_words + + # 扩展停用词列表（包含中英文） + stopwords = { + # 英文停用词 + 'the', 'a', 'an', 'in', 'on', 'at', 'to', 'for', 'of', 'and', 'or', 'is', 'are', 'was', 'were', + 'be', 'been', 'have', 'has', 'had', 'do', 'does', 'did', 'will', 'would', 'could', 'should', + 'this', 'that', 'these', 'those', 'with', 'from', 'by', 'as', 'can', 'but', 'not', 'if', 'it', + 'they', 'them', 'their', 'you', 'your', 'we', 'our', 'my', 'me', 'i', 'he', 'she', 'him', 'her', + # 常见无意义词 + 'about', 'above', 'after', 'again', 'all', 'also', 'any', 'because', 'before', 'between', + 'both', 'each', 'few', 'first', 'get', 'how', 'into', 'just', 'last', 'made', 'make', 'may', + 'most', 'new', 'now', 'old', 'only', 'other', 'over', 'said', 'same', 'see', 'some', 'such', + 'take', 'than', 'then', 'time', 'two', 'use', 'very', 'way', 'well', 'where', 'when', 'which', + 'while', 'who', 'why', 'work', 'world', 'year', 'years', + # 中文停用词 + '的', '了', '和', '是', '就', '都', '而', '及', '与', '这', '那', '有', '在', '中', '为', '对', '等', + '能', '会', '可以', '没有', '什么', '一个', '自己', '这个', '那个', '这些', '那些', '如果', '因为', '所以' + } + + # 过滤单词：长度>=2，不在停用词中，不是纯数字 + words = [w for w in all_words + if len(w) >= 2 and w not in stopwords and not w.isdigit()] + + # 返回词频最高的15个词 + return Counter(words).most_common(15) + +def extract_metadata(content): + """提取文章元数据，支持YAML front matter""" + metadata = { + 'title': "未命名", + 'description': "", + 'tags': [], + 'categories': [], + 'disable_related': False # 新增：是否禁用相关推荐 + } + + # 尝试解析YAML front matter + yaml_match = re.match(r'^---\s*\n(.*?)\n---\s*\n', content, re.DOTALL) + if yaml_match: + try: + yaml_content = yaml_match.group(1) + yaml_data = yaml.safe_load(yaml_content) + if yaml_data: + metadata['title'] = str(yaml_data.get('title', '未命名')).strip('"\'') + metadata['description'] = str(yaml_data.get('description', '')).strip('"\'') + metadata['disable_related'] = yaml_data.get('disable_related', False) + + # 处理tags + tags = yaml_data.get('tags', []) + if isinstance(tags, list): + metadata['tags'] = [str(tag).strip() for tag in tags] + elif isinstance(tags, str): + metadata['tags'] = [tag.strip() for tag in tags.split(',') if tag.strip()] + + # 处理categories + categories = yaml_data.get('categories', []) + if isinstance(categories, list): + metadata['categories'] = [str(cat).strip() for cat in categories] + elif isinstance(categories, str): + metadata['categories'] = [cat.strip() for cat in categories.split(',') if cat.strip()] + except yaml.YAMLError: + pass # 如果YAML解析失败，使用默认值 + + # 如果YAML解析失败，回退到正则表达式 + if metadata['title'] == "未命名": + title_match = re.search(r'^title:\s*(.+)$', content, re.MULTILINE) + if title_match: + metadata['title'] = title_match.group(1).strip('"\'') + + return metadata + +def get_category_from_path(file_path): + """从文件路径提取分类""" + parts = file_path.split('/') + if len(parts) > 2: + return parts[1] # blog/category/file.md 或 develop/category/file.md格式 + elif len(parts) > 1: + return parts[0] # blog 或 develop + return "未分类" + +def calculate_content_hash(content): + """计算内容哈希，用于检测内容变化""" + return hashlib.md5(content.encode('utf-8')).hexdigest() + +def on_files(files, config): + """预处理所有文章，建立索引""" + global article_index, category_index, keyword_index + + # 清空索引 + article_index.clear() + category_index.clear() + keyword_index.clear() + + processed_count = 0 + excluded_count = 0 + + for file in files: + if should_index_file(file.src_path): + try: + with open(file.abs_src_path, 'r', encoding='utf-8') as f: + content = f.read() + + # 提取元数据 + metadata = extract_metadata(content) + + # 检查是否禁用相关推荐 + if metadata.get('disable_related', False): + excluded_count += 1 + continue + + # 再次检查是否在排除列表中（双重检查） + if is_page_excluded(file.src_path): + excluded_count += 1 + continue + + # 提取关键词 + keywords = extract_keywords(content, metadata['title']) + + # 获取分类 + path_category = get_category_from_path(file.src_path) + + # 构建文章信息 + article_info = { + 'title': metadata['title'], + 'description': metadata['description'], + 'tags': metadata['tags'], + 'categories': metadata['categories'], + 'path_category': path_category, + 'keywords': keywords, + 'url': file.url, + 'path': file.src_path, + 'content_hash': calculate_content_hash(content), + 'source_dir': file.src_path.split('/')[0] # blog 或 develop + } + + # 添加到主索引 + article_index[file.src_path] = article_info + + # 添加到分类索引 + category_index[path_category].append(file.src_path) + for category in metadata['categories']: + if category: # 确保分类不为空 + category_index[category].append(file.src_path) + + # 添加到关键词索引 + for keyword, _ in keywords: + keyword_index[keyword].add(file.src_path) + for tag in metadata['tags']: + if tag: # 确保标签不为空 + keyword_index[tag.lower()].add(file.src_path) + + processed_count += 1 + + except Exception as e: + print(f"❌ 处理文件 {file.src_path} 时出错: {e}") + + print(f"✅ 已索引 {processed_count} 篇文章 (blog + develop)") + if excluded_count > 0: + print(f"📝 排除 {excluded_count} 篇禁用推荐或在排除列表中的文章") + print(f"📊 分类数量: {len(category_index)}") + print(f"🔤 关键词数量: {len(keyword_index)}") + return files + +def on_page_markdown(markdown, **kwargs): + """为每篇文章添加相关推荐""" + page = kwargs['page'] + config = kwargs['config'] + + # 检查是否应该处理这个页面 + if not should_index_file(page.file.src_path): + return markdown + + # 检查是否被排除 + if is_page_excluded(page.file.src_path): + return markdown + + # 检查文章元数据是否禁用推荐 + try: + with open(page.file.abs_src_path, 'r', encoding='utf-8') as f: + content = f.read() + metadata = extract_metadata(content) + if metadata.get('disable_related', False): + return markdown + except Exception: + pass # 如果读取失败，继续处理 + + # 获取相关文章 + related_articles = get_related_articles(page.file.src_path, max_count=5) + + if not related_articles: + return markdown + + # 从 config 中获取 site_url 并解析出基本路径 + site_url = config.get('site_url', '') + base_path = urlparse(site_url).path if site_url else '/' + if not base_path.endswith('/'): + base_path += '/' + + # 构建推荐HTML - 针对Safari浏览器优化 + recommendation_html = "\n" + + # 添加CSS样式，特别针对Safari浏览器优化 + recommendation_html += """ +""" + + # 简化且兼容的HTML结构 + recommendation_html += '
\n' + recommendation_html += '

📚 相关文章推荐

\n' + recommendation_html += '\n' + recommendation_html += '
\n' + + # 确保没有多余的空行 + return markdown.rstrip() + recommendation_html + +def get_related_articles(current_path, max_count=5): + """获取相关文章，使用改进的算法""" + if current_path not in article_index: + return [] + + current_article = article_index[current_path] + similarities = [] + + # 获取当前文章的关键信息 + current_title = current_article['title'].lower() + current_tags = set(tag.lower() for tag in current_article['tags'] if tag) + current_categories = set(cat.lower() for cat in current_article['categories'] if cat) + + for path, article_info in article_index.items(): + if path == current_path: + continue + + # 过滤掉标题为"未命名"的文章 + if article_info['title'] == "未命名" or not article_info['title'].strip(): + continue + + # 再次检查是否在排除列表中（双重检查） + if is_page_excluded(path): + continue + + # 计算相似度 + score = calculate_similarity(current_article, article_info) + + # 标题相似度加权 + title_similarity = calculate_title_similarity(current_title, article_info['title'].lower()) + if title_similarity > 0.3: # 标题有一定相似度 + score += title_similarity * SIMILARITY_CONFIG['title_similarity'] + + # 应用最低阈值 + if score > SIMILARITY_CONFIG['min_threshold']: + similarities.append((score, article_info)) + + # 按相似度排序 + similarities.sort(key=lambda x: x[0], reverse=True) + + # 多样性优化：确保不同分类的文章都有机会被推荐 + if len(similarities) > max_count * 2: + # 按分类分组 + category_groups = defaultdict(list) + for score, article in similarities: + for category in article['categories']: + if category: + category_groups[category.lower()].append((score, article)) + + # 从每个分类中选取最相关的文章 + diverse_results = [] + used_paths = set() + + # 首先添加最相关的文章 + if similarities: + top_score, top_article = similarities[0] + diverse_results.append((top_score, top_article)) + used_paths.add(top_article['path']) + + # 然后从每个分类中添加最相关的文章 + for category in sorted(category_groups.keys()): + if len(diverse_results) >= max_count: + break + + for score, article in category_groups[category]: + if article['path'] not in used_paths: + diverse_results.append((score, article)) + used_paths.add(article['path']) + break + + # 如果还有空位，从剩余的高分文章中填充 + if len(diverse_results) < max_count: + for score, article in similarities: + if article['path'] not in used_paths and len(diverse_results) < max_count: + diverse_results.append((score, article)) + used_paths.add(article['path']) + + # 重新按相似度排序 + diverse_results.sort(key=lambda x: x[0], reverse=True) + return diverse_results[:max_count] + + return similarities[:max_count] + +def calculate_title_similarity(title1, title2): + """计算两个标题的相似度""" + # 分词 + words1 = set(re.findall(r'\b\w+\b', title1)) + words2 = set(re.findall(r'\b\w+\b', title2)) + + if not words1 or not words2: + return 0 + + # 计算Jaccard相似度 + intersection = len(words1.intersection(words2)) + union = len(words1.union(words2)) + + if union == 0: + return 0 + + return intersection / union + +def calculate_similarity(article1, article2): + """计算两篇文章的相似度""" + score = 0 + weights = SIMILARITY_CONFIG['weights'] + + # 1. 关键词相似度 + keywords1 = dict(article1['keywords']) + keywords2 = dict(article2['keywords']) + common_keywords = set(keywords1.keys()) & set(keywords2.keys()) + + if common_keywords: + # 考虑关键词的频率和重要性 + keyword_score = sum(min(keywords1[kw], keywords2[kw]) for kw in common_keywords) + # 关键词匹配数量的奖励 + keyword_count_bonus = len(common_keywords) / max(len(keywords1), 1) * 0.5 + score += (keyword_score + keyword_count_bonus) * weights['keywords'] + + # 2. 标签相似度 + tags1 = set(tag.lower() for tag in article1['tags'] if tag) + tags2 = set(tag.lower() for tag in article2['tags'] if tag) + + if tags1 and tags2: # 确保两篇文章都有标签 + tag_overlap = len(tags1 & tags2) + tag_ratio = tag_overlap / max(len(tags1), 1) # 相对重叠比例 + tag_score = tag_overlap * 8 * (1 + tag_ratio) # 增加重叠比例奖励 + score += tag_score * weights['tags'] + + # 3. 分类相似度 + categories1 = set(cat.lower() for cat in article1['categories'] if cat) + categories2 = set(cat.lower() for cat in article2['categories'] if cat) + + if categories1 and categories2: # 确保两篇文章都有分类 + category_overlap = len(categories1 & categories2) + category_ratio = category_overlap / max(len(categories1), 1) + category_score = category_overlap * 12 * (1 + category_ratio) + score += category_score * weights['categories'] + + # 4. 路径分类相似度 + if article1['path_category'] == article2['path_category']: + score += 3 * weights['path'] + + # 5. 同源目录加分 + if article1.get('source_dir') == article2.get('source_dir'): + score += 2 * weights['source_dir'] + + return score +``` + + +## 效果如下 \ No newline at end of file diff --git a/docs/overrides/hooks/__pycache__/related_posts.cpython-311.pyc b/docs/overrides/hooks/__pycache__/related_posts.cpython-311.pyc new file mode 100644 index 0000000..32116d0 Binary files /dev/null and b/docs/overrides/hooks/__pycache__/related_posts.cpython-311.pyc differ diff --git a/docs/overrides/hooks/related_posts.py b/docs/overrides/hooks/related_posts.py new file mode 100644 index 0000000..dea6389 --- /dev/null +++ b/docs/overrides/hooks/related_posts.py @@ -0,0 +1,503 @@ +import os +import re +from collections import Counter, defaultdict +from textwrap import dedent +import hashlib +import yaml +from urllib.parse import urlparse + +# 存储所有文章的信息和索引 +article_index = {} +category_index = defaultdict(list) +keyword_index = defaultdict(set) + +# 配置：需要索引的目录 +INDEXED_DIRECTORIES = ['blog/', 'develop/'] + +# 配置：排除推荐的页面列表（支持精确匹配和模式匹配） +EXCLUDED_PAGES = { + # 精确路径匹配 + 'blog/index.md', + 'develop/index.md', + # 可以添加更多排除的页面 + # 'blog/special-page.md', +} + +# 配置：排除推荐的页面模式（支持通配符） +EXCLUDED_PATTERNS = [ + r'.*\/index\.md$', # 排除所有 index.md 文件 + r'.*\/archive\.md$', # 排除所有 archive.md 文件 + r'blog\/posts?\/.*', # 排除 blog/post/ 和 blog/posts/ 目录下的所有文章 + # 可以添加更多模式 + # r'blog\/draft\/.*', # 排除草稿目录 +] + +# 配置：相似度阈值和权重 +SIMILARITY_CONFIG = { + 'min_threshold': 0.15, # 提高最低相似度阈值 + 'weights': { + 'keywords': 0.35, # 关键词权重 + 'tags': 0.30, # 标签权重 + 'categories': 0.20, # 分类权重 + 'path': 0.10, # 路径分类权重 + 'source_dir': 0.05 # 源目录权重 + }, + 'title_similarity': 0.25 # 标题相似度权重 +} + +def is_page_excluded(file_path): + """检查页面是否应该排除推荐""" + # 精确匹配检查 + if file_path in EXCLUDED_PAGES: + return True + + # 模式匹配检查 + for pattern in EXCLUDED_PATTERNS: + if re.match(pattern, file_path): + return True + + return False + +def should_index_file(file_path): + """检查文件是否应该被索引""" + if not file_path.endswith('.md'): + return False + + # 先检查是否被排除 + if is_page_excluded(file_path): + return False + + # 检查是否在指定目录下 + for directory in INDEXED_DIRECTORIES: + if file_path.startswith(directory): + return True + + return False + +def extract_keywords(content, title): + """提取文章中的关键词，改进算法""" + # 移除YAML front matter + content = re.sub(r'^---\s*\n.*?\n---\s*\n', '', content, flags=re.DOTALL | re.MULTILINE) + + # 移除代码块 + content = re.sub(r'```.*?```', '', content, flags=re.DOTALL) + # 移除HTML标签 + content = re.sub(r'<.*?>', '', content) + # 移除链接 + content = re.sub(r'\[.*?\]\(.*?\)', '', content) + # 移除标题标记 + content = re.sub(r'^#+\s+', '', content, flags=re.MULTILINE) + + # 合并标题和内容，标题权重更高 + title_words = re.findall(r'\b\w+\b', title.lower()) * 4 # 增加标题权重 + content_words = re.findall(r'\b\w+\b', content.lower()) + all_words = title_words + content_words + + # 扩展停用词列表（包含中英文） + stopwords = { + # 英文停用词 + 'the', 'a', 'an', 'in', 'on', 'at', 'to', 'for', 'of', 'and', 'or', 'is', 'are', 'was', 'were', + 'be', 'been', 'have', 'has', 'had', 'do', 'does', 'did', 'will', 'would', 'could', 'should', + 'this', 'that', 'these', 'those', 'with', 'from', 'by', 'as', 'can', 'but', 'not', 'if', 'it', + 'they', 'them', 'their', 'you', 'your', 'we', 'our', 'my', 'me', 'i', 'he', 'she', 'him', 'her', + # 常见无意义词 + 'about', 'above', 'after', 'again', 'all', 'also', 'any', 'because', 'before', 'between', + 'both', 'each', 'few', 'first', 'get', 'how', 'into', 'just', 'last', 'made', 'make', 'may', + 'most', 'new', 'now', 'old', 'only', 'other', 'over', 'said', 'same', 'see', 'some', 'such', + 'take', 'than', 'then', 'time', 'two', 'use', 'very', 'way', 'well', 'where', 'when', 'which', + 'while', 'who', 'why', 'work', 'world', 'year', 'years', + # 中文停用词 + '的', '了', '和', '是', '就', '都', '而', '及', '与', '这', '那', '有', '在', '中', '为', '对', '等', + '能', '会', '可以', '没有', '什么', '一个', '自己', '这个', '那个', '这些', '那些', '如果', '因为', '所以' + } + + # 过滤单词：长度>=2，不在停用词中，不是纯数字 + words = [w for w in all_words + if len(w) >= 2 and w not in stopwords and not w.isdigit()] + + # 返回词频最高的15个词 + return Counter(words).most_common(15) + +def extract_metadata(content): + """提取文章元数据，支持YAML front matter""" + metadata = { + 'title': "未命名", + 'description': "", + 'tags': [], + 'categories': [], + 'disable_related': False # 新增：是否禁用相关推荐 + } + + # 尝试解析YAML front matter + yaml_match = re.match(r'^---\s*\n(.*?)\n---\s*\n', content, re.DOTALL) + if yaml_match: + try: + yaml_content = yaml_match.group(1) + yaml_data = yaml.safe_load(yaml_content) + if yaml_data: + metadata['title'] = str(yaml_data.get('title', '未命名')).strip('"\'') + metadata['description'] = str(yaml_data.get('description', '')).strip('"\'') + metadata['disable_related'] = yaml_data.get('disable_related', False) + + # 处理tags + tags = yaml_data.get('tags', []) + if isinstance(tags, list): + metadata['tags'] = [str(tag).strip() for tag in tags] + elif isinstance(tags, str): + metadata['tags'] = [tag.strip() for tag in tags.split(',') if tag.strip()] + + # 处理categories + categories = yaml_data.get('categories', []) + if isinstance(categories, list): + metadata['categories'] = [str(cat).strip() for cat in categories] + elif isinstance(categories, str): + metadata['categories'] = [cat.strip() for cat in categories.split(',') if cat.strip()] + except yaml.YAMLError: + pass # 如果YAML解析失败，使用默认值 + + # 如果YAML解析失败，回退到正则表达式 + if metadata['title'] == "未命名": + title_match = re.search(r'^title:\s*(.+)$', content, re.MULTILINE) + if title_match: + metadata['title'] = title_match.group(1).strip('"\'') + + return metadata + +def get_category_from_path(file_path): + """从文件路径提取分类""" + parts = file_path.split('/') + if len(parts) > 2: + return parts[1] # blog/category/file.md 或 develop/category/file.md格式 + elif len(parts) > 1: + return parts[0] # blog 或 develop + return "未分类" + +def calculate_content_hash(content): + """计算内容哈希，用于检测内容变化""" + return hashlib.md5(content.encode('utf-8')).hexdigest() + +def on_files(files, config): + """预处理所有文章，建立索引""" + global article_index, category_index, keyword_index + + # 清空索引 + article_index.clear() + category_index.clear() + keyword_index.clear() + + processed_count = 0 + excluded_count = 0 + + for file in files: + if should_index_file(file.src_path): + try: + with open(file.abs_src_path, 'r', encoding='utf-8') as f: + content = f.read() + + # 提取元数据 + metadata = extract_metadata(content) + + # 检查是否禁用相关推荐 + if metadata.get('disable_related', False): + excluded_count += 1 + continue + + # 再次检查是否在排除列表中（双重检查） + if is_page_excluded(file.src_path): + excluded_count += 1 + continue + + # 提取关键词 + keywords = extract_keywords(content, metadata['title']) + + # 获取分类 + path_category = get_category_from_path(file.src_path) + + # 构建文章信息 + article_info = { + 'title': metadata['title'], + 'description': metadata['description'], + 'tags': metadata['tags'], + 'categories': metadata['categories'], + 'path_category': path_category, + 'keywords': keywords, + 'url': file.url, + 'path': file.src_path, + 'content_hash': calculate_content_hash(content), + 'source_dir': file.src_path.split('/')[0] # blog 或 develop + } + + # 添加到主索引 + article_index[file.src_path] = article_info + + # 添加到分类索引 + category_index[path_category].append(file.src_path) + for category in metadata['categories']: + if category: # 确保分类不为空 + category_index[category].append(file.src_path) + + # 添加到关键词索引 + for keyword, _ in keywords: + keyword_index[keyword].add(file.src_path) + for tag in metadata['tags']: + if tag: # 确保标签不为空 + keyword_index[tag.lower()].add(file.src_path) + + processed_count += 1 + + except Exception as e: + print(f"❌ 处理文件 {file.src_path} 时出错: {e}") + + print(f"✅ 已索引 {processed_count} 篇文章 (blog + develop)") + if excluded_count > 0: + print(f"📝 排除 {excluded_count} 篇禁用推荐或在排除列表中的文章") + print(f"📊 分类数量: {len(category_index)}") + print(f"🔤 关键词数量: {len(keyword_index)}") + return files + +def on_page_markdown(markdown, **kwargs): + """为每篇文章添加相关推荐""" + page = kwargs['page'] + config = kwargs['config'] + + # 检查是否应该处理这个页面 + if not should_index_file(page.file.src_path): + return markdown + + # 检查是否被排除 + if is_page_excluded(page.file.src_path): + return markdown + + # 检查文章元数据是否禁用推荐 + try: + with open(page.file.abs_src_path, 'r', encoding='utf-8') as f: + content = f.read() + metadata = extract_metadata(content) + if metadata.get('disable_related', False): + return markdown + except Exception: + pass # 如果读取失败，继续处理 + + # 获取相关文章 + related_articles = get_related_articles(page.file.src_path, max_count=5) + + if not related_articles: + return markdown + + # 从 config 中获取 site_url 并解析出基本路径 + site_url = config.get('site_url', '') + base_path = urlparse(site_url).path if site_url else '/' + if not base_path.endswith('/'): + base_path += '/' + + # 构建推荐HTML - 针对Safari浏览器优化 + recommendation_html = "\n" + + # 添加CSS样式，特别针对Safari浏览器优化 + recommendation_html += """ +""" + + # 简化且兼容的HTML结构 + recommendation_html += '
\n' + recommendation_html += '

📚 相关文章推荐

\n' + recommendation_html += '\n' + recommendation_html += '
\n' + + # 确保没有多余的空行 + return markdown.rstrip() + recommendation_html + +def get_related_articles(current_path, max_count=5): + """获取相关文章，使用改进的算法""" + if current_path not in article_index: + return [] + + current_article = article_index[current_path] + similarities = [] + + # 获取当前文章的关键信息 + current_title = current_article['title'].lower() + current_tags = set(tag.lower() for tag in current_article['tags'] if tag) + current_categories = set(cat.lower() for cat in current_article['categories'] if cat) + + for path, article_info in article_index.items(): + if path == current_path: + continue + + # 过滤掉标题为"未命名"的文章 + if article_info['title'] == "未命名" or not article_info['title'].strip(): + continue + + # 再次检查是否在排除列表中（双重检查） + if is_page_excluded(path): + continue + + # 计算相似度 + score = calculate_similarity(current_article, article_info) + + # 标题相似度加权 + title_similarity = calculate_title_similarity(current_title, article_info['title'].lower()) + if title_similarity > 0.3: # 标题有一定相似度 + score += title_similarity * SIMILARITY_CONFIG['title_similarity'] + + # 应用最低阈值 + if score > SIMILARITY_CONFIG['min_threshold']: + similarities.append((score, article_info)) + + # 按相似度排序 + similarities.sort(key=lambda x: x[0], reverse=True) + + # 多样性优化：确保不同分类的文章都有机会被推荐 + if len(similarities) > max_count * 2: + # 按分类分组 + category_groups = defaultdict(list) + for score, article in similarities: + for category in article['categories']: + if category: + category_groups[category.lower()].append((score, article)) + + # 从每个分类中选取最相关的文章 + diverse_results = [] + used_paths = set() + + # 首先添加最相关的文章 + if similarities: + top_score, top_article = similarities[0] + diverse_results.append((top_score, top_article)) + used_paths.add(top_article['path']) + + # 然后从每个分类中添加最相关的文章 + for category in sorted(category_groups.keys()): + if len(diverse_results) >= max_count: + break + + for score, article in category_groups[category]: + if article['path'] not in used_paths: + diverse_results.append((score, article)) + used_paths.add(article['path']) + break + + # 如果还有空位，从剩余的高分文章中填充 + if len(diverse_results) < max_count: + for score, article in similarities: + if article['path'] not in used_paths and len(diverse_results) < max_count: + diverse_results.append((score, article)) + used_paths.add(article['path']) + + # 重新按相似度排序 + diverse_results.sort(key=lambda x: x[0], reverse=True) + return diverse_results[:max_count] + + return similarities[:max_count] + +def calculate_title_similarity(title1, title2): + """计算两个标题的相似度""" + # 分词 + words1 = set(re.findall(r'\b\w+\b', title1)) + words2 = set(re.findall(r'\b\w+\b', title2)) + + if not words1 or not words2: + return 0 + + # 计算Jaccard相似度 + intersection = len(words1.intersection(words2)) + union = len(words1.union(words2)) + + if union == 0: + return 0 + + return intersection / union + +def calculate_similarity(article1, article2): + """计算两篇文章的相似度""" + score = 0 + weights = SIMILARITY_CONFIG['weights'] + + # 1. 关键词相似度 + keywords1 = dict(article1['keywords']) + keywords2 = dict(article2['keywords']) + common_keywords = set(keywords1.keys()) & set(keywords2.keys()) + + if common_keywords: + # 考虑关键词的频率和重要性 + keyword_score = sum(min(keywords1[kw], keywords2[kw]) for kw in common_keywords) + # 关键词匹配数量的奖励 + keyword_count_bonus = len(common_keywords) / max(len(keywords1), 1) * 0.5 + score += (keyword_score + keyword_count_bonus) * weights['keywords'] + + # 2. 标签相似度 + tags1 = set(tag.lower() for tag in article1['tags'] if tag) + tags2 = set(tag.lower() for tag in article2['tags'] if tag) + + if tags1 and tags2: # 确保两篇文章都有标签 + tag_overlap = len(tags1 & tags2) + tag_ratio = tag_overlap / max(len(tags1), 1) # 相对重叠比例 + tag_score = tag_overlap * 8 * (1 + tag_ratio) # 增加重叠比例奖励 + score += tag_score * weights['tags'] + + # 3. 分类相似度 + categories1 = set(cat.lower() for cat in article1['categories'] if cat) + categories2 = set(cat.lower() for cat in article2['categories'] if cat) + + if categories1 and categories2: # 确保两篇文章都有分类 + category_overlap = len(categories1 & categories2) + category_ratio = category_overlap / max(len(categories1), 1) + category_score = category_overlap * 12 * (1 + category_ratio) + score += category_score * weights['categories'] + + # 4. 路径分类相似度 + if article1['path_category'] == article2['path_category']: + score += 3 * weights['path'] + + # 5. 同源目录加分 + if article1.get('source_dir') == article2.get('source_dir'): + score += 2 * weights['source_dir'] + + return score diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css index 41133c7..c069ddd 100644 --- a/docs/stylesheets/extra.css +++ b/docs/stylesheets/extra.css @@ -15,7 +15,7 @@ button.md-top { font-family: LXGW WenKai; /* 修改字体 */ font-size: 16px; /* 修改字体大小 */ font-weight: lighter;/* 修改字体粗细 */ - color: #1D2636; /* 修改字体颜色 */ + /* color: #1D2636; 修改字体颜色 */ } :root { @@ -604,3 +604,27 @@ filter: progid:DXImageTransform.Microsoft.BasicImage(grayscale=1); height: 30rem; } +/* 仅针对文本链接设置红色 */ +.md-typeset p a:not(.md-button):not([class*="md-"]):not([style*="background"]), +.md-typeset li a:not(.md-button):not([class*="md-"]):not([style*="background"]), +.md-typeset td a:not(.md-button):not([class*="md-"]):not([style*="background"]), +.md-typeset blockquote a:not(.md-button):not([class*="md-"]):not([style*="background"]) { + color: #5688F7 !important; /* 红色文本链接 */ + text-decoration: none; +} + +.md-typeset p a:not(.md-button):not([class*="md-"]):not([style*="background"]):hover, +.md-typeset li a:not(.md-button):not([class*="md-"]):not([style*="background"]):hover, +.md-typeset td a:not(.md-button):not([class*="md-"]):not([style*="background"]):hover, +.md-typeset blockquote a:not(.md-button):not([class*="md-"]):not([style*="background"]):hover { + color: #43d2e8 !important; /* 悬停时深红色 */ + text-decoration: underline; +} + +/* 访问过的文本链接 */ +/* .md-typeset p a:not(.md-button):not([class*="md-"]):not([style*="background"]):visited, +.md-typeset li a:not(.md-button):not([class*="md-"]):not([style*="background"]):visited, +.md-typeset td a:not(.md-button):not([class*="md-"]):not([style*="background"]):visited, +.md-typeset blockquote a:not(.md-button):not([class*="md-"]):not([style*="background"]):visited { + color: #b83280 !important; +} */ \ No newline at end of file diff --git a/docs/stylesheets/extra2.css b/docs/stylesheets/extra2.css index b00cabf..31f1f5b 100644 --- a/docs/stylesheets/extra2.css +++ b/docs/stylesheets/extra2.css @@ -40,4 +40,9 @@ padding: 0.25rem 0.5rem; border-radius: 4px; cursor: pointer; +} + +/* 目录导航的缩进参考线 */ +nav.md-nav--secondary ul { + border-left: 1px solid lightblue; } \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 4b3c409..13c3e8e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -76,6 +76,7 @@ nav: - 3.解决Github Pages部署mkdocs自定义域名失效的问题: blog/Mkdocs/mkdocs3.md - Mkdocs美化/补充: - MkDocs文档AI摘要: blog/websitebeauty/Mkdocs-AI-Summary.md + - 添加相关推荐文章: blog/websitebeauty/recommend.md - 添加阅读信息统计: blog/websitebeauty/reading_time.md - 添加Mkdocs博客: blog/Mkdocs/mkdocsblog.md - 如何给MKdocs添加友链: blog/Mkdocs/linktech.md @@ -318,4 +319,5 @@ extra_css: hooks: - docs/overrides/hooks/socialmedia.py - docs/overrides/hooks/reading_time.py - - docs/overrides/hooks/ai_summary.py \ No newline at end of file + - docs/overrides/hooks/ai_summary.py + - docs/overrides/hooks/related_posts.py \ No newline at end of file