128 lines
6.6 KiB
JavaScript
128 lines
6.6 KiB
JavaScript
// 语言 → 分类 的映射表
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// 根据仓库主要编程语言直接映射到对应的分类
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const languageCategoryMap = {
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'HTML': 'Web应用', 'CSS': 'Web应用', 'JavaScript': 'Web应用',
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'TypeScript': 'Web应用', 'Vue': 'Web应用', 'Svelte': 'Web应用',
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'PHP': 'Web应用', 'Ruby': 'Web应用', 'Elixir': 'Web应用',
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'Python': 'AI/机器学习', 'Jupyter Notebook': 'AI/机器学习',
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'R': '数据分析', 'Julia': '数据分析',
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'Java': '移动应用', 'Kotlin': '移动应用', 'Dart': '移动应用',
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'Swift': '移动应用', 'Objective-C': '移动应用',
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'C++': '桌面应用', 'C#': '桌面应用', 'Go': '桌面应用',
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'Rust': '桌面应用', 'C': '桌面应用', 'Zig': '桌面应用',
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'SQL': '数据库', 'PLpgSQL': '数据库',
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'Shell': '开发工具', 'Dockerfile': '开发工具', 'Makefile': '开发工具',
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'CMake': '开发工具', 'PowerShell': '开发工具',
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};
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// 关键词规则：每个分类关联一组关键词，与仓库名称/描述匹配
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// 关键词越长代表越精确，匹配时按关键词长度加权
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const keywordRules = [
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{ category: 'AI/机器学习', keywords: ['ai', 'artificial intelligence', 'machine learning', 'deep learning', 'neural', 'llm', 'gpt', 'transformer', 'pytorch', 'tensorflow', 'keras', 'openai', 'langchain', 'rag', 'embedding', 'stable diffusion', 'huggingface', 'nlp', 'computer vision', 'reinforcement learning', 'generative'] },
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{ category: 'Web应用', keywords: ['web', 'website', 'api', 'rest', 'graphql', 'frontend', 'backend', 'fullstack', 'react', 'next.js', 'nuxt', 'express', 'django', 'flask', 'spring', 'laravel', 'rails', 'http', 'server', 'middleware', 'cors', 'websocket'] },
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{ category: '移动应用', keywords: ['mobile', 'android', 'ios', 'flutter', 'react native', 'swiftui', 'jetpack', 'compose', 'app', 'pwa'] },
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{ category: '桌面应用', keywords: ['desktop', 'electron', 'tauri', 'qt', 'gtk', 'winui', 'wpf', 'cli', 'terminal', 'tui'] },
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{ category: '数据库', keywords: ['database', 'sql', 'nosql', 'redis', 'postgresql', 'mysql', 'mongodb', 'sqlite', 'cassandra', 'dynamodb', 'elasticsearch', 'orm', 'prisma', 'sequelize'] },
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{ category: '开发工具', keywords: ['devtool', 'developer tool', 'ide', 'editor', 'vscode', 'plugin', 'extension', 'compiler', 'linter', 'formatter', 'debugger', 'docker', 'kubernetes', 'k8s', 'ci/cd', 'github action', 'automation', 'scaffold', 'boilerplate', 'template', 'sdk', 'library', 'framework', 'package'] },
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{ category: '安全工具', keywords: ['security', 'hack', 'penetration', 'vulnerability', 'exploit', 'encrypt', 'decrypt', 'auth', 'oauth', 'jwt', 'firewall', 'malware', 'ransomware', 'cve', 'bug bounty', 'sast', 'dast'] },
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{ category: '游戏', keywords: ['game', 'gaming', 'unity', 'unreal', 'godot', 'sprite', 'animation', 'physics engine', '3d', 'webgl', 'opengl', 'vulkan', 'shader'] },
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{ category: '设计工具', keywords: ['design', 'ui', 'ux', 'figma', 'sketch', 'svg', 'icon', 'font', 'typography', 'color', 'theme', 'animation', 'css', 'tailwind', 'bootstrap', 'material'] },
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{ category: '效率工具', keywords: ['productivity', 'utility', 'tool', 'workflow', 'automation', 'clipboard', 'note', 'todo', 'calendar', 'password', 'manager', 'sync'] },
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{ category: '教育学习', keywords: ['tutorial', 'course', 'learn', 'education', 'documentation', 'book', 'cheatsheet', 'roadmap', 'example', 'demo', 'guide', 'awesome', 'awesome-'] },
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{ category: '社交网络', keywords: ['social', 'chat', 'messaging', 'forum', 'community', 'blog', 'feed', 'timeline', 'notification', 'follower', 'friend'] },
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{ category: '数据分析', keywords: ['data', 'analytics', 'visualization', 'dashboard', 'chart', 'statistics', 'etl', 'big data', 'spark', 'hadoop', 'pandas', 'numpy', 'scipy', 'tableau', 'metabase', 'superset'] },
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];
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// 文本标准化：转小写 + 去除首尾空格
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function normalize(text) {
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return (text || '').toLowerCase().trim();
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}
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// 关键词评分：遍历关键词列表，匹配到的每个关键词按长度累加得分
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function scoreByKeywords(text, keywords) {
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const normalized = normalize(text);
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let score = 0;
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for (const kw of keywords) {
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if (normalized.includes(kw)) {
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score += kw.length;
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}
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}
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return score;
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}
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// 对单个仓库进行自动分类（返回最多 3 个最匹配的分类）
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export function autoCategorize(repo, categories) {
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const lang = repo.language || '';
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const name = repo.full_name || '';
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const desc = repo.description || '';
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const results = [];
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// 遍历所有关键词规则进行评分
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for (const rule of keywordRules) {
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let score = 0;
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// 仓库名称权重 2 倍，描述权重 1 倍
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score += scoreByKeywords(name, rule.keywords) * 2;
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score += scoreByKeywords(desc, rule.keywords);
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// 如果编程语言直接匹配当前分类，加基础分
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const langMatch = languageCategoryMap[lang];
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if (langMatch === rule.category) {
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score += 5;
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}
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if (score > 0) {
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const matched = categories.find(c => c.name === rule.category);
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if (matched) {
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results.push({ category: matched, score });
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}
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}
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}
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// 关键词没匹配到：回退到仅按语言映射分类
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if (results.length === 0) {
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const langCategory = languageCategoryMap[lang];
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if (langCategory) {
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const matched = categories.find(c => c.name === langCategory);
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if (matched) return [matched];
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}
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return [];
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}
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// 按得分排序，取最高分的 60% 以上的分类（最多 3 个）
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results.sort((a, b) => b.score - a.score);
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const maxScore = results[0].score;
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return results
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.filter(r => r.score >= maxScore * 0.6)
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.map(r => r.category)
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.slice(0, 3);
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}
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// 批量处理多个仓库的自动分类
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export function autoCategorizeAll(repos, categories) {
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const results = [];
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for (const repo of repos) {
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const matchedCategories = autoCategorize(repo, categories);
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results.push({
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repoId: repo.id,
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repoName: repo.full_name,
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categoryIds: matchedCategories.map(c => c.id),
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categoryNames: matchedCategories.map(c => c.name),
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});
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}
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return results;
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}
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// 执行自动分类的完整流程：获取未分类仓库 → 分类匹配 → 批量保存
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export async function runAutoCategorize(categories, getUncategorizedRepos, batchSetRepoCategories) {
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const uncategorized = await getUncategorizedRepos();
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if (uncategorized.length === 0) return 0;
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const results = autoCategorizeAll(uncategorized, categories);
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const assignments = results.filter(r => r.categoryIds.length > 0).map(r => ({
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repoId: r.repoId,
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categoryIds: r.categoryIds,
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}));
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if (assignments.length === 0) return 0;
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return await batchSetRepoCategories(assignments);
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}
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