From 376b4412cfb44df903444acd238d7003e67f1d4b Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 13 Nov 2025 02:28:39 +0000 Subject: [PATCH] feat: migrate Astro site to root directory and update deployment workflows - Remove old Docusaurus site files - Move Astro site from new-site/ to project root - Update GitHub Actions workflows for Astro deployment - Configure deploy.yml to build and deploy from dist/ directory - Update test-deploy.yml to use Node 20 and Astro build - Add proper permissions for GitHub Pages deployment --- .github/workflows/deploy.yml | 49 +- .github/workflows/test-deploy.yml | 16 +- .gitignore | 31 +- new-site/.prettierrc => .prettierrc | 0 AGENTS.md | 178 - ...TION_STATUS.md => BLOG_MIGRATION_STATUS.md | 0 .../CURRENT_STATUS.md => CURRENT_STATUS.md | 0 .../MIGRATION_GUIDE.md => MIGRATION_GUIDE.md | 0 .../PROJECT_SUMMARY.md => PROJECT_SUMMARY.md | 0 README.md | 354 +- new-site/astro.config.mjs => astro.config.mjs | 0 babel.config.js | 3 - 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new-site/tsconfig.json => tsconfig.json (100%) diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index 86e430b..b417a9d 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -2,50 +2,49 @@ name: Deploy to GitHub Pages on: push: - branches: - - main - # Review gh actions docs if you want to further define triggers, paths, etc - # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#on + branches: [main, astro-main] + workflow_dispatch: + +permissions: + contents: read + pages: write + id-token: write + +concurrency: + group: "pages" + cancel-in-progress: false jobs: build: - name: Build Docusaurus runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - uses: actions/setup-node@v4 + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup Node.js + uses: actions/setup-node@v4 with: - node-version: 18 - cache: npm + node-version: '20' + cache: 'npm' - name: Install dependencies run: npm ci - - name: Build website + + - name: Build with Astro run: npm run build - - name: Upload Build Artifact + - name: Upload artifact uses: actions/upload-pages-artifact@v3 with: - path: build + path: ./dist deploy: - name: Deploy to GitHub Pages needs: build - - # Grant GITHUB_TOKEN the permissions required to make a Pages deployment - permissions: - pages: write # to deploy to Pages - id-token: write # to verify the deployment originates from an appropriate source - - # Deploy to the github-pages environment + runs-on: ubuntu-latest environment: name: github-pages url: ${{ steps.deployment.outputs.page_url }} - - runs-on: ubuntu-latest steps: - name: Deploy to GitHub Pages id: deployment - uses: actions/deploy-pages@v4 \ No newline at end of file + uses: actions/deploy-pages@v4 diff --git a/.github/workflows/test-deploy.yml b/.github/workflows/test-deploy.yml index cc32de6..c2708cc 100644 --- a/.github/workflows/test-deploy.yml +++ b/.github/workflows/test-deploy.yml @@ -12,15 +12,17 @@ jobs: name: Test deployment runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - uses: actions/setup-node@v4 + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup Node.js + uses: actions/setup-node@v4 with: - node-version: 18 - cache: npm + node-version: '20' + cache: 'npm' - name: Install dependencies run: npm ci - - name: Test build website + + - name: Test build with Astro run: npm run build \ No newline at end of file diff --git a/.gitignore b/.gitignore index b2d6de3..f965de2 100644 --- a/.gitignore +++ b/.gitignore @@ -1,20 +1,29 @@ # Dependencies -/node_modules +node_modules/ -# Production -/build +# Build outputs +dist/ +.astro/ -# Generated files -.docusaurus -.cache-loader +# Environment +.env +.env.* +!.env.example -# Misc +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# OS .DS_Store -.env.local -.env.development.local -.env.test.local -.env.production.local +Thumbs.db +# Logs npm-debug.log* yarn-debug.log* yarn-error.log* +pnpm-debug.log* +lerna-debug.log* diff --git a/new-site/.prettierrc b/.prettierrc similarity index 100% rename from new-site/.prettierrc rename to .prettierrc diff --git a/AGENTS.md b/AGENTS.md deleted file mode 100644 index f680638..0000000 --- a/AGENTS.md +++ /dev/null @@ -1,178 +0,0 @@ -# AGENTS.md - LLM Instructions for Behitek.com Project - -## Project Overview - -This is **Behitek.com** - a personal website and blog for Hieu Nguyen (behitek), an AI Engineer from Vietnam. The site is built using **Docusaurus 3.7.0**, a modern static site generator, and serves as a platform for sharing AI/ML knowledge, technical documentation, and personal projects. - -### Site Purpose -- **Personal Portfolio**: Showcases Hieu's background as an AI Engineer with focus on NLP, RAG, LLMs, and information retrieval -- **Technical Blog**: AI/ML articles, tutorials, and insights -- **Documentation Hub**: Computer science fundamentals (data structures, algorithms) -- **Project Showcase**: Personal and professional projects -- **Interactive Games**: Mini-games like Minesweeper, Clash of Clans integration - -## Architecture & Technology Stack - -### Core Framework -- **Docusaurus 3.7.0** - React-based static site generator -- **React 18.2.0** - Frontend framework -- **Node.js ≥18.0** - Runtime requirement - -### Styling & UI -- **TailwindCSS 3.4.1** - Utility-first CSS framework -- **Custom CSS** - Located in `src/css/custom.css` -- **Dark Mode**: Default theme (forced dark mode, no toggle) -- **FontAwesome Icons** - For social links and UI elements - -### Content & Features -- **MDX Support** - Enhanced Markdown with React components -- **Math Rendering** - KaTeX for mathematical expressions -- **Search** - Algolia DocSearch integration -- **Analytics** - Google Tag Manager (GTM) -- **Comments** - Disqus integration for blog posts - -## Project Structure - -``` -behitek.github.io/ -├── blog/ # Blog posts (Markdown files) -│ ├── authors.yml # Author information -│ └── *.md # Blog posts with YYYY-MM-DD-title.md format -├── docs/ # Documentation pages -│ ├── algorithms/ # Algorithm explanations -│ ├── data-structures/ # Data structure guides -│ └── *.md # Other documentation -├── src/ -│ ├── css/ # Custom styles -│ ├── data/ # Site data (projects, social links) -│ ├── pages/ # Custom React pages -│ │ ├── index.js # Homepage -│ │ ├── projects/ # Projects showcase -│ │ ├── minesweeper/ # Game page -│ │ └── bank/ # Banking game -│ └── theme/ # Docusaurus theme customizations -├── static/ # Static assets -│ ├── img/ # Images and media -│ ├── pdf/ # Documents (resume, etc.) -│ └── CNAME # Custom domain configuration -├── docusaurus.config.js # Main configuration -├── sidebars.js # Documentation sidebar structure -└── package.json # Dependencies and scripts -``` - -## Key Configuration Files - -### docusaurus.config.js -- **Site metadata**: Title, tagline, URL configuration -- **Theme settings**: Dark mode, navbar, footer -- **Plugin configuration**: Algolia search, Google Tag Manager -- **Content settings**: Blog and docs configuration -- **Math support**: KaTeX integration - -### sidebars.js -- Defines documentation sidebar structure -- Categories: Data Structures, Algorithms -- Individual docs: Git, Python conventions - -## Content Guidelines - -### Blog Posts -- **Location**: `blog/` directory -- **Naming**: `YYYY-MM-DD-title.md` format -- **Frontmatter**: Required metadata (title, authors, tags, etc.) -- **Author**: Use `hieunv` as defined in `authors.yml` -- **Topics**: AI/ML, programming, tutorials, technical insights - -### Documentation -- **Location**: `docs/` directory -- **Structure**: Organized by categories (algorithms, data structures) -- **Style**: Educational, with examples and explanations -- **Images**: Store in `static/img/docs/` - -### Projects -- **Configuration**: `src/data/_Projects.js` -- **Format**: Array of project objects with metadata -- **Required fields**: category, title, subtitle, description, tech, links - -## Development Workflow - -### Local Development -```bash -# Install dependencies -npm install - -# Start development server -npm start - -# Build for production -npm run build - -# Serve built site -npm run serve -``` - -### Content Creation -1. **Blog posts**: Create in `blog/` with proper frontmatter -2. **Documentation**: Add to `docs/` and update `sidebars.js` if needed -3. **Images**: Place in appropriate `static/img/` subdirectory -4. **Projects**: Update `src/data/_Projects.js` - -### Deployment -- **Platform**: GitHub Pages -- **Domain**: behitek.com (configured via CNAME) -- **Branch**: `gh-pages` for deployment -- **Command**: `npm run deploy` - -## Important Notes for LLMs - -### When Working with This Project: - -1. **Content Focus**: This is an AI Engineer's personal site - content should be technical, educational, and professional - -2. **File Naming**: - - Blog posts: `YYYY-MM-DD-title.md` - - Use kebab-case for URLs and file names - - Images: descriptive names in appropriate subdirectories - -3. **Markdown Features**: - - Supports MDX (React components in Markdown) - - Math expressions with KaTeX - - Code syntax highlighting with Prism - - Frontmatter metadata is required for blog posts - -4. **Styling**: - - Uses TailwindCSS classes - - Custom CSS variables defined in `src/css/custom.css` - - Dark mode is default and forced (no light mode toggle) - -5. **Navigation**: - - Main nav: Docs, Blog, Games, Resume - - Footer: Connect, Discover, Products sections - - Social links: LinkedIn, GitHub, Twitter, Email - -6. **SEO & Analytics**: - - Google Tag Manager configured - - Algolia search integration - - Meta descriptions and titles important - -### Common Tasks: -- **Adding blog posts**: Create markdown file in `blog/` with proper frontmatter -- **Updating projects**: Modify `src/data/_Projects.js` -- **Adding documentation**: Create in `docs/` and update `sidebars.js` -- **Styling changes**: Edit `src/css/custom.css` or use Tailwind classes -- **Configuration changes**: Modify `docusaurus.config.js` - -### Best Practices: -- Always include proper frontmatter in blog posts -- Use descriptive alt text for images -- Maintain consistent formatting and style -- Test locally before deployment -- Keep content professional and educational -- Use proper heading hierarchy (H1 → H2 → H3) - -## Contact Information -- **Site Owner**: Hieu Nguyen (behitek) -- **Email**: hello@behitek.com -- **GitHub**: https://github.com/behitek -- **LinkedIn**: https://www.linkedin.com/in/behitek/ -- **Twitter**: https://twitter.com/behitek_ diff --git a/new-site/BLOG_MIGRATION_STATUS.md b/BLOG_MIGRATION_STATUS.md similarity index 100% rename from new-site/BLOG_MIGRATION_STATUS.md rename to BLOG_MIGRATION_STATUS.md diff --git a/new-site/CURRENT_STATUS.md b/CURRENT_STATUS.md similarity index 100% rename from new-site/CURRENT_STATUS.md rename to CURRENT_STATUS.md diff --git a/new-site/MIGRATION_GUIDE.md b/MIGRATION_GUIDE.md similarity index 100% rename from new-site/MIGRATION_GUIDE.md rename to MIGRATION_GUIDE.md diff --git a/new-site/PROJECT_SUMMARY.md b/PROJECT_SUMMARY.md similarity index 100% rename from new-site/PROJECT_SUMMARY.md rename to PROJECT_SUMMARY.md diff --git a/README.md b/README.md index 0c6c2c2..1bee22f 100644 --- a/README.md +++ b/README.md @@ -1,41 +1,365 @@ -# Website +# Behitek Portfolio - Astro Edition -This website is built using [Docusaurus](https://docusaurus.io/), a modern static website generator. +> A blazing-fast, modern portfolio website for an AI Engineer, built with Astro 4.x and TailwindCSS. + +🌐 **Live Site:** [behitek.com](https://behitek.com) +🎨 **Framework:** Astro 4.x +💅 **Styling:** TailwindCSS 3.x +📝 **Content:** MDX for blog posts + +--- + +## ✨ Features + +### Core Features +- ⚡ **Lightning Fast**: < 1s load time with minimal JavaScript +- 🌙 **Dark Mode**: Smooth theme switching with persistence +- 📱 **Fully Responsive**: Mobile-first design +- 🎨 **Modern UI**: Glass morphism, gradient accents, smooth animations +- 🔍 **SEO Optimized**: Complete meta tags, OpenGraph, Twitter cards +- 📊 **RSS Feed**: Subscribe to blog updates +- ♿ **Accessible**: WCAG 2.1 AA compliant + +### Blog Features +- 📝 10+ technical articles about AI/ML, NLP, RAG, Python +- 🌐 **Bilingual Support**: English & Vietnamese posts (tagged) +- 🏷️ **Smart Filtering**: Filter by language and category +- 📖 **Table of Contents**: Auto-generated with active section highlighting +- 📈 **Reading Progress**: Visual progress bar +- 💬 **Comments**: Giscus (GitHub Discussions) +- 🔗 **Share Buttons**: Twitter, LinkedIn, Copy link +- ⏱️ **Reading Time**: Estimated time for each post + +### Project Showcase +- 🚀 Featured projects: LCOJ, Inverted HyDE, hoc-bash +- 📂 Categorized: Product, Research, Tutorial, Tool, Fun +- 🏷️ Tech stack tags +- 🔗 Links to GitHub, live demos, blog posts + +### Technical Highlights +- 🎯 TypeScript for type safety +- 🎨 Custom Tailwind theme with emerald green branding +- 📦 Content Collections for type-safe content +- 🖼️ Optimized images with Astro Image +- 🤖 Google Tag Manager integration +- 🚀 GitHub Actions deployment + +--- + +## 🚀 Quick Start + +### Prerequisites +- Node.js 20.x or higher +- npm or pnpm ### Installation +```bash +# Install dependencies +npm install + +# Start development server +npm run dev + +# Build for production +npm run build + +# Preview production build +npm run preview ``` -$ yarn + +### Development Commands + +```bash +npm run dev # Start dev server (http://localhost:4321) +npm run build # Build for production +npm run preview # Preview production build +npm run astro # Run Astro CLI commands ``` -### Local Development +--- + +## 📁 Project Structure ``` -$ yarn start +/ +├── public/ # Static assets +│ ├── images/ # Images (copied from old site) +│ │ ├── blog/ # Blog post images +│ │ └── me.jpeg # Profile picture +│ ├── favicon.ico +│ ├── CNAME # Custom domain config +│ └── .nojekyll # GitHub Pages config +│ +├── src/ +│ ├── components/ # Reusable components +│ │ ├── Navbar.astro +│ │ ├── Footer.astro +│ │ ├── ThemeToggle.astro +│ │ ├── BlogCard.astro +│ │ ├── ProjectCard.astro +│ │ ├── LanguageBadge.astro +│ │ ├── TableOfContents.astro +│ │ ├── Giscus.astro +│ │ └── SEO.astro +│ │ +│ ├── layouts/ # Page layouts +│ │ ├── BaseLayout.astro +│ │ └── BlogPostLayout.astro +│ │ +│ ├── pages/ # Routes +│ │ ├── index.astro # Homepage +│ │ ├── blog/ +│ │ │ ├── index.astro # Blog listing +│ │ │ └── [...slug].astro # Blog post pages +│ │ ├── projects.astro +│ │ ├── contact.astro +│ │ ├── 404.astro +│ │ └── rss.xml.ts # RSS feed +│ │ +│ ├── content/ # Content collections +│ │ ├── blog/ # Blog posts (MDX) +│ │ │ └── inverted-hyde.mdx +│ │ ├── projects/ # Projects (JSON) +│ │ │ ├── lcoj.json +│ │ │ ├── inverted-hyde.json +│ │ │ └── hoc-bash.json +│ │ └── config.ts # Content collection schemas +│ │ +│ ├── styles/ # Global styles +│ │ └── global.css # Tailwind + custom styles +│ │ +│ └── utils/ # Utility functions +│ ├── constants.ts +│ └── helpers.ts +│ +├── .github/ +│ └── workflows/ +│ └── deploy.yml # GitHub Actions deployment +│ +├── astro.config.mjs # Astro configuration +├── tailwind.config.mjs # Tailwind configuration +├── tsconfig.json # TypeScript configuration +└── package.json ``` -This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. +--- + +## 🎨 Design System + +### Color Palette + +```css +Primary (Emerald Green): + - #10B981 (Main) + - #059669 (Dark) + - #34D399 (Light) -### Build +Accent: + - #06B6D4 (Cyan) - For highlights + - #8B5CF6 (Purple) - For AI/ML content +Neutral (Slate): + - Light mode: #FFFFFF background, #0F172A text + - Dark mode: #0F172A background, #F8FAFC text ``` -$ yarn build + +### Typography + +- **Headings**: Inter (Google Fonts) +- **Body**: System fonts for performance +- **Code**: JetBrains Mono / Fira Code + +### Components + +- **Glass Morphism**: Navbar with backdrop blur +- **Bento Grid**: About section layout +- **Cards**: Elevated cards with hover effects +- **Badges**: Language (EN/VI) and category badges +- **Buttons**: Primary (filled) and secondary (outlined) + +--- + +## 📝 Content Management + +### Adding Blog Posts + +1. Create new MDX file in `src/content/blog/`: + +```markdown +--- +title: "Your Post Title" +description: "Brief description" +date: 2025-11-12 +author: "Hieu Nguyen" +language: "en" # or "vi" +category: "AI/ML" # AI/ML, Python, Tutorial, Data +tags: ["tag1", "tag2"] +image: "/images/blog/your-image.jpg" +draft: false +--- + +Your content here... ``` -This command generates static content into the `build` directory and can be served using any static contents hosting service. +2. Add images to `public/images/blog/` +3. Blog post will be automatically generated at `/blog/your-post-slug` -### Deployment +### Adding Projects -Using SSH: +1. Create JSON file in `src/content/projects/`: +```json +{ + "title": "Project Name", + "description": "Project description", + "category": "Product", + "tech": ["Python", "FastAPI", "Docker"], + "links": { + "website": "https://example.com", + "github": "https://github.com/..." + }, + "featured": true, + "order": 1 +} ``` -$ USE_SSH=true yarn deploy + +2. Project will appear on homepage and projects page + +--- + +## 🔧 Configuration + +### Giscus Comments + +1. Enable GitHub Discussions in your repository +2. Visit [giscus.app](https://giscus.app) +3. Configure and get your repo ID and category ID +4. Update `src/components/Giscus.astro`: + +```astro +script.setAttribute('data-repo', 'behitek/behitek.github.io'); +script.setAttribute('data-repo-id', 'YOUR_REPO_ID'); +script.setAttribute('data-category-id', 'YOUR_CATEGORY_ID'); ``` -Not using SSH: +### Google Tag Manager +Update in `src/layouts/BaseLayout.astro`: + +```html + ``` -$ GIT_USER= yarn deploy + +### Custom Domain + +Update `public/CNAME` with your domain: + +``` +behitek.com +``` + +--- + +## 🚀 Deployment + +### GitHub Pages (Recommended) + +1. Push to main branch or designated branch +2. GitHub Actions will automatically build and deploy +3. Enable GitHub Pages in repo settings: + - Settings → Pages + - Source: GitHub Actions + +### Manual Deployment + +```bash +# Build +npm run build + +# Deploy dist/ folder to your hosting provider ``` -If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch. +--- + +## 📊 Performance + +### Lighthouse Scores (Target) +- Performance: 95+ +- Accessibility: 95+ +- Best Practices: 95+ +- SEO: 100 + +### Bundle Size +- JavaScript: ~5-10 KB (vs 200 KB with Docusaurus) +- CSS: ~20 KB (with TailwindCSS purging) +- First Load: < 1 second + +--- + +## 🛠️ Tech Stack + +- **Framework**: [Astro 4.x](https://astro.build) +- **Styling**: [TailwindCSS 3.x](https://tailwindcss.com) +- **Content**: MDX with Content Collections +- **Fonts**: Google Fonts (Inter) +- **Icons**: Emoji + SVG +- **Comments**: [Giscus](https://giscus.app) +- **Analytics**: Google Tag Manager +- **Hosting**: GitHub Pages +- **CI/CD**: GitHub Actions + +--- + +## 📚 Migration from Docusaurus + +See [MIGRATION_GUIDE.md](./MIGRATION_GUIDE.md) for detailed migration steps. + +**Summary**: +- Copy blog posts from `/blog/*.md` to `/src/content/blog/*.mdx` +- Update frontmatter format +- Fix image paths: `/img/blog` → `/images/blog` +- Remove `` tags +- Test each post + +--- + +## 🤝 Contributing + +This is a personal portfolio, but suggestions and bug reports are welcome! + +1. Fork the repository +2. Create your feature branch +3. Commit your changes +4. Push to the branch +5. Open a Pull Request + +--- + +## 📄 License + +© 2025 Hieu Nguyen. All rights reserved. + +--- + +## 🙏 Acknowledgments + +- Built with [Astro](https://astro.build) +- Styled with [TailwindCSS](https://tailwindcss.com) +- Comments powered by [Giscus](https://giscus.app) +- Hosted on [GitHub Pages](https://pages.github.com) + +--- + +## 📧 Contact + +- **Email**: hello@behitek.com +- **LinkedIn**: [linkedin.com/in/behitek](https://linkedin.com/in/behitek) +- **GitHub**: [github.com/behitek](https://github.com/behitek) +- **Twitter**: [@behitek_](https://twitter.com/behitek_) + +--- + +**Built with ❤️ by Hieu Nguyen** diff --git a/new-site/astro.config.mjs b/astro.config.mjs similarity index 100% rename from new-site/astro.config.mjs rename to astro.config.mjs diff --git a/babel.config.js b/babel.config.js deleted file mode 100644 index e00595d..0000000 --- a/babel.config.js +++ /dev/null @@ -1,3 +0,0 @@ -module.exports = { - presets: [require.resolve('@docusaurus/core/lib/babel/preset')], -}; diff --git a/blog/2022-11-19-selenium-capture-http-request.md b/blog/2022-11-19-selenium-capture-http-request.md deleted file mode 100644 index b6fe01b..0000000 --- a/blog/2022-11-19-selenium-capture-http-request.md +++ /dev/null @@ -1,82 +0,0 @@ ---- -title: Capture HTTP request with Selenium like DevTools -authors: hieunv -tags: [selenium, web crawler] -description: This tutorial will show you how to use Selenium Wire to capture HTTP requests like the Chrome DevTools (Networks). -image: /img/blog/selenium-wire.png ---- - -Selenium is a great library for browser automation, this helps us do automation testing for QC and is also a way to crawl web data. For the dynamic content (i.e., loaded by Javascript), Selenium can help us capture the data while using the requests we can not. - - - -This tutorial will show you how to use Selenium Wire to capture HTTP requests like the Chrome DevTools (Networks). - -## Why capture requests? - -Selenium is the most popular open-source tool for automating web application testing across various platforms and browsers, as is well known. It is capable of running several web browsers and web controllers. However, Selenium can’t always be used when we need to receive the data the API returned to the front end for testing, logging, and debugging. - -Most people think of using Fiddler, Wireshark, or Badboy when discussing capturing HTTP requests because these tools let you start up a proxy server and intercept all requests and responses. However, these tools don’t have any API or CLI integration methods. - -## Selenium Wire vs Selenium - -As previously noted, you must utilize an HTTP proxy and set up your browser to route all traffic through the proxy in order to record HTTP requests and network traffic. - -A Python package called Selenium Wire expands the Selenium Webdriver bindings to provide your tests access to the browser’s underlying queries. It is a compact library with few external dependencies that are made for simplicity of usage. - -In the same way that you would with Selenium, Selenium Wire allows you to write tests, but it also adds a user-friendly API for accessing request/response headers, status codes, and body content. -By extending Selenium’s Python bindings, Selenium Wire gives you access to the browser’s underlying requests. Similar to Selenium, you write your code in the same manner, but with additional APIs for examining requests and answers and making live modifications to them. - -## Using selenium wire - -First, you need to install this package with this command: - -```bash -pip install selenium-wire -``` - -After that, you can use this package like you use selenium, everything is the same. So, this section only focuses on the additional feature: Capture HTTP requests. - - -When the browser visits a webpage, Selenium Wire records all HTTP/HTTPS requests that are made by the browser. - - -The `driver.requests` return a list of requests captured by the driver. So, you can filter them by looping through them and checking the URL, method, headers, response body, etc. - -```py -from seleniumwire import webdriver # Import seleniumwire - -# Create the Chrome driver -driver = webdriver.Chrome() - -# Go to the Github homepage -driver.get('https://www.github.com') - -# Access requests list via the `requests` attribute -for request in driver.requests: - if request.response: - print( - request.url, - request.response.status_code, - request.response.headers['Content-Type'] - ) -``` - -Output: - -``` - https://accounts.google.com/ListAccounts?gpsia=1&source=ChromiumBrowser&json=standard 200 application/json; charset=utf- -https://www.github.com/ 301 None -https://github.com/ 200 text/html; charset=utf-8 -https://github.githubassets.com/assets/light-719f1193e0c0.css 200 text/css -https://github.githubassets.com/assets/dark-0c343b529849.css 200 text/css -https://github.githubassets.com/assets/github-5d7162839c37.css 200 text/css -https://github.githubassets.com/assets/global-f7b08e35f325.css 200 text/css -https://github.githubassets.com/assets/primer-f9c4f0f1debb.css 200 text/css -https://github.githubassets.com/assets/dashboard-9336d0a99052.css 200 text/css -https://github.githubassets.com/assets/home-9685f6c9dd42.css 200 text/css -https://github.githubassets.com/assets/home-campaign-2d8ef15d1695.css 200 text/css -https://github.githubassets.com/static/fonts/github/mona-sans.woff2 200 binary/octet-stream -https://github.githubassets.com/assets/site-7f9cfaf3fb6a.css 200 text/css -... -``` diff --git a/blog/2022-12-11-bert-finetuned.md b/blog/2022-12-11-bert-finetuned.md deleted file mode 100644 index bb97a7c..0000000 --- a/blog/2022-12-11-bert-finetuned.md +++ /dev/null @@ -1,387 +0,0 @@ ---- -title: Fine-tuning BERT for the Sentence Pair Classification Task -sidebar_label: BERT for Sentence Pair Classification -authors: hieunv -tags: [bert, fine-tuning, reranking, natural language processing] -description: This tutorial will teach you how to fine-tune BERT for the Sentence Pair Classification task. In this task, each dataset sample contains two sentences and the appropriate target variable (label). -image: /img/blog/sentence-pair-classification.jpeg ---- - -This tutorial will teach you how to fine-tune BERT for the Sentence Pair Classification task. In this task, each dataset sample contains two sentences and the appropriate target variable (label). The sentence pair classification can use for many tasks such as information retrieval, question-answering, reranking and more. - - - -Look at the fantastic success of BERT and other Transformer models in many NLP tasks, and it should be no surprise that they also succeed at the sentence pair classification task. This tutorial explains how to use HuggingFace to fine-tune sentence pair classification using the power of Transformer models (BERT/RoBERTa). - -Environment setup ------------------ - -Before we start our tutorial, we need to set up the environment for our project. - -1\. First, it’s recommended to install conda for environment management, e.g. Miniconda, anaconda. You can download and install it from [here](https://docs.conda.io/en/latest/miniconda.html). - -2\. Create a new environment for the project: - -```bash -$ conda create --prefix ./venv python=3.9 -$ conda activate ./venv -$ pip install numpy transformers datasets pandas scikit-learn torch matplotlib -``` - - -Here is the requirements.txt of our experiment on the NVIDIA A40 GPU. But you can use Google Colab to train this task by reducing the batch size when training and inference. - -``` -datasets==2.7.1 -numpy==1.23.5 -pandas==1.5.2 -scikit-learn==1.1.3 -scipy==1.9.3 -torch==1.13.0 -transformers==4.24.0 -matplotlib -``` - - -3\. Now, we should import the package we will use in this project. - -``` -import transformers -import datasets -import json -import numpy as np -import matplotlib -``` - - -Dataset preparation -------------------- - -Now we need a dataset to train and test our models after the environment is set up. To simplify, we decided to use the dataset squad to evaluate the effectiveness of the proposed method and fine-tune BERT for sentence pair classification. This dataset can be downloaded automatically by the datasets library, so what we need to do to get the data is quite simple. - -``` -# Load squad dataset -squad = datasets.load_dataset("squad") -print(squad) -print(squad["train"][0]) -``` - - -Output: - -```json -DatasetDict({ - train: Dataset({ - features: ['id', 'title', 'context', 'question', 'answers'], - num_rows: 87599 - }) - validation: Dataset({ - features: ['id', 'title', 'context', 'question', 'answers'], - num_rows: 10570 - }) -}) -{'id': '5733be284776f41900661182', 'title': 'University_of_Notre_Dame', 'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.', 'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?', 'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]}} -``` - - -Each sample in the SQUAD dataset consists of id, title, context, question, and answers. This is a question-answering dataset, but why do we use this for sentence pair classification? Look at the picture below. - -For the retrieval-based question-answering system, before you can find the correct answer to the question, you need to find the relevant document related to the question. Find the relevant document can be considered as sentence pair classification where each sample consists of: - -* question -* context -* label (related or not) - -Create negative sample ----------------------- - -In this tutorial, we also plan to create a dataset to train the model for the document retrieval task. Specifically, the sentence pair classification dataset will be generated from SQUAD as follows: - -* Each pair of (question, context) is labeled as 1 if relevant, else 0. -* We already have the relevant pairs because each question in every sample of SQUAD has the correct context attached. -* So, we only need to create the negative sample: question and context unrelated. - -First, we will only need the questions and contexts from SQUAD. - -``` -# Extract question and context from each item in the squad -questions = [item["question"] for item in squad["train"]] -contexts = [item["context"] for item in squad["train"]] - questions.extend([item["question"] for item in squad["validation"]]) -contexts.extend([item["context"] for item in squad["validation"]]) - print(len(questions), len(contexts)) -``` - - -``` -98169 98169 -``` - - -Because the dataset is quite large for building a tutorial, we decided to use only part of the dataset to reduce the training time. - -* We pick the first 10000 samples as the positive samples (context related to the question) -* To create the negative, for each question, we find 10 other contexts not related to the question. The create\_non\_relevants() function will do this work. -* After the dataset preparation, we split to train and test set by the ratio 80:20. -* Each train and test set is then saved to a separate JSON file in JSON line format. - -```py -pair_data = [] - n_samples = 10000 - questions = questions[:n_samples] -contexts = contexts[:n_samples] - def create_non_relevants(index, n=10): - other_indexs = [i for i in range(len(questions)) if i != index] - return [i for i in np.random.choice(other_indexs, n)] - # For each questions, create 10 non relevant contexts -for index, question in enumerate(questions): - pair_data.append({"question": question, "context": contexts[index], "label": 1}) - non_relevants_indexs = create_non_relevants(index) - for non_relevant_index in non_relevants_indexs: - pair_data.append({"question": question, "context": contexts[non_relevant_index], "label": 0}) - # Shuffle and split train and test -test_size = 0.2 -np.random.shuffle(pair_data) -train_size = int(len(pair_data) * (1 - test_size)) -train_data = pair_data[:train_size] -test_data = pair_data[train_size:] - # Save to jsonl -with open("train.jsonl", "w") as f: - for item in train_data: - f.write(json.dumps(item) + "\n") - with open("test.jsonl", "w") as f: - for item in test_data: - f.write(json.dumps(item) + "\n") - print('Train size: ', len(train_data)) -print('Test size: ', len(test_data)) -``` - - -``` -Train size: 88000 -Test size: 22000 -``` - - -Here is some sample in the train.jsonl and test.jsonl: - -```bash -$ head test.jsonl -{"question": "Which country's courts were noted by the ECHR for taking a wider stance on provisions of genocide laws?", "context": "Antibiotics revolutionized medicine in the 20th century, and have together with vaccination led to the near eradication of diseases such as tuberculosis in the developed world. Their effectiveness and easy access led to overuse, especially in livestock raising, prompting bacteria to develop resistance. This has led to widespread problems with antimicrobial and antibiotic resistance, so much as to prompt the World Health Organization to classify antimicrobial resistance as a \"serious threat [that] is no longer a prediction for the future, it is happening right now in every region of the world and has the potential to affect anyone, of any age, in any country\".", "label": 0} -{"question": "Most domestic animals were selected for what traits?", "context": "Office buildings in Shanghai's financial district, including the Jin Mao Tower and the Hong Kong New World Tower, were evacuated. A receptionist at the Tibet Hotel in Chengdu said things were \"calm\" after the hotel evacuated its guests. Meanwhile, workers at a Ford plant in Sichuan were evacuated for about 10 minutes. Chengdu Shuangliu International Airport was shut down, and the control tower and regional radar control evacuated. One SilkAir flight was diverted and landed in Kunming as a result. Cathay Pacific delayed both legs of its quadruple daily Hong Kong to London route due to this disruption in air traffic services. Chengdu Shuangliu Airport reopened later on the evening of May 12, offering limited service as the airport began to be used as a staging area for relief operations.", "label": 0} -{"question": "What does the AAA feel is incompatible with working with the military?", "context": "During installation, an iPod is associated with one host computer. Each time an iPod connects to its host computer, iTunes can synchronize entire music libraries or music playlists either automatically or manually. Song ratings can be set on an iPod and synchronized later to the iTunes library, and vice versa. A user can access, play, and add music on a second computer if an iPod is set to manual and not automatic sync, but anything added or edited will be reversed upon connecting and syncing with the main computer and its library. If a user wishes to automatically sync music with another computer, an iPod's library will be entirely wiped and replaced with the other computer's library.", "label": 0} -{"question": "About how many police were said to have protected the torch in France?", "context": "Formal membership varies between communities, but basic lay adherence is often defined in terms of a traditional formula in which the practitioner takes refuge in The Three Jewels: the Buddha, the Dharma (the teachings of the Buddha), and the Sangha (the Buddhist community). At the present time, the teachings of all three branches of Buddhism have spread throughout the world, and Buddhist texts are increasingly translated into local languages. While in the West Buddhism is often seen as exotic and progressive, in the East it is regarded as familiar and traditional. Buddhists in Asia are frequently well organized and well funded. In countries such as Cambodia and Bhutan, it is recognized as the state religion and receives government support. Modern influences increasingly lead to new forms of Buddhism that significantly depart from traditional beliefs and practices.", "label": 0} -{"question": "In 1833 with whom with Chopin work to get his music published?", "context": "Chopin seldom performed publicly in Paris. In later years he generally gave a single annual concert at the Salle Pleyel, a venue that seated three hundred. He played more frequently at salons, but preferred playing at his own Paris apartment for small groups of friends. The musicologist Arthur Hedley has observed that \"As a pianist Chopin was unique in acquiring a reputation of the highest order on the basis of a minimum of public appearances\u2014few more than thirty in the course of his lifetime.\" The list of musicians who took part in some of his concerts provides an indication of the richness of Parisian artistic life during this period. Examples include a concert on 23 March 1833, in which Chopin, Liszt and Hiller performed (on pianos) a concerto by J.S. Bach for three keyboards; and, on 3 March 1838, a concert in which Chopin, his pupil Adolphe Gutmann, Charles-Valentin Alkan, and Alkan's teacher Joseph Zimmermann performed Alkan's arrangement, for eight hands, of two movements from Beethoven's 7th symphony. Chopin was also involved in the composition of Liszt's Hexameron; he wrote the sixth (and final) variation on Bellini's theme. Chopin's music soon found success with publishers, and in 1833 he contracted with Maurice Schlesinger, who arranged for it to be published not only in France but, through his family connections, also in Germany and England.", "label": 1} -{"question": "During his search for enlightenment, Gautama combined what teachings?", "context": "The two became friends, and for many years lived in close proximity in Paris, Chopin at 38 Rue de la Chauss\u00e9e-d'Antin, and Liszt at the H\u00f4tel de France on the Rue Lafitte, a few blocks away. They performed together on seven occasions between 1833 and 1841. The first, on 2 April 1833, was at a benefit concert organized by Hector Berlioz for his bankrupt Shakespearean actress wife Harriet Smithson, during which they played George Onslow's Sonata in F minor for piano duet. Later joint appearances included a benefit concert for the Benevolent Association of Polish Ladies in Paris. Their last appearance together in public was for a charity concert conducted for the Beethoven Memorial in Bonn, held at the Salle Pleyel and the Paris Conservatory on 25 and 26 April 1841.", "label": 0} -{"question": "What was the name of West's second album?", "context": "Chopin's public popularity as a virtuoso began to wane, as did the number of his pupils, and this, together with the political strife and instability of the time, caused him to struggle financially. In February 1848, with the cellist Auguste Franchomme, he gave his last Paris concert, which included three movements of the Cello Sonata Op. 65.", "label": 0} -``` - - -Dataset visualization ---------------------- - -Before using the prepared dataset to fine-tune BERT for the sentence pair classification task. We should verify that the length of each sample is fittable in the pre-trained. Most pre-trained BERT use `max_length = 512` tokens. - -```py -dataset = datasets.load_dataset("json", data_files={"train": "train.jsonl", "test": "test.jsonl"}) - question_and_context = [] - for item in dataset["train"]: - question_and_context.append(item["question"] + " " + item["context"]) - # Visualize the distribution of the length (number of words) of the questions and contexts -import matplotlib.pyplot as plt -plt.hist([len(item.split()) for item in question_and_context], bins=100) -plt.show() -``` - - -Output: - -Looks like everything is fine. So, we move to the next section. - -Train the sentence pair classification model --------------------------------------------- - -First, we need to load the tokenizer and model before use. In this tutorial, we use the RoBERTa, a variant of BERT. But actually, you can also use another pre-trained model. - -```py -from transformers import AutoTokenizer, AutoModelForSequenceClassification -tokenizer = AutoTokenizer.from_pretrained("roberta-base") -``` - - -Before training ---------------- - -Also, you may want to see how the tokenizer encodes your data. - -```py -encoded = tokenizer("Hello, my dog is cute", "Hello, my cat is amazing", return_tensors="pt") -decoded = tokenizer.decode(encoded["input_ids"][0]) -print(decoded) -``` - - -Output: - -``` -Hello, my dog is cuteHello, my cat is amazing -``` - - -The RoBERTa inserts a padding `` between the two sentences. This may differ when you using another pre-trained. - -As you know, a tokenizer is required to parse the text, and a padding and truncation mechanism is to manage varied sequence lengths. To treat your dataset in a single step, apply a preprocessing function to the entire dataset using the Datasets’ map method: - -```py -def preprocess_function(batch): - return tokenizer(batch["question"], batch["context"], truncation=True, padding="max_length") - dataset = datasets.load_dataset("json", data_files={"train": "train.jsonl", "test": "test.jsonl"}) -tokenized_data = dataset.map(preprocess_function, batched=True) -print(tokenized_data) -``` - - -Output: - -```json -DatasetDict({ - train: Dataset({ - features: ['question', 'context', 'label', 'input_ids', 'attention_mask'], - num_rows: 88000 - }) - test: Dataset({ - features: ['question', 'context', 'label', 'input_ids', 'attention_mask'], - num_rows: 22000 - }) -}) -``` - - -Model configuration -------------------- - -The code below shows our model configuration for fine-tuning BERT for sentence pair classification. We use the F1 score as the evaluation metric to evaluate model performance. - -Because this sentence pair classification task is a binary classification task, so we pass the `num_labels` is 2. - -Make sure to reduce the batch\_size if you get out of memory error. - -```py -from transformers import Trainer, TrainingArguments - def compute_metrics(eval_pred): - f1_score = datasets.load_metric("f1") - logits, labels = eval_pred - predictions = np.argmax(logits, axis=-1) - f1_score.add_batch(predictions=predictions, references=labels) - return f1_score.compute() - model = AutoModelForSequenceClassification.from_pretrained("roberta-base", num_labels=2) - training_args = TrainingArguments( - output_dir="./results", # output directory - num_train_epochs=3, # total # of training epochs - per_device_train_batch_size=32, # batch size per device during training - per_device_eval_batch_size=64, # batch size for evaluation - warmup_steps=500, # number of warmup steps for learning rate scheduler - weight_decay=0.01, # strength of weight decay - learning_rate=2e-5, # learning rate - save_total_limit=2, # limit the total amount of checkpoints, delete the older checkpoints - logging_dir="./logs", # directory for storing logs - logging_steps=100, - evaluation_strategy="steps", - eval_steps=100, - save_strategy="steps", - save_steps=100, -) - trainer = Trainer( - model=model, # the instantiated 🤗 Transformers model to be trained - args=training_args, # training arguments, defined above - train_dataset=tokenized_data["train"], # training dataset - eval_dataset=tokenized_data["test"], # evaluation dataset - compute_metrics=compute_metrics, # the callback that computes metrics of interest -``` - - -Model training --------------- - -After all, we can start training our sentence pair classification model: - -``` -trainer.train() -``` - - -And here is some very last line of training logs: - -``` -Model weights saved in ./results/checkpoint-7900/pytorch_model.bin -Deleting older checkpoint [results/checkpoint-7700] due to args.save_total_limit -{'loss': 0.0066, 'learning_rate': 6.451612903225807e-07, 'epoch': 2.91} - 97%|████████████████████████████████████████████████████████████████████████████████████▎ | 8000/8250 [5:20:04<03:04, 1.36it/s]The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: question, context. If question, context are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message. -***** Running Evaluation ***** - Num examples = 22000 - Batch size = 64 -{'eval_loss': 0.018584178760647774, 'eval_f1': 0.9785344189489267, 'eval_runtime': 164.1653, 'eval_samples_per_second': 134.011, 'eval_steps_per_second': 2.095, 'epoch': 2.91} - 97%|████████████████████████████████████████████████████████████████████████████████████▎ | 8000/8250 [5:22:48<03:04, 1.36it/s]Saving model checkpoint to ./results/checkpoint-8000 -Configuration saved in ./results/checkpoint-8000/config.json -Model weights saved in ./results/checkpoint-8000/pytorch_model.bin -Deleting older checkpoint [results/checkpoint-7800] due to args.save_total_limit -{'loss': 0.0042, 'learning_rate': 3.870967741935484e-07, 'epoch': 2.95} - 98%|█████████████████████████████████████████████████████████████████████████████████████▍ | 8100/8250 [5:24:06<01:50, 1.35it/s]The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: question, context. If question, context are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message. -***** Running Evaluation ***** - Num examples = 22000 - Batch size = 64 -{'eval_loss': 0.01884392462670803, 'eval_f1': 0.9783144406111385, 'eval_runtime': 165.3622, 'eval_samples_per_second': 133.041, 'eval_steps_per_second': 2.08, 'epoch': 2.95} - 98%|█████████████████████████████████████████████████████████████████████████████████████▍ | 8100/8250 [5:26:51<01:50, 1.35it/s]Saving model checkpoint to ./results/checkpoint-8100 -Configuration saved in ./results/checkpoint-8100/config.json -Model weights saved in ./results/checkpoint-8100/pytorch_model.bin -Deleting older checkpoint [results/checkpoint-7900] due to args.save_total_limit -{'loss': 0.0082, 'learning_rate': 1.2903225806451614e-07, 'epoch': 2.98} - 99%|██████████████████████████████████████████████████████████████████████████████████████▍| 8200/8250 [5:28:09<00:36, 1.35it/s]The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: question, context. If question, context are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message. -***** Running Evaluation ***** - Num examples = 22000 - Batch size = 64 -{'eval_loss': 0.01884007453918457, 'eval_f1': 0.9787759131293189, 'eval_runtime': 164.9596, 'eval_samples_per_second': 133.366, 'eval_steps_per_second': 2.085, 'epoch': 2.98} - 99%|██████████████████████████████████████████████████████████████████████████████████████▍| 8200/8250 [5:30:54<00:36, 1.35it/s]Saving model checkpoint to ./results/checkpoint-8200 -Configuration saved in ./results/checkpoint-8200/config.json -Model weights saved in ./results/checkpoint-8200/pytorch_model.bin -Deleting older checkpoint [results/checkpoint-8000] due to args.save_total_limit -100%|███████████████████████████████████████████████████████████████████████████████████████| 8250/8250 [5:31:35<00:00, 1.36it/s] - Training completed. Do not forget to share your model on huggingface.co/models =) - {'train_runtime': 19895.0898, 'train_samples_per_second': 13.27, 'train_steps_per_second': 0.415, 'train_loss': 0.027081513957543806, 'epoch': 3.0} -100%|███████████████████████████████████████████████████████████████████████████████████████| 8250/8250 [5:31:35<00:00, 2.41s/it] -The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: question, context. If question, context are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message. -***** Running Evaluation ***** - Num examples = 22000 - Batch size = 64 -{'eval_loss': 0.016812607645988464, 'eval_f1': 0.9798155993022676, 'eval_runtime': 167.2537, 'eval_samples_per_second': 131.537, 'eval_steps_per_second': 2.057} -``` - - -To evaluate the sentence pair classification model after training, you can add this line at the end. - -```py -trainer.evaluate() -``` - - -Or load it from the saved checkpoint. - -```py -model = AutoModelForSequenceClassification.from_pretrained( - "results/checkpoint-8200", num_labels=2 -) -... -trainer.evaluate() -``` - - -Output: - -``` -{'eval_loss': 0.016812607645988464, 'eval_f1': 0.9798155993022676, 'eval_runtime': 167.2537, 'eval_samples_per_second': 131.537, 'eval_steps_per_second': 2.057} -``` - -Conclusion ----------- - -This tutorial about sentence pair classification’s source code can be accessed from [this repo](https://github.com/pyzone-dev/sentence-pair-classification), and you can also open it in Google Colab by the link in README. diff --git a/blog/2024-03-09-python-decorator.md b/blog/2024-03-09-python-decorator.md deleted file mode 100644 index 7564096..0000000 --- a/blog/2024-03-09-python-decorator.md +++ /dev/null @@ -1,399 +0,0 @@ ---- -title: Python Decorator -authors: hieunv -tags: [python, decorator, vietnamese] -description: Python Decorator là một công cụ mạnh mẽ giúp chúng ta mở rộng chức năng của một hàm mà không cần thay đổi nội dung bên trong hàm đó. -image: /img/blog/python-decorator.jpeg ---- - -Python decorator là gì? decorator có nghĩa nôm na là "đồ đem đi trang trí". Quả thực vậy, decorator trong Python sinh ra là để đem đi trang trí - phụ họa thêm chức năng cho đối tượng (function, class) mà không can thiệp sửa đổi đối tượng đó. - - - -:::info - -TL;DR - -Bạn có thể cuộn xuống [mục 4](#các-ứng-dụng-của-python-decorator) để xem ngay và luôn ứng dụng thực thế của python decorator - -::: - -Quan sát ảnh phía dưới, nếu bạn đã biết về Python thì `@app.route ...` chắc hẳn bạn đã thấy hoặc dùng không ít lần. Nó chính là 1 decorator được dùng để chỉ định `route` cho hàm ngay phía dưới nó. - -![Python decorator được sử dụng để routing trong FastAPI / Flask](/img/blog/1709987562856.jpeg) - -Mục đích chính của việc sử dụng decorator là để áp dụng các nguyên tắc “DRY” (Don't Repeat Yourself - Không viết lại mã code) và "SoC" (Separation of Concerns - Phân chia trách nhiệm), cho phép chúng ta tách rời các phần mã không thuộc về logic chính của hàm như xác thực, logging, đo thời gian thực thi, xử lý ngoại lệ,... Decorator giúp chúng ta làm được điều này mà không cần sửa đổi nội dung bên trong đối tượng gốc. - -## Cú pháp của Python decorator - -Cú pháp của Python decorator rất đơn giản và dễ hiểu. Để sử dụng một decorator đã có cho một hàm bất kỳ, chúng ta chỉ cần sử dụng cú pháp `@` ngay phía trên định nghĩa của một hàm khác. Trong đó, `` là tên của hàm decorator. - -Dưới đây là cú pháp chung của Python decorator: -```py -@decorator -def function(): - # Function body -``` - -Trong đó: -- `decorator` là tên của decorator, thường là một hàm được định nghĩa trước đó. -- `function` là tên của hàm mà chúng ta muốn áp dụng `decorator` lên. - -Khi chúng ta định nghĩa một hàm, chúng ta có thể sử dụng cú pháp decorator để áp dụng các chức năng của decorator lên hàm đó. Python sẽ tự động chuyển hàm đó vào decorator và thực hiện các hoạt động tương ứng. - -Đó là cách dùng 1 decorator đã có, còn cách tạo nó thì mình giải thích sau ví dụ này: - -```py -def my_decorator(func): - def wrapper(): - # Code trước khi hàm func được gọi - # ... - func() - # Code sau khi hàm func được gọi - # ... - return wrapper - -@my_decorator -def my_function(): - # Function body - # ... - -my_function() -``` - -Trong ví dụ trên, `my_decorator` là một decorator và `my_function` là hàm mà chúng ta muốn áp dụng decorator lên. Khi chạy `my_function`, Python sẽ tự động gọi `my_decorator`, đưa `my_function` vào decorator và thực hiện các hoạt động bổ sung trước và sau khi `my_function` được gọi. - -**Rồi, vậy làm sao tạo ra được 1 decorator của riêng mình?** - -> Quan sát hàm `my_decorator` phía trên, chúng ta dễ dàng nhận ra là nó cũng chỉ là 1 hàm bình thường, nhưng có hàm con bên trong. Hàm con bên trong sẽ bao bọc "đối tượng" được nó trang trí, thêm thắt mắm muối trước và sau khi thực thi đối tượng đó. Các phần tiếp theo chúng ta sẽ đi vào chi tiết hơn nhé. - - -**Vậy 1 đối tượng có 2 đồ trang trí được không?** -> Cú pháp decorator cũng cho phép chúng ta áp dụng nhiều decorator lên cùng một hàm, thông qua việc sử dụng nhiều `@` cạnh nhau. - -Ví dụ cho 1 function cho cả 2 method GET và POST: -```py -@app.get('/') -@app.post('/) -def function(): - # function body -``` -Trong Python, decorator được dùng cho hàm (function), hoặc class. Các mục dưới đây sẽ đi vào tìm hiểu decorator lần lượt cho hàm và class. - -## Function là decorator - -Trong ngôn ngữ lập trình Python, một decorator function là một hàm được sử dụng để thay đổi hoặc bổ sung chức năng của một hàm khác mà không cần thay đổi mã nguồn của hàm đó. Decorator function nhận một hàm khác làm đối số và trả về một hàm mới có thể áp dụng các hoạt động bổ sung trước và sau khi hàm gốc được thực thi. - -Để định nghĩa một decorator function trong Python, chúng ta sử dụng cú pháp như sau: -```py -def decorator_function(func): - def wrapper_function(*args, **kwargs): - # Code trước khi hàm func được gọi - # ... - result = func(*args, **kwargs) - # Code sau khi hàm func được gọi - # ... - return result - return wrapper_function -``` - -Trong đó: -- `decorator_function` là tên của decorator function, bạn có thể đặt tên theo ý muốn. -- `func` là hàm mà chúng ta muốn áp dụng decorator lên. -- `wrapper_function` là hàm bao ngoài (wrapper function) được tạo bởi decorator, nó thực hiện các hoạt động bổ sung trước và sau khi func được gọi. - -Trong decorator function, chúng ta có thể sử dụng đối số `*args` và `**kwargs` để truyền các đối số và tham số keyword của hàm gốc cho wrapper_function. Sau đó, chúng ta gọi `func(*args, **kwargs)` để thực thi hàm gốc và lấy kết quả trả về. - -Sau khi định nghĩa decorator function, chúng ta có thể sử dụng cú pháp `@` để áp dụng decorator lên một hàm cụ thể trước định nghĩa hàm đó. - -Trong mục này, có thể chia nhỏ ra 3 trường hợp -- TH1: Cả function và decorator function đều không có tham số -- TH2: Function có tham số, decorator function không có tham số -- TH3: Cả 2 đều có tham số riêng - -### Ví dụ đơn giản (TH1) - -Dưới đây là một ví dụ minh họa về cách sử dụng decorator function trong Python: -```py -def uppercase_decorator(func): - def wrapper(): - result = func() - return result.upper() - return wrapper - -@uppercase_decorator -def say_hello(): - return "Hello, world!" - -print(say_hello()) # Output: HELLO, WORLD! -``` - -Trong ví dụ trên, `uppercase_decorator` là một decorator function, nó cung cấp thêm chức năng chuyển hết các ký tự trong kết quả của `say_hello` thành chữ hoa. Bằng cách gắn decorator `@uppercase_decorator` lên hàm `say_hello`, khi chúng ta gọi `say_hello()`, decorator function sẽ tự động được áp dụng và trả về kết quả đã được chuyển thành chữ hoa. - -### Function có tham số (TH2) - -Thực tế thì ta nên viết theo cách dưới đây thì function được "trang trí" dù có tham số hay không tham số đều hoạt động tốt. - -```py -def uppercase_decorator(func): - def wrapper(*args, **kwargs): - result = func(*args, **kwargs) - return result.upper() - return wrapper - -@uppercase_decorator -def say_hello(): - return "Hello, world!" - -print(say_hello()) # Output: HELLO, WORLD! -``` - -So với TH1, chúng ta chỉ bổ sung `*args, **kwargs` vào hàm wrapper và lời gọi hàm `func`. Đây là cách viết tổng quát, decorator đều sẽ hoạt động dù hàm mà nó "trang trí" có tham số hay không. - -### Decorator có tham số (TH3) - -Bên cạnh tham số của hàm được "trang trí", bản thân decoration function cũng có thể có tham số. Ví dụ quen thuộc: -```py -@app.get("/") -def home(): - pass -``` - -Giờ thử với một ví dụ đơn giản mà chúng ta tự viết từ A-Z - -```py -def greeting_decorator(greeting_message): - def decorator_function(func): - def wrapper(*args, **kwargs): - print(greeting_message) - result = func(*args, **kwargs) - return result - return wrapper - return decorator_function -``` -Trong ví dụ trên, `greeting_decorator` là một decorator function có tham số `greeting_message`. Nó trả về một decorator function bổ sung, `decorator_function`, được sử dụng để áp dụng chức năng bổ sung trước và sau khi hàm gốc `func` được gọi. - -Sau khi định nghĩa decorator function, bạn có thể sử dụng nó và truyền các đối số tùy chỉnh vào decorator như sau: -```py -@greeting_decorator("Hello, world!") -def say_name(name): - print("My name is", name) - -say_name("John") -``` - -Kết quả: - -``` -Hello, world! -My name is John -``` - - -## Class là decorator - -Bạn cũng có thể tạo một decorator với class, nó cũng có đáp ứng được các khả năng giống như decorator được tạo với hàm. - -Để định nghĩa một decorator class trong Python, bạn cần định nghĩa một lớp có phương thức `__call__`. Phương thức `__call__` sẽ được gọi khi đối tượng của lớp được gọi. Trong phương thức này, bạn có thể thực hiện các hoạt động bổ sung trước và sau khi hàm gốc được gọi. - -Dưới đây là một ví dụ minh họa về cách tạo decorator với class và sử dụng nó để "trang trí" một hàm trong Python: -```py -class uppercase_decorator: - def __init__(self, func): - self.func = func - - def __call__(self, *args, **kwargs): - result = self.func(*args, **kwargs) - return result.upper() - -@uppercase_decorator -def say_hello(): - return "Hello, world!" - -print(say_hello()) # Output: HELLO, WORLD! -``` - -## Các ứng dụng của Python decorator - -Mục này mình sẽ đưa ra một số ứng dụng rất thực tiễn mà khi sử dụng decorator sẽ giúp bạn tiết kiệm nhiều thời gian và tối ưu mã nguồn. Rồi từ đó bạn có thể tự rút ra kết luận decorator nên được dùng vào trường hợp nào. -Một số ứng dụng của decorator có thể là: -- Đo thời gian thực thi của 1 loạt các hàm / bước trong dự án -- Kiểm tra ngoại lệ trước khi thực thi -- Xác thực / phần quyền -- Caching -- ... - -### Đo thời gian thực thi - -Giả sử bạn có 1 luồng xử lý dữ liệu gồm các bước như sau: -- read_data -- preprocess_data -- extract_features -- export_results - -Bạn muốn đo thời gian thực hiện của từng bước trong pipeline để kiểm tra bước nào tốn thời gian nhất. - -```py -import time - -# timer decorator -def timer(func): - def wrapper(*args, **kwargs): - start = time.time() - result = func(*args, **kwargs) - end = time.time() - print(f'{func.__name__} took {end - start} seconds') - return result - return wrapper - -# Giả sử bạn có 1 pipeline xử lý dữ liệu như sau: -# - read_data -# - preprocess_data -# - extract_features -# - export_results -# Bạn muốn đo thời gian thực hiện của từng bước trong pipeline. - -@timer -def read_data(): - time.sleep(2) - print('reading data') - -@timer -def preprocess_data(): - time.sleep(3) - print('preprocessing data') - -@timer -def extract_features(): - time.sleep(1) - print('extracting features') - -@timer -def export_results(): - time.sleep(1) - print('exporting results') - -if __name__ == '__main__': - read_data() - preprocess_data() - extract_features() - export_results() -``` - -Kết quả thực thi: -``` -reading data -read_data took 2.005117177963257 seconds -preprocessing data -preprocess_data took 3.0033648014068604 seconds -extracting features -extract_features took 1.0050358772277832 seconds -exporting results -export_results took 1.0050859451293945 seconds -``` - -### Ghi log / debug - -Bạn muốn biết thứ tự các hàm được gọi, hay muốn biến giá trị đầu vào hoặc đầu ra của hàm khi nó thực thi mà không muốn sửa hàm. - -```py -def log_decorator(func): - def wrapper(*args, **kwargs): - # Ghi log trước khi thực thi hàm - print(f"Calling function: {func.__name__}") - print(f"Arguments: {args}") - print(f"Keyword arguments: {kwargs}") - result = func(*args, **kwargs) - # Ghi log sau khi thực thi hàm - print(f"Function {func.__name__} executed successfully") - return result - return wrapper - -@log_decorator -def add_numbers(a, b): - return a + b - -result = add_numbers(2, 3) -print(result) -``` - -Kết quả: -``` -Calling function: add_numbers -Arguments: (2, 3) -Keyword arguments: {} -Function add_numbers executed successfully -5 -``` - -### Xác thực / phân quyền - -Ví dụ dưới đây sử dụng decorator để kiểm tra liệu 1 hành động nhạy cảm có được phép thực thi không dựa vào xác thực trước khi thực thi hàm, và chỉ thực thi nếu đủ điều kiện. - -```py -# This is an example, function body just return False for testing -def is_authenticated(): - # Check if user is authenticated - return False - -def authentication_decorator(func): - def wrapper(*args, **kwargs): - if is_authenticated(): - result = func(*args, **kwargs) - return result - else: - print("Authentication failed. Access denied.") - return None - return wrapper - -@authentication_decorator -def perform_sensitive_operation(): - # Do something sensitive - pass - -perform_sensitive_operation() -# Output: Authentication failed. Access denied. -``` - -## Câu hỏi thường gặp (FAQ) - -### 1. Có thể dùng decorator cho phương thức của class (class methods) hoặc phương thức tĩnh không (static methods)? - -> Có, bạn vẫn có thể dùng và cách dùng không khác gì cả. - -### 2. Sử dụng decorator làm mất thông tin metadata của hàm gốc? - -Khi áp dụng decorator lên một hàm, thông tin metadata như tên hàm, tên module và tài liệu sử dụng của hàm có thể bị mất đi. Sử dụng hàm functools.wraps để bảo toàn các thông tin metadata này. Thêm dòng @wraps(func) trước khai báo của wrapper function trong decorator. - -```py -import functools - -def uppercase_decorator(func): - """This decorator turns the result of the function into uppercase.""" - @functools.wraps(func) # Dòng này giúp giữ nguyên metadata của hàm được decorate - def wrapper(*args, **kwargs): - result = func(*args, **kwargs) - return result.upper() - return wrapper - -@uppercase_decorator -def say_hello(): - """This function returns a greeting.""" - return "Hello, world!" - -print("Function name: ", say_hello.__name__) -print("Help: ", say_hello.__doc__) -``` - -Kết quả trước và sau khi có `@functools.wraps(func)`: - -``` -# Trước -Function name: wrapper -Help: None - -# Sau -Function name: say_hello -Help: This function returns a greeting. -``` - diff --git a/blog/2024-04-01-beam-search.md b/blog/2024-04-01-beam-search.md deleted file mode 100644 index 08992e4..0000000 --- a/blog/2024-04-01-beam-search.md +++ /dev/null @@ -1,236 +0,0 @@ ---- -title: Beam search algorithm -authors: hieunv -tags: [beam search, machine learning, natural language processing, vietnamese] -description: Beam search là một thuật toán tìm kiếm heuristic được sử dụng trong nhiều bài toán như dịch máy, nhận dạng giọng nói, tóm tắt văn bản,… Đó là các bài toán NLP có đầu ra liên quan đến việc tạo một chuỗi các từ. Trong bài viết này, Behitek sẽ cùng bạn đi tìm hiểu về thuật toán beam search, các ưu điểm của nó và đi vào thực hành sử dụng beam search trong bài toán khôi phục dấu thanh cho tiếng Việt không dấu. -image: /img/blog/thuat-toan-beam-search.webp ---- - -Beam search là một thuật toán tìm kiếm heuristic được sử dụng trong nhiều bài toán như dịch máy, nhận dạng giọng nói, tóm tắt văn bản,… Đó là các bài toán NLP có đầu ra liên quan đến việc tạo một chuỗi các từ. Trong bài viết này, Behitek sẽ cùng bạn đi tìm hiểu về thuật toán beam search, các ưu điểm của nó và đi vào thực hành sử dụng beam search trong bài toán khôi phục dấu thanh cho tiếng Việt không dấu. - - - -Sau khi đọc xong bài viết này, bạn sẽ có được những kiến thức nhất định về: - -- Khi nào chúng ta nên sử dụng thuật toán beam search -- Hiểu thuật toán tìm kiếm tham lam (greedy search) và cách cài đặt bằng Python -- Hiểu thuật toán beam search và cách cài đặt nó trong Python -- Ứng dụng beam search vào bài toán khôi phục dấu thanh (sắc, hỏi, huyền, ngã, nặng) cho tiếng Việt. - -## Tại sao sử dụng beam search? - -Trong các bài toán NLP như dịch máy (machine translation), tạo caption ảnh tự động (image caption generation), tóm tắt văn bản (text summarization), tổng hợp tiếng nói (auto speech recognition), … yêu cầu đầu ra của mô hình là chuỗi các từ có trong từ điển. - -Thường thì mỗi từ trong chuỗi từ mà mô hình của các bài toán như trên dự đoán (predict) sẽ đi kèm theo một phân phối xác suất tương ứng. Khi đó, bộ Decoder của mô hình sẽ dựa trên phân bố xác suất đó để tìm ra chuỗi từ phù hợp nhất. - -Tìm kiếm chuỗi từ phù hợp nhất yêu cầu chúng ta cần duyệt qua tất cả các chuỗi từ có thể có từ dự đoán của mô hình. Thường thì từ điển của chúng ta sẽ có kích thước rất lớn, với các bài toán về tiếng Việt thì khoảng trên dưới 30.000 từ khác nhau (bao gồm các từ tiếng anh phổ biến, tiếng Việt thuần thì ít hơn) hoặc nếu làm với dữ liệu tiếng anh thì kích thước bộ từ điển còn lớn hơn nhiều. - -Khi đó, ước lượng không gian tìm kiếm của chúng ta sẽ là **kích thước từ điển lũy thừa với độ dài của chuỗi cần dự đoán**, O(n!) - một chi phí rất lớn! -![Độ phức tạp thuật toán](/img/blog/image-1711979010411.png) - -Do chi phí tìm kiếm là quá lớn. Trên thực tế , chúng ta thường dùng một thuật toán tìm kiếm heuristic để có được một kết quả tìm kiếm đủ tốt (good enough) cho mỗi dự đoán thay vì phải tìm kiếm toàn cục. - -Mỗi chuỗi từ sẽ được gán một số điểm dựa trên xác suất phân bố của chúng, thuật toán tìm kiếm sẽ dựa trên điểm số này để đánh giá các chuỗi từ này. Có 2 thuật toán tìm kiếm phổ biến giúp tìm ra chuỗi từ “đủ tốt” đó là tìm kiếm tham lam (greedy search) và beam search. - -## Greedy search vs Beam search - -Trong phần này, mình sẽ cùng các bạn đi làm rõ ý tưởng của 2 thuật toán greedy search và beam search. Từ đó, bạn có thể thấy được ưu điểm, nhược điểm riêng của mỗi thuật toán cũng như lý do tại sao beam search hoạt động hiệu quả hơn. - -- Giải thuật tìm kiếm tham lam (greedy search) khởi đầu với chuỗi rỗng. Tại mỗi bước, nó thực hiện tìm kiếm toàn bộ trên không gian của bước đó và chỉ lấy duy nhất 1 kết quả có điểm số cao nhất và bỏ qua tất cả các kết quả khác. Sang bước tiếp theo, nó chỉ mở rộng tìm kiếm từ kết quả duy nhất trước đó. -- Giải thuật toán kiếm beam search cũng khởi đầu với chuỗi rỗng. Tại mỗi bước, nó thực hiện tìm kiếm toàn bộ trên không gian của bước đó và lấy ra k kết quả có điểm số cao nhất thay vì chỉ lấy 1 kết quả cao nhất. Hình ảnh dưới đây sẽ cho bạn thấy rõ nhất cách hoạt động của beam search với k=2. - -![Mô phỏng Beam search với k = 2](/img/blog/beam-search.webp) - -**Nhận xét:** - -- Thuật toán beam search với k = 1 chính là thuật toán greedy search. Dẫn tới đôi khi kết quả cuối cùng của greedy search khác xa so với kết quả mong đợi. -- Thuật toán beam search tại mỗi bước giữ lại k kết quả tốt nhất. Điều đó làm cho phương pháp tìm kiếm này đạt được kết quả tối ưu toàn cục tốt hơn. -- Trên thực tế, giá trị k của beam search thường từ 5 đến 10. Giá trị k lớn hơn cho kết quả có thể tốt hơn nhưng bạn sẽ phải đánh đổi với chi phí tìm kiếm. - -## Thực hành với beam search -Trong phần thực hành này, mình sẽ thử dùng beam search vào bài toán khôi phục dấu câu cho tiếng Việt không dấu. Đại loại là bài toán sẽ cố gắng đưa một câu tiếng Việt không dấu (toi yeu viet nam) thành câu tiếng việt có dấu đầy đủ (tôi yêu việt nam). - -**Ý tưởng thực hiện** -1. Từ câu tiếng Việt không dấu, mình sẽ sinh ra tất cả các khả năng có thể đánh dấu cho câu đó. -2. Sử dụng mô hình ngôn ngữ (language model) để xác định điểm số (phân bố xác suất) của các từ, chuỗi từ. -3. Áp dụng thuật toán beam search đã trình bày ở trên để tìm kiếm kết chuỗi từ (đã được thêm dấu câu) phù hợp nhất. - -Bây giờ chúng ta sẽ bắt tay vào thực hiện từng bước nhé. Tất cả phần hướng dẫn sau đây đều được thực hiện bằng ngôn ngữ Python. - -> Mình sẽ không đề cao hiệu quả (độ chính xác ở đây), mục tiêu là giải thích & cài đặt beam search để bạn dễ hiểu nhất. - -### Tìm tất cả các cách đánh dấu câu - -Vì việc thêm dấu câu chỉ áp dụng với các từ tiếng Việt (bao gồm các nguyên âm u, e, o, a, i, y và phụ âm d). Do đó, mình sẽ xây dựng bộ từ điển để lưu tất cả các khả năng đánh dấu câu của một từ tiếng Việt bất kỳ theo dạng này: - -``` -'duoc' = {'được', 'dược', 'dước', 'đuộc', 'duộc', 'duốc', 'đước', 'đuốc'} -``` -**Ý tưởng**: Với mỗi từ, xóa dấu của nó để xem nó thuộc về key nào, rồi thêm nó vào đúng chỗ nó thuộc về. - -```py -# Tạo bộ từ điển sinh dấu câu cho các từ không dấu -map_accents = {} -for word in open('vn_syllables.txt').read().splitlines(): - word = word.lower() - no_accent_word = remove_vn_accent(word) - if no_accent_word not in map_accents: - map_accents[no_accent_word] = set() - map_accents[no_accent_word].add(word) -``` - -Vậy là giờ mình có thể sinh ra tất cả các khả năng đánh dấu câu của một từ tiếng Việt bất kỳ rồi. Vấn đề này đã xong, ta chuyển sang bước tiếp theo. - -### Xây dựng mô hình ngôn ngữ - -Thành bại của bài toán nằm ở phần này nha các bạn. Mô hình ngôn ngữ sẽ quyết định độ chính xác của bài toán thêm dấu câu này. Do đó, muốn bài toán chính xác cao thì chúng ta cần tập trung làm tốt phần này nhé. Nhưng trong bài này thì mình làm rất đơn giản thôi ^^. - -Mô hình ngôn ngữ mà mình sử dụng ở phần này là mô hình 2-gram 1 chiều (chỉ thống kê từ kế tiếp). Nó được xây dựng từ tập dữ liệu 19GB tin tức tiếng Việt được anh [binhvq](https://github.com/binhvq/news-corpus) chia sẻ. Kết quả thống kê này được bạn [Hoàng Đức Hiền](https://www.facebook.com/fsflv) chia sẻ cho mình. - -Cách xây dựng: Thống kê số lần xuất hiện của mỗi từ trong tập dữ liệu (corpus), và thống kê xem có các từ nào xuất hiện sau một từ kèm theo số lần xuất hiện. Ví dụ, dưới đây là kết quả thống kê trên từ lập(đã xóa bớt vì dài quá). - -Trong đó: “s” là từ đang thống kê, “sum” là số lần từ “s” xuất hiện trong corpus, “next” là thống kê các từ liền sau nó và số lần xuất hiện kèm theo. - -``` -{ - "s": "lập", - "sum": 2461684, - "next": { - "tức": 297978, - "công": 93254, - "một": 68355, - "biên": 66212, - "và": 62925, - "trường": 41511, - "các": 40176, - "trình": 39124, - "ra": 36957, - "gia": 33068, - "luận": 31702, - "dự": 30768, - "hồ": 30576, - "nghiệp": 30217, - "kỷ": 28633, - "lại": 28430, - "pháp": 28192, - "được": 27729, - "của": 27478, - "quan": 26738, - "với": 22868, - "kế": 21957, - "hội": 21293, - "tự": 21167, - "từ": 20509, - "năm": 20400, - "nên": 20115, - "ban": 19797, - "tổ": 19152, - "quỹ": 18333, - "đoàn": 17237, - "vào": 16903, - "ở": 16846, - "trong": 16546, - "chính": 15905, - "cú": 14588, - "tại": 14292, - "quy": 13515, - "cho": 13136, - "mới": 12697, - "thành": 12260, - "để": 12135, - "đã": 11991, - "danh": 10789, - "dân": 10235, - "có": 10203, - ........... - } -} -``` - -**Vậy tính xác suất cho một từ trong chuỗi từ ra sao?** - -Do đây là mô hình ngôn ngữ n-gram với n = 2, nên mình sẽ tính xác suất có điều kiện của 1 từ (w2) chỉ phụ thuộc vào 1 từ trước nó (w1): - -$$ -P(w1, w2) = \frac{Count(w1w2)}{Count(w1)} -$$ - -Ví dụ, với chuỗi từ “lập tức”, ta có: P (lập, tức) = Count(“lập tức”) / Count(“lập”) = 297978/2461684 ≈ 0.121. - -Ngoài ra, để tránh việc mô hình ngôn ngữ trả về xác suất bằng 0 với các từ out of vocab (OOV) thì chúng ta có thể áp dụng thêm kỹ thuật smoothing. Có rất nhiều kỹ thuật smoothing khác nhau, bạn đọc quan tâm có thể tìm hiểu thêm ở tài liệu tham khảo số [3]. - -```py -# tính xác suất dùng smoothing -def get_proba(current_word, next_word): - if current_word not in lm: - return 1 / total_word; - if next_word not in lm[current_word]['next']: - return 1 / (lm[current_word]['sum'] + vocab_size) - return (lm[current_word]['next'][next_word] + 1) / (lm[current_word]['sum'] + vocab_size) -``` - -**Nhận xét:** - -- Mô hình ngôn ngữ dùng trong bài thực hành này là 2-gram, nếu có thể thì ta nên dùng mô hình 3-gram hoặc 4-gram sẽ cho kết quả tốt hơn. Và dùng thêm kỹ thuật smoothing xịn hơn nữa nhé! -- Mô hình ngôn ngữ này thiếu từ bắt đầu (xác định từ bắt đầu của câu) và từ kết thúc (xác định từ kết thúc câu) câu nên cũng làm ảnh hưởng đáng kể tới độ chính xác của bài toán. -- Bạn có thể sử dụng công cụ xây dựng mô hình ngôn ngữ như KenLM hay SRILM để dễ dàng hơn, và kích thước LM cũng nhỏ hơn nhiều ^^ -- Nên dùng mô hình ngôn ngữ được xây dựng từ dữ liệu có cùng miền (tin tức, giải trí, mạng xã hội,…) với bài toán của bạn. - -### Tìm cách đánh dấu câu tốt nhất -Chúng ta sẽ xây dựng một hàm tìm kiếm beam search để tìm ra cách đánh dấu câu phù hợp nhất. Hàm sẽ nhận vào chuỗi từ không dấu và tham số k. Tại mỗi bước lặp, từ hiện tại sẽ được ghép cặp với tất cả các khả năng đánh dấu của từ phía sau nó. Ở mỗi bước đó thì điểm số của chuỗi từ sẽ được tính bằng cách nhân các xác suất với nhau và chỉ giữ lại (lấy) k chuỗi từ có điểm số cao nhất. Quá trình tiếp tục cho tới khi kết thúc câu. - -Do mô hình ngôn ngữ mình dùng thiếu token bắt đầu, từ đầu tiên sẽ không có phân bố xác suất 2-gram nên bước đầu tiên mình lấy tất cả các khả năng. Nói là tất cả nhưng không nhiều đâu, làm vậy cho chính xác hơn! - -Do giá trị xác suất của các cặp từ là các giá trị rất bé, nên nếu ta nhân chúng với nhau liên tục sẽ có thể bị tràn số. Để khắc phục vấn đề này, người ta thường sử dụng hàm log để giữ cho giá trị xác suất đủ lớn. - -```py -# hàm beam search -def beam_search(words, k=3): - sequences = [] - for idx, word in enumerate(words): - if idx == 0: - sequences = [([x], 0.0) for x in map_accents.get(word, [word])] - else: - all_sequences = [] - for seq in sequences: - for next_word in map_accents.get(word, [word]): - current_word = seq[0][-1] - proba = get_proba(current_word, next_word) - # print(current_word, next_word, proba, log(proba)) - proba = log(proba) - new_seq = seq[0].copy() - new_seq.append(next_word) - all_sequences.append((new_seq, seq[1] + proba)) - # sắp xếp và lấy k kết quả ngon nhất - all_sequences = sorted(all_sequences,key=lambda x: x[1], reverse=True) - sequences = all_sequences[:k] - return sequences -``` - -### Thử nghiệm và đánh giá -Việc xây dựng bài toán khôi phục dấu câu sử dụng mô hình ngôn ngữ và thuật toán tìm kiếm beam search đã hoàn thành. Và bây giờ là lúc để chúng ta kiểm tra chất lượng của mô hình. - -![Minh họa sử dụng beam search + language model cho khôi phục dấu thanh](/img/blog/image-1711981101023.png) - -Đó mới chỉ là một từ, mình đã thử xây dựng bộ test gồm 4983 câu văn trong tập chủ đề tin tức (cùng miền với dữ liệu xây dựng mô hình ngôn ngữ) và đánh giá nó bằng độ chính xác (Accuracy) thì được kết quả như dưới đây: - -![Kết quả đánh giá sơ bộ bài toán thêm dấu câu tiếng Việt -](/img/blog/image-1711985331038.png) - -Đúng hơn ra bài toán này nên dùng [BLEU score](https://en.wikipedia.org/wiki/BLEU) để đánh giá mới đúng nha các bạn. Bởi vì dùng accuracy thì một từ gán dấu sai là coi như sai rồi (trên thực tế thì vẫn chấp nhận được, vì đôi khi đến người cũng chả biết đúng sai thế nào nêú thiếu ngữ cảnh, “em dam dang lam”). - -Source code của bài hướng dẫn này mình cung cấp đầy đủ tại https://github.com/behitek/beam-search-tutorial - -Ngoài ra, thay vì sử dụng n-gram truyền thống để giải bài toán thêm dấu tiếng Việt thì bạn có thể sử dụng mô hình dịch máy deep learning sẽ cho kết quả tốt hơn, xem cách đó ở đây. - -## Kết luận -Hi vọng qua bài viết này, các bạn đã nắm được cách hoạt động cũng như cách tự cài đặt thuật toán beam search. Cũng như nắm được lý do tại sao beam search lại được sử dụng trong các bài toán NLP sinh chuỗi. - -Nếu có bất kỳ góp ý hoặc thắc mắc nào liên quan tới bài viết này, hãy để lại 1 bình luận phía dưới. Hẹn gặp lại các bạn ở các nlp tutorial tiếp theo. - -## Tài liệu tham khảo -Dưới đây là một số tài liệu tham khảo liên quan rất đáng để đọc nếu bạn đang tìm hiểu và muốn tìm hiểu sâu hơn những nội dung mình trình bày trong bài này cũng như kiến thức liên quan: - -1. [Dive into deep learning (Beam search)](https://d2l.ai/chapter_recurrent-modern/beam-search.html). -2. [How to Implement a Beam Search Decoder for Natural Language Processing](https://machinelearningmastery.com/beam-search-decoder-natural-language-processing/) -3. [N-gram language models](https://web.stanford.edu/~jurafsky/slp3/3.pdf) diff --git a/blog/2024-07-18-rag-in-production.md b/blog/2024-07-18-rag-in-production.md deleted file mode 100644 index 5c6d17a..0000000 --- a/blog/2024-07-18-rag-in-production.md +++ /dev/null @@ -1,182 +0,0 @@ ---- -title: RAG In Production - Best Practices Notes -authors: hieunv -tags: [LLM, RAG, chatbot, natural language processing] -description: Retrieval-Augmented Generation (RAG) method are transforming the landscape of natural language processing by combining the strengths of retrieval-based and generative models (LLMs). When deployed in production, RAG systems can provide more accurate and contextually relevant responses. This guide outlines best practices for implementing RAG models in a production environment, ensuring robustness, scalability, and efficiency. -image: /img/blog/rag-in-production.png -comments: true # for Disqus ---- - -Retrieval-Augmented Generation (RAG) method are transforming the landscape of natural language processing by combining the strengths of retrieval-based and generative models (LLMs). When deployed in production, RAG systems can provide more accurate and contextually relevant responses. This guide outlines best practices for implementing RAG models in a production environment, ensuring robustness, scalability, and efficiency. - - - -## Why RAG? - -The exact question is: Why using RAG instead of only LLM models? Consider the two-main reasons: - -- Reduce hallucination: RAG can provide more accurate and contextually relevant responses by combining the strengths of retrieval-based and generative models. - -- External knowledge / Up-to-date information: With In-Context Learning, we can added new knowledge to the model without retraining the model. - -Well, but fine-tuning a LLM model can also adding the external knowledge to the model, then improve the model's performance. So, the question is: When should we use RAG instead of fine-tuning a LLM model? - -Take a look at the following comparison: - -![Comparison between RAG and Fine-tuning](/img/blog/RAG_FT_Table.jpg 'Comparison between RAG and Fine-tuning') -
-Comparison between RAG and Fine-tuning, source: [Tongji-KGLLM/RAG-Survey](https://github.com/Tongji-KGLLM/RAG-Survey/) -
- -Both of RAG and fine-tuning have their own pros and cons. But if you can accept the cons of RAG, then RAG is more suitable for the following cases: -- When the external knowledge is dynamic and frequently updated. -- Hallucination: RAG can provide more accurate and contextually relevant responses. -- Controlability: RAG can provide more control over the generation process. - -:::warning - -The above comparison is not correct with OpenAI fine-tuning mechanism. It is not good for adding external knowledge to the model. Please refer to the [OpenAI video](https://youtu.be/ahnGLM-RC1Y?t=1771) for more information. - -::: - -## RAG best practices - -In this section, we will outline best practices for implementing RAG in a production environment. These practices are designed to ensure robustness, scalability, and efficiency. We will first talk about the problem of RAG, then we will provide the best practices to solve the problem. - - -### Try the simple method first - -If you are not sure which RAG method is the best for your problem, try the simple method first. A good starting method is the [RAG with BM25 retrieval](https://github.com/behitek/simple-rag/) method. The BM25 retrieval method is simple, efficient, and effective for many problems. - -BM25 algorithm is a keyword-based retrieval algorithm, some of the following tips can help you improve the performance of the BM25 algorithm: -- Remove the stop words. -- Use the stemming algorithm. -- Convert the text to lowercase. -- Extract and using the keywords from text. -- Generate variants of search queries and documents, i.e., synonyms, paraphrases, etc. - -Even if the BM25 algorithm is not get the good performance, it still can consider as a baseline for the more complex RAG methods. -- You have the baseline to compare with other experiments, to know your future experiments are better or not. -- You can analyze the issues or weakness of the BM25 algorithm, then easier to find the solution for the issues. - -### Maintain the hierarchical structure of your documents. - -The hierarchical structure of your documents is crucial for the performance of RAG models. The hierarchical structure allows the model to efficiently retrieve relevant information and generate contextually relevant responses. When designing your documents, make sure to organize them in a hierarchical manner, with clear headings and subheadings. - -What is the hierarchical structure of a document? -- For a Wikipedia article, the hierarchical structure could be: Title -> Sections -> Subsections -> Paragraphs. -- For PDF documents, the hierarchical structure could be: Title -> Chapters -> Sections -> Subsections. - -For example, if you just extract the text from the PDF without maintaining the hierarchical structure, then every word in the document will be treated equally. - -```txt -## BUSINESS STRATEGIES -- Enhancement of our R&D capabilities to ride on trends and business opportunities arising from the digitalisation of the telecommunications industry; -- Expansion of our product range, which includes the continual development and launch of eco-friendly and ESG products such as “green and innovative antenna and subsystem solution”; -- Strengthening of our sales and marketing capabilities to capture new business opportunities and expand our customer base; -- -- ... -## FUTURE PLANS -... -``` - -Consider the example text above, what happend if you don't maintain the hierarchical structure of the document? -- Your chunking strategy will not work well, since we don't have enough information to make a good chunking. -- If the query is "What are the business strategies?", only the heading matches the query, but the content does not match the query, since the text not contain the relevant words. What happend if the "bussiness stratagy" section is divided into two different chunks? or the "bussiness stratagy" line is the last line of the chunk before? -- Is LLM model easy to understand a document lossing the hierarchical structure? - -**Final words**: Maintain the hierarchical structure of your documents directly and indirectly improve the performance of the RAG model. -- Directly: It is better for LLM to understand the document. -- Indirectly: It is better for the chunking strategy, eg. chunking by section boundaries. -- Indirectly: It is better for the implement the retrieval strategy, eg. indexing the section title to represent the section. - -### Take a look at the chunking strategy - -The chunking algorithm help to divide the document into smaller chunks, but it is also making the "break-in-the-middle" problem. The "break-in-the-middle" problem is when the relevant information is divided into two different chunks. - -Chunking is the process of dividing a document into smaller chunks to improve the efficiency of the retrieval process. The chunking strategy you choose can have a significant impact on the performance of your RAG model. When designing your chunking strategy, consider the following best practices: - -- Use the hierarchical structure of your documents to guide the chunking process. For example, you can chunk the document at section boundaries, then paragraph boundaries, then sentence boundaries. - -- Experiment with different chunk sizes to find the optimal balance between granularity and efficiency. Larger chunks may contain more context, but smaller chunks may be more efficient to process. - -:::tip - -If your chunking is for semantic search (vector similarity), make sure that your chunks are fit into the input max_length of the embedding model. Otherwise, you may lose some information when doing the embedding. - -::: - -Later, you will embedding the chunks into vector space for semantic search. So, your chunking should break document into chunks where each chunk should represent only one piece of information. - -### Consider using text summarization for long documents - -Text summarization can be used to generate concise summaries of long documents, which can improve the efficiency of the retrieval process. By summarizing long documents, you can: -- Remove unnecessary information that may distract the model. -- Retain key information help improve the retrieval process. -- Summarize help reduce the break-in-the-middle problem of the chunking process. -- Summarize can be considered as a semantic chunking, if you summarize the document into chunks. - -Depending on your problem, you can use only the summary or use the summary as additional data. - -### Take a look at the format of (search_query, document) pairs - -In most cases, the search query is a question, while documents are not question. So, the search query and the document are in different formats. This difference in format can make it difficult for the model to retrieve relevant information. - -To address this issue, consider the following best practices: - -- Generate the fake_answer for the search query, then using the fake_answer as the search_query. Then both the search_query and the document are in the same format. This idea is originally from [Gao et al., ACL 2023](https://aclanthology.org/2023.acl-long.99/) paper, HYDE is its short name. - -- Using LLM to generate Q&A pairs from the document, or generate FAQs from the document. Then the search_query and the document are in the same format. - -Personally, I prefer the second solution, because: -- Create a good fake_answer is not easy, it is require a good LLM, such as OpenAI LLMs. -- We have time at data processing, but user don't want to wait long time for the response. - -### Divide and conquer - -Divide and conquer is a technique for solving complex problems by breaking them down into smaller, easier-to-solve subproblems. This traditional approach can apply to many problems, including RAG. - -We can use this approach in both RAG backend (data processing & ingesting) and RAG frontend (user interaction with the pipeline). - -For example, we can divide the document into categories, or organize the data into a hierarchy structure. This helps narrow down the retrieval scope, thus improving the accuracy of the search results. - -On the other hand, we can also use this approach to divide the search query into categories - this is often call query router, which helps route the search query to the right pipeline. Another example is break the query into sub-queries. - -Thats some idea to consider, feel free to explore more or modify it to match your needs. - -### Moving tasks from RAG-frontend to RAG-backend - -Generally, the RAG model is divided into two parts: -- RAG-frontend: Where user interact with the pipeline, eg. input the search query, retrieve the relevant documents, and generate the response. -- RAG-backend: Where you prepare the data, data processing / enrichment, and indexing the data. - -The RAG-frontend is the part that user interact with the pipeline, and run every time the user input the search query. So, it should be fast and efficient. -The RAG-backend is the part that run only one time, can run in the background -> it can be slow. - -So, think about moving the tasks from RAG-frontend to RAG-backend, if it is possible. That is why I prefer convert document to Q&A pairs in the data processing step, instead of generate the fake_answer in the RAG-frontend. - -Consider the below PDF's content (A troubleshooting guide for a software product): - -![](/img/blog/sample-pdf-content.png) - -If you are using the common RAG method, you can see that your RAG won't work well with the above content with the query like: How to fix the error code XX? - -> The reason is the error code (keyword) in the document not directly mention the solution, it is refer the reader go to another section to find the solution. - -There are some approachs such as Chain of Thought, Iteratively Search (Multi-step retrieval) can help you solve the problem. Because they are using LLM after every search to evaluate the relevance of the search result, then decide the next action. - -However, these approaches are on the frontend side, which not only takes time but also costs money. In this case, you should consider moving to RAG-backend side. - -## Conclusion - -RAG models are transforming the landscape of natural language processing by combining the strengths of retrieval-based and generative models. When deployed in a production environment, RAG systems can provide more accurate and contextually relevant responses. By following the best practices outlined in this guide, you can ensure the robustness, scalability, and efficiency of your RAG implementation. - -In summary, the best practices for implementing RAG in a production environment include: -- Try the simple method first. -- Maintain the hierarchical structure of your documents. -- Take a look at the chunking strategy. -- Consider using text summarization for long documents. -- Take a look at the format of (search_query, document) pairs. -- Moving tasks from RAG-frontend to RAG-backend. - -Thank you for reading this guide, and we hope you found it helpful. If you have any questions or would like to learn more about RAG, feel free to reach out to us. \ No newline at end of file diff --git a/blog/2024-10-08-opensource-github-copilot.md b/blog/2024-10-08-opensource-github-copilot.md deleted file mode 100644 index 82fbbfc..0000000 --- a/blog/2024-10-08-opensource-github-copilot.md +++ /dev/null @@ -1,140 +0,0 @@ ---- -title: Open-source AI code assistant Setup Guide -authors: hieunv -tags: [Tool, VSCode, LLM] -description: Setup your local AI Code Assistant with just a few steps to replace Github Copilot - Help you save money and better data privacy. -image: /img/blog/continue-dev.png -comments: true # for Disqus ---- - -Looking for a good alternative to Github Copilot? The answer is [Continue.dev](https://continue.dev) - An open source AI code assistant that can help you write better code faster. Again, I want to emphasize that it is an open source project, and it give you more control over your data by hosting them on your own computer, including its model. - - - -## Introduction - -An AI coding assistant often have two main functions (including Github Copilot): -- Code autocomplete (to suggest next lines of code based on the current context). -- Chatbot (to interact with your code). - -The two functions are depend on the large language model (LLM), which is trained by massive data - I guess the training data for this kind of LLM contains a lot of stackoverflow and Github data. - -This article will guide you how to setup a local AI code assistant with Continue (for AI code assistant functions) and Ollama (for self-hosted LLM). - -## Prerequisites - -Because we are self-hosted our AI code assistant, so we need some resources to run it. - -- For minimum, your machine must able to hosted at least a 3B LLM model, i.e. 6GB VRAM or more. -- For better performance, you can use a GPU with 8GB VRAM or more. -- Or you have a macbook air M1 or above - -**Note:** -- Continue support both VSCode and JetBrains IDEs (PyCharm, IntelliJ, WebStorm,...). I will use VSCode as an example in this article. -- This article is for Ubuntu/Debian users, but you can follow the same steps to setup on other OS. - -## Installation - -### Step 1 - Install Ollama - -To install (or upgrade) Ollama, run the following command: - -```sh -curl -fsSL https://ollama.com/install.sh | sh -``` - -This will download and install the latest version of Ollama on your machine. Later you can use `ollama` to manage your LLM models. - -### Step 2 - Download LLMs - -Because two functions (autocomplete and chatbot) are very different, so it is better if we have two separate LLMs for each function. But if your machine is not powerful enough, you can use a single model for both functions. - -**For autocomplete**: -- Try `deepseek-coder:1.3b-base` if you don't have much resources. -- Try `deepseek-coder:6.7b-base` if your machine is powerful enough. - -To download a model, run the following command (eg: `deepseek-coder:6.7b-base`): - -```sh -ollama pull deepseek-coder:6.7b-base -``` - -**For chatbot (Optional):** - -You have many option for this function, you can pick one of bellow: -- Qwen2.5 Coder 7B(`qwen2.5-coder`) -- Llama3.1 8B (`llama3.1`) -- DeepSeek Coder 2 16B (`deepseek-coder-v2`) -- ... - -To download a model, run the following command (eg: `Qwen2.5 Coder`): - -``` -ollama run qwen2.5-coder -``` - -The recommended models above is not by me, its from Model Setup documentation of Continue.dev, you can check it out at: -- [Autocomplete Model Setup](https://docs.continue.dev/autocomplete/model-setup) -- [Chatbot Model Setup](https://docs.continue.dev/chat/model-setup) - -They said that the models they recommended are tested and work best with their platform (i.e. the prompting) to do the tasks. You should check their documentation for an up-to-date information. - -### Step 3. Keep your LLM loaded in memory - -By default, Ollama will unload the model after 5 minutes of inactivity. If you want to keep it loaded in memory, run this command: - -```sh -curl http://localhost:11434/api/generate -d '{"model": "deepseek-coder:6.7b-base", "keep_alive": -1}' -``` - -The `keep_alive = -1` means that the model will be kept loaded in memory forever. You can unload it by setting `keep_alive = 0`. - -For more information, check out [Ollama FAQ](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-keep-a-model-loaded-in-memory-or-make-it-unload-immediately). - -### Step 4. Install Continue.dev extension - -You can easy install Continue.dev extension from Visual Studio Code Marketplace: [Continue.dev](https://marketplace.visualstudio.com/items?itemName=Continue.continue) or from VSCode Sidebar (Ctrl+Shift+X). - -![](/img/blog/continue-ext.png) - -### Step 5. Configure your Continue.dev extension - -Open the `config.json` file in the Continue.dev extension and take a look at two keys: `models` and `tabAutocompleteModel`, following the example below. - -```json -{ - "models": [ - { - "model": "AUTODETECT", - "title": "Ollama", - "completionOptions": {}, - "apiBase": "http://localhost:11434", - "provider": "ollama" - } - ], - "tabAutocompleteModel": { - "model": "deepseek-coder:6.7b-base", - "title": "deepseek-coder", - "completionOptions": {}, - "apiBase": "http://localhost:11434", - "provider": "ollama" - }, - ... -} -``` - -Now, you can start using your own AI coding assistant. If you having issues with the extension, please check out [Continue.dev Troubleshooting](https://docs.continue.dev/troubleshooting) page. - -Here's an improved version of your conclusion with a more polished and engaging tone: - ---- - -## Conclusion - -In this article, we've walked through the steps to install and configure the **Continue.dev** extension for VSCode, enabling you to integrate your own AI coding assistant seamlessly into your workflow. I hope this guide helps enhance your development experience. - -If you have any questions or feedback, feel free to drop a comment below—I'll do my best to respond promptly. - -For further details and updates, be sure to explore the [Continue.dev documentation](https://docs.continue.dev/). - -Finally, a special thanks to the **Ollama**, the **Continue.dev team**, and the entire open-source community for their valuable contributions in making this tool available to all. diff --git a/blog/2024-10-10-python-313-free-threaded.md b/blog/2024-10-10-python-313-free-threaded.md deleted file mode 100644 index b5f0a5f..0000000 --- a/blog/2024-10-10-python-313-free-threaded.md +++ /dev/null @@ -1,139 +0,0 @@ ---- -title: Free Threaded Mode in Python3.13 (GIL disabled) -authors: hieunv -tags: [Python, GIL] -description: Python 3.13 just release recently, with an amazing new feature called "Free threaded mode". This is a great improvement for the performance of your code when you are using threads. -image: /img/blog/python-313.png -comments: true # for Disqus ---- - -Python 3.13 just release recently, with an amazing new feature called "Free threaded mode". This is a great improvement for the performance of your code when you are using threads. This article shows how to enable this feature (not enabled by default) and shows "free threaded mode" impact on the performance of your code. - - - -## Install Free Threaded Python - -### Windows & MacOS users - -For Windows and MacOS users, just download the latest installer from [Python website](https://www.python.org/downloads/). When you install Python, there is a checkbox to enable "Free threaded mode" when you select "Customize installation" option. - -![](/img/blog/python313-no-gil.png) - -### Ubuntu users - -For Ubuntu users, you can enable this feature by running the following command in your terminal: - -```bash -sudo add-apt-repository ppa:deadsnakes -sudo apt-get update -sudo apt-get install python3.13-nogil -``` - -### Verify Free Threaded Mode is enabled - -After installing the package, you can run your code with `python3.13` (original) and `python3.13-nogil` or `python3.13t` (free threaded Python). - -Check out this [article](https://py-free-threading.github.io/installing_cpython/#linux-distros) for more details on how to install Python 3.13 experimental on Linux distros. - -To verify your Python has "Free threaded mode" enable, you can use the following command: - -```bash -python3.13t -VV -Python 3.13.0 experimental free-threading build (main, Oct 8 2024, 08:51:28) [GCC 11.4.0] -``` - - -## Free Threaded Mode Performance - -### Experiment Setup - -Let's see the impact of free threaded mode on a simple code below: -- I have a function `worker` that does some computation and returns the sum of numbers from 0 to 10 million. -- I have the "Test 1" to run the `worker` function 5 times, sequentially. -- I have the "Test 2" to run the `worker` function in parallel using multiple threads, with number of threads is 5. -- I do measure the execution time of both tests. - -```py -import sys -import threading -import time - -print("Python version : ", sys.version) - -def worker(): - sum = 0 - for i in range(10000000): - sum += i - - -n_worker = 5 -# Single thread - -start = time.perf_counter() -for i in range(n_worker): - worker() -print("Single Thread: ", time.perf_counter() - start, "seconds") - - -# Multi thread -start = time.perf_counter() -threads = [] -for i in range(n_worker): - t = threading.Thread(target=worker) - - threads.append(t) - t.start() - -for t in threads: - t.join() -print("Multi Thread: ", time.perf_counter() - start, "seconds") - -``` - -Later, I will run this code with normal Python (`python3.13` binary) and free threaded Python (`pypy3.13t` binary). - -### Results - -First, run the test with `python3.13`: -``` -python3.13 gil_test.py -Python version : 3.13.0 (main, Oct 8 2024, 08:51:28) [GCC 11.4.0] -Single Thread: 1.4370562601834536 seconds -Multi Thread: 1.3681392602156848 seconds -``` - -Then, run the test with `pypy3.13t`: -``` -python3.13t gil_test.py -Python version : 3.13.0 experimental free-threading build (main, Oct 8 2024, 08:51:28) [GCC 11.4.0] -Single Thread: 1.862126287072897 seconds -Multi Thread: 0.3931183419190347 seconds -``` - -I also trying with `python3.11`: -``` -python3.11 gil_test.py -Python version : 3.11.3 (main, Apr 25 2023, 16:40:23) [GCC 11.3.0] -Single Thread: 1.753435204969719 seconds -Multi Thread: 1.457715731114149 seconds -``` - -### Result Analysis - -Python default has GIL (Global Interpreter Lock) locking mechanism, making multi-threaded actually not parallel. You can see the time processing of single thread is similar to multi thread. - -With `python3.11t` (free threaded mode), multi threaded performance is much faster than single threaded. So, multi threading now actually parallel. - -:::info - -But, do you see Single Thread test in `python3.13t` a bit slower than `pypy3.13`? - -> I don't really understand why, so let me know if you have any explanation. - -::: - -## Conclusion - -I think it is good to use multi threading in python for parallel processing. But, without GIL locking mechanism, it requires developer to be careful about the "thread safety", ie. sharing data between threads. - -Also, we need to wait for libraries and packages update to fully support free threaded mode. That's one of the reason why this "free threaded mode" is not enabled by default for now. But, I think it will be a good feature in future. \ No newline at end of file diff --git a/blog/2024-11-17-flux-lora.md b/blog/2024-11-17-flux-lora.md deleted file mode 100644 index f9e0e00..0000000 --- a/blog/2024-11-17-flux-lora.md +++ /dev/null @@ -1,352 +0,0 @@ ---- -title: Fine-tuning Flux.1-dev LoRA on yourself -authors: hieunv -tags: [Flux, LoRa, Text to Image] -description: This blog serves as my personal guide to fine-tuning Flux.1-dev LoRA to generate high-quality, lifelike images of myself—all without the hassle of taking photos. -image: /img/blog/flux-lora.png -comments: true ---- - -Impressed by the image-generation capabilities of Flux, I decided to dive in and experience it for myself. This blog is a personal guide to fine-tuning Flux 1.1-dev LoRA to create high-quality, lifelike images of myself—all without the hassle of taking photos. As someone who prefers working with computers over cameras and exploring AI for fun rather than research, this experiment lets me sidestep my camera shyness while enjoying the cutting-edge possibilities of AI creativity. - - - -
- Here are some images of myself generated by Flux.1-dev LoRA - Flux.1-dev LoRA - Flux.1-dev LoRA - Flux.1-dev LoRA -
- - -## Prerequisites -This tutorial is not the only way to fine-tune Flux.1-dev LoRA, you can train Flux.1-dev LoRA on Google colab, or using online services. But because this tutorial is using local GPU, you need to prepare the following to run it smoothly: -* You are a developer -* OS: Ubuntu Linux -* Nvidia GPU, at least 12GB VRAM -* Python 3.10 or 3.11 - -I did not write the code for this training. Instead, I used [SimpleTuner](https://github.com/bghira/SimpleTuner/blob/main/documentation/quickstart/FLUX.md) to fine-tune Flux.1-dev LoRA. You can refer directly to the SimpleTuner documentation for instructions on fine-tuning Flux.1-dev LoRA. - -## Environment Setup - -``` -$ git clone --branch=release https://github.com/bghira/SimpleTuner.git - -$ cd SimpleTuner - - -$ python -m venv .venv - -$ source .venv/bin/activate - -$ pip install -U poetry pip - -$ poetry config virtualenvs.create false - -$ poetry install -``` - -## Dataset preparation - -### The dataset - -To create your own Flux model, you need to prepare some of your images, just 5 images is enough, but 20 images is better. The quality of the image contributes greatly to the effectiveness of the model, consider the following factors: -* WebP, JPG, and PNG formats are all supported -* Use 1024x1024 or higher resolution if possible. -* The photo shows a clear face, without accessories, eg glasses. -* Photos share different angles of the face. -* The photo collection should be diverse (e.g. angles, outfits, ...) -* Photos should be taken within the last 6 months. - -![Sample dataset](/img/blog/flux-dataset.png) -Credits: [Replicate](https://replicate.com/blog/fine-tune-flux-with-faces) - -### The trigger word - -When fine-tuning Flux with your images, it’s essential to choose a unique trigger word that represents your face. This trigger word will act as a key for generating images of your likeness. Ideally, the trigger word should be distinct and specific to your face, avoiding any overlap with common objects or concepts to ensure accurate results. - -Here are the tips to choose a good trigger word: -* The trigger word should not be a word found in any language dictionary. For example, 'face' or 'flower' is not a good trigger word. -* Make it short, because it will take some space in the prompt. -* Example: hieunv3, behitek are good trigger words. - -**Reason**: The words in dictionary can be easily recognized by the model, and the model will not be able to generate images of your likeness. - -### Generate captions - -For better quality, you should generate a caption for each image. Here, I will use the [Microsoft Florence 2](https://huggingface.co/multimodalart/Florence-2-large-no-flash-attn) model to generate captions for the images. You can also use ChatGPT, llava or llama3.2 to generate captions. - -After captions are generated, add your trigger word to the beginning of the captions. For example (using behitek as the trigger word): - -![Example caption prepare for Flux LoRa training](/img/blog/flux-caption.png) - -Do organize your images and captions in a folder, and put them in a folder with structure like this: - -``` -$ tree . - -├── 1000003258.jpg -├── 1000003258.txt -├── 1000003259.jpg -├── 1000003259.txt -├── 1000003262.jpg -├── 1000003262.txt -├── 1000003263.jpg -├── 1000003263.txt -├── 1000003264.jpg -├── 1000003264.txt -├── 1000003265.jpg -├── 1000003265.txt -├── IMG_20220326_135114.jpg -├── IMG_20220326_135114.txt -├── IMG_20220706_184917.jpg -├── IMG_20220706_184917.txt -├── IMG_20230512_150235.jpg -├── IMG_20230512_150235.txt -├── IMG_20230719_065425.jpg -├── IMG_20230719_065425.txt -``` - -Where *.JPG is the image, and the same file name with extension *.TXT contains the caption of the image. I will assume this dataset directory is located in `/path/to/datasets/behitek-lora`. - -## Training configuration - -Go back to SimpleTuner, you can find the configuration sample in the `config` folder. - -### Create your own config -```bash -cd config/ -cp config.json.example config.json -cp lycoris_config.json.example lycoris_config.json -cp user_prompt_library.json.example user_prompt_library.json -cp multidatabackend.json.example dataset.behitek.json -``` - -I will provide you a sample config for you to fine-tune Flux.1-dev LoRA, with trigger word behitek. - -### The config.json - -This is the config.json file, update the following key: -* `--validation_prompt`, enter your prompt with trigger word. -* `--data_backend_config`, `--output_dir`, `--lycoris_config`, and `--user_prompt_library`, make sure they are correct. - -```json -{ - "--resume_from_checkpoint": "latest", - "--data_backend_config": "config/dataset.behitek.json", - "--aspect_bucket_rounding": 2, - "--seed": 42, - "--minimum_image_size": 0, - "--output_dir": "output/behitek-lycoris-v0.1", - "--lora_type": "lycoris", - "--lycoris_config": "config/lycoris_config.json", - "--max_train_steps": 10000, - "--num_train_epochs": 0, - "--checkpointing_steps": 1000, - "--checkpoints_total_limit": 5, - "--hub_model_id": "simpletuner-lora", - "--push_to_hub": "false", - "--push_checkpoints_to_hub": "false", - "--tracker_project_name": "lora-training", - "--tracker_run_name": "simpletuner-lora", - "--report_to": "tensorboard", - "--model_type": "lora", - "--pretrained_model_name_or_path": "black-forest-labs/FLUX.1-dev", - "--model_family": "flux", - "--train_batch_size": 1, - "--gradient_checkpointing": "true", - "--caption_dropout_probability": 0.1, - "--resolution_type": "pixel_area", - "--resolution": 1024, - "--validation_seed": 42, - "--validation_steps": 1000, - "--validation_resolution": "1024x1024", - "--validation_guidance": 3.0, - "--validation_guidance_rescale": "0.0", - "--validation_num_inference_steps": "20", - "--validation_prompt": "behitek a man sitting on a chair wearing a white shirt, focused on the book he is holding in his hand. On the table in front of him is an object, and behind him are a few people, trees, and a blue sky", - "--mixed_precision": "bf16", - "--optimizer": "adamw_bf16", - "--learning_rate": "1e-4", - "--lr_scheduler": "polynomial", - "--lr_warmup_steps": 100, - "--validation_torch_compile": "false", - "--disable_benchmark": "false", - "--base_model_precision": "int8-quanto", - "--text_encoder_1_precision": "no_change", - "--text_encoder_2_precision": "no_change", - "--lora_rank": 16, - "--max_grad_norm": 1.0, - "--base_model_default_dtype": "bf16", - "--user_prompt_library": "config/user_prompt_library.json" -} -``` -**Notes**: -* I use `base_model_precision=int8-quanto` for VRAM saving. -* Every `validation_steps`, the model will generate validation images, and save them to the `validation_images` folder inside the `output_dir`. -* `train_batch_size=1` is a recommended value (by SimpleTuner) for small dataset. - -### The user_prompt_library.json -The `user_prompt_library.json` file is a list of prompts, you can add prompts here. During the model validation, it will generate images based on these prompts. Example: - -```json -{ - "behitek_1": "behitek A 30-year-old man with a rugged yet stylish look, dressed in a beige explorer jacket with cargo pants, standing on the edge of a cliff at sunset, overlooking a vast canyon. His expression is a mix of awe and determination. The scene is rich with glowing orange and red hues, with wind tousling his hair.", - "behitek_2": "behitek A 30-year-old man with a sharp jawline, wearing a sleek black turtleneck and tailored gray trousers, walking confidently down a bustling city street. Neon lights from billboards reflect on the wet pavement, creating a vibrant, futuristic cityscape." -} -``` - -### The dataset.behitek.json - -The `dataset.behitek.json` file: - -```json - -[ - { - "id": "behitek-subject", - "type": "local", - "crop": true, - "crop_style": "center", - "crop_aspect": "square", - "resolution": 1024, - "minimum_image_size": 1024, - "maximum_image_size": 1024, - "target_downsample_size": 1024, - "resolution_type": "pixel_area", - "cache_dir_vae": "cache/vae/flux/behitek-subject", - "instance_data_dir": "/path/to/datasets/behitek-lora", - "caption_strategy": "textfile", - "instance_prompt": "behitek", - "metadata_backend": "discovery", - "is_regularisation_data": false, - "repeats": 1000 - }, - { - "id": "behitek-subject-512", - "type": "local", - "crop": true, - "crop_style": "center", - "crop_aspect": "square", - "resolution": 512, - "minimum_image_size": 512, - "maximum_image_size": 512, - "target_downsample_size": 512, - "resolution_type": "pixel_area", - "cache_dir_vae": "cache/vae/flux/behitek-subject-512", - "instance_data_dir": "/path/to/datasets/behitek-lora", - "caption_strategy": "textfile", - "instance_prompt": "behitek", - "metadata_backend": "discovery", - "is_regularisation_data": false, - "repeats": 1000 - }, - { - "id": "text-embeds", - "type": "local", - "dataset_type": "text_embeds", - "default": true, - "cache_dir": "cache/text/flux", - "disabled": false, - "write_batch_size": 128 - } -] -``` - -**Notes:** -* Make sure to correct the `instance_data_dir` to your dataset directory. -* If your image dimension is smaller than 1024x1024, you will got the error: `No images were discovered by the bucket ...`. -* There is several `caption_strategy`, but `textfile` is the right setting. -* `repeats` is the number of times to repeat the same image, 1000 is a good number. - - -### The lycoris_config.json - -For the `lycoris_config.json` file, leave it as it is, the default setting is fine. - -## Training - -Still in the root directory of SimpleTuner, run the following command: - -``` -$ tmux new -s train # create a new tmux session -$ source ./venv/bin/activate -./train.sh -``` - -During training, the model will generate images based on the prompts in the `user_prompt_library.json` and `validation_prompt` in the config file, these images is save in your `output_dir`. - -### Training Experience - -- If your dataset consists only of faces (neck and head) with a clean background, you don't need to generate captions. Simply use `caption_strategy: "instanceprompt"`, where the `instanceprompt` serves as the trigger word. In this case, 2000 training steps are sufficient. -- For diverse data (e.g., different outfits, backgrounds, or even the same person in varied settings), generating captions is recommended. With more diverse data, training for more steps is beneficial. For example, 10,000 steps is a good benchmark. -- If you're unsure when the model has converged, check the loss curve in TensorBoard and save additional checkpoints for safety. - -## Inference - -Once the model is trained, you can use the below Python script (not a clean code) to generate images: - -```python -import torch -from diffusers import DiffusionPipeline -from lycoris import create_lycoris_from_weights -import os - -model_id = 'black-forest-labs/FLUX.1-dev' -adapter_file_path = "/path/to/output_dir/pytorch_lora_weights.safetensors" -pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16 -lora_scale = 1.0 -wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer) -wrapper.merge_to() - - -## Optional: quantise the model to save on vram. -## Note: The model was quantised during training, and so it is recommended to do the same during inference time. -from optimum.quanto import quantize, freeze, qint8 -quantize(pipeline.transformer, weights=qint8) -freeze(pipeline.transformer) - -output_dir = os.path.join("output", "behitek-hitech") -os.makedirs(output_dir, exist_ok=True) - - -prompts = [ - "behitek A 30-year-old man with a rugged yet stylish look, dressed in a beige explorer jacket with cargo pants, standing on the edge of a cliff at sunset, overlooking a vast canyon. His expression is a mix of awe and determination. The scene is rich with glowing orange and red hues, with wind tousling his hair.", - "behitek A 30-year-old man with a sharp jawline, wearing a sleek black turtleneck and tailored gray trousers, walking confidently down a bustling city street. Neon lights from billboards reflect on the wet pavement, creating a vibrant, futuristic cityscape.", - "behitek A young man, 30 years old, sitting in a high-tech office filled with holographic screens and futuristic gadgets. He’s wearing a casual blazer over a graphic T-shirt and glasses with a subtle glow, symbolizing augmented reality.", - "behitek A 30-year-old warrior with a chiseled physique, clad in intricate silver armor with glowing blue runes. He holds a massive sword that emits a faint magical aura. He stands on a battlefield surrounded by mythical creatures under a dark, stormy sky.", - "behitek A 30-year-old man with a cheerful demeanor, dressed in outdoor gear, standing beside a sparkling mountain lake. A camera hangs from his neck, and his backpack is packed with supplies. The serene landscape features towering pine trees and a snow-capped peak in the background.", - "behitek A young man, 30 years old, styled in 1980s adventure fashion—khaki shorts, a buttoned shirt, and a leather satchel. He is mid-action, leaping over a crumbling stone bridge in an ancient jungle temple, vines and artifacts all around.", - "behitek A 30-year-old man in a contemporary interpretation of samurai attire, blending traditional armor with urban streetwear. He holds a katana and stands in an empty urban alley lit by paper lanterns, exuding a calm yet powerful presence.", - "behitek A young man in his 30s, dressed in a futuristic spacesuit with a sleek, glowing helmet. He floats in the vastness of space, with a swirling galaxy visible in the background. The stars and nebulae create a stunning, ethereal glow.", - "behitek A 30-year-old man in Renaissance clothing—ruffled shirt and velvet coat—standing in an ornate studio. He is surrounded by canvases, sculptures, and paints. His face reflects deep concentration as he works on a masterpiece.", - "behitek A young man, 30 years old, sitting in a grand library filled with towering shelves of ancient books. He’s wearing a long coat with mystical symbols embroidered in gold, surrounded by floating magical texts and glowing orbs." -] - -import random -for idx, prompt in enumerate(prompts): - for i in range(30): - print(f"Generating image {i}") - seed = random.randint(0, 1000000000) - pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level - image = pipeline( - prompt=prompt, - num_inference_steps=20, - generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(seed), - width=1024, - height=1024, - guidance_scale=3.0, - ).images[0] - image.save(os.path.join(output_dir, f"{idx}_{seed}.png"), format="PNG") -``` - -With the same prompt, try changing the seed to get different results. In the above bad code, I do generate 30 images for each prompt. From many generated images, you can select some good ones. - - -That's it! You have now created a Flux LoRa model. - -## Reference -* [Sample work: salmonrusty/simpletuner-lora](https://huggingface.co/salmonrusty/simpletuner-lora#training-settings) -* [SimpleTuner](https://github.com/bghira/SimpleTuner) -* [Fine-tune FLUX.1 to create images of yourself](https://replicate.com/blog/fine-tune-flux-with-faces) diff --git a/blog/2025-08-31-stop-using-requirements.md b/blog/2025-08-31-stop-using-requirements.md deleted file mode 100644 index fa7b74e..0000000 --- a/blog/2025-08-31-stop-using-requirements.md +++ /dev/null @@ -1,669 +0,0 @@ ---- -slug: stop-using-requirements-txt -title: "Stop Using requirements.txt" -authors: [hieunv] -description: "The requirements.txt is a legacy dependency management tool that is no longer fit for modern Python projects. We need a better dependency management tool." -image: /img/blog/stop-using-requirements.jpg -comments: true -tags: [python, dependency-management, poetry, uv, devops, best-practices] ---- - -It's 3 AM, and your production deployment just failed. Again. The same code that worked perfectly on your local machine is now throwing mysterious import errors on the server. Sound familiar? - - - -## The 3 AM Production Nightmare - -Let me paint you a picture that might feel uncomfortably familiar. You're running a Flask web application in production using Docker containers. Your team just deployed what should have been a routine update—running your test suite with pytest to ensure everything works before the new release. - -Then the container crashes on startup. - -```bash -ImportError: cannot import name 'url_quote' from 'werkzeug.urls' -(/opt/conda/lib/python3.10/site-packages/werkzeug/urls.py) -``` - -Your heart sinks. The exact same code that runs perfectly when you execute `python run.py` is now failing during pytest execution. You haven't changed any Flask or Werkzeug code—so what went wrong? - -Your `requirements.txt` looked innocent enough: - -```txt -Flask==2.2.2 -pytest>=7.0.0 -# ... other dependencies -``` - -The problem? Flask 2.2.2 specified `Werkzeug>=2.2.0` in its dependencies, which seemed reasonable. Your local environment had been running Werkzeug 2.3.7 for weeks without issues. But when pytest was installed fresh in the Docker container, it pulled in the latest available Werkzeug version: 3.0.0. - -Here's the kicker: [Werkzeug 3.0.0 removed the deprecated `url_quote` function](https://werkzeug.palletsprojects.com/en/3.0.x/changes/#version-3-0-0) that Flask 2.2.2 still relied on. Your "compatible" dependency specification had just broken your entire application. - -The impact was immediate and severe. Your CI/CD pipeline was blocked, preventing any deployments. The development team couldn't run tests locally after pulling the latest changes. What should have been a 5-minute deployment turned into a 3-hour emergency debugging session, with the fix being a simple line: `Werkzeug==2.3.7`. - -This scenario plays out in Python teams worldwide, every single day. The root cause isn't developer incompetence or bad luck—it's the fundamental limitations of `requirements.txt` as a dependency management solution. What worked fine for simple scripts in 2008 simply cannot handle the complexity of modern Python applications with dozens of dependencies, each with their own sub-dependencies and version constraints. - -The good news? There's a better way, and migrating is easier than you might think. - -## The Fundamental Problems with requirements.txt - -### Dependency Resolution Hell - -The most critical flaw in `requirements.txt` is its complete lack of intelligent dependency resolution. When you run `pip install -r requirements.txt`, pip installs packages in the order they appear, attempting to satisfy version constraints as it goes. But it has no global view of all requirements and cannot backtrack when conflicts arise. - -Consider this real-world scenario: - -```txt -# requirements.txt -django==4.2.0 -django-extensions==3.2.1 -celery==5.3.0 -kombu==5.2.4 -``` - -This looks reasonable, but there's a hidden time bomb. `django-extensions` 3.2.1 requires `Django>=3.2,<4.2`, while you've specified `Django==4.2.0`. The installation might succeed if Django is installed first, but you're running with an unsupported configuration that could break at any time. - -Poetry's dependency resolver would catch this immediately: - -```bash -$ poetry add django==4.2.0 django-extensions==3.2.1 - -Because django-extensions (3.2.1) depends on Django (>=3.2,<4.2) - and no versions of django-extensions match >3.2.1,<4.0.0, -django-extensions is forbidden. -So, because your-project depends on django-extensions (^3.2.1), -version solving failed. -``` - -This upfront conflict detection prevents the production surprises that plague `requirements.txt` users. - -### Reproducible Environment Issues - -`requirements.txt` only captures your direct dependencies, not the entire dependency tree. This creates a reproducibility nightmare that every Python developer has experienced. - -When you install `requests==2.31.0`, you're also installing: - -``` -requests==2.31.0 -├── certifi>=2017.4.17 -├── charset-normalizer>=2.0.0,<4 -├── idna>=2.5,<4 -└── urllib3>=1.21.1,<3 -``` - -Your `requirements.txt` doesn't capture these sub-dependency versions. Six months later, when `urllib3` releases version 2.1.0 with breaking changes, your production deployment might install this newer version while your development environment still runs the older one. - -The "works on my machine" syndrome isn't a developer joke—it's a systematic failure of dependency management that costs teams thousands of hours annually. - -### Development vs Production Separation - -Most Python projects struggle with cleanly separating development tools from production dependencies. You end up with solutions like: - -```txt -# requirements.txt -django==4.2.0 -psycopg2-binary==2.9.7 -gunicorn==21.2.0 - -# requirements-dev.txt --r requirements.txt -pytest==7.4.0 -black==23.7.0 -mypy==1.5.0 -``` - -This approach has several problems: - -1. **Security risk**: Development tools often have broader permissions and additional attack surface -2. **Performance impact**: Extra packages increase container size and startup time -3. **Maintenance burden**: Multiple files get out of sync easily -4. **Deployment complexity**: CI/CD pipelines need special handling for different requirement files - -### Manual Dependency Management Burden - -Managing dependencies with `requirements.txt` is entirely manual. When you want to add a new package, you need to: - -1. Research compatible versions -2. Manually add it to `requirements.txt` -3. Install and test -4. Update version constraints if conflicts arise -5. Remember to update related packages -6. Manually check for security vulnerabilities - -This process is error-prone and time-consuming. Teams often defer dependency updates, leading to technical debt and security vulnerabilities. The manual overhead grows exponentially with project complexity. - -## Modern Solutions: Poetry and uv - -The Python ecosystem has evolved significantly since `requirements.txt` was introduced. Two tools have emerged as clear leaders in modern dependency management: Poetry and uv. Both solve the fundamental problems we've discussed, but with different approaches and trade-offs. - -### Poetry - The Established Choice - -Poetry has been the gold standard for Python dependency management since 2018. It's a mature, battle-tested tool that combines dependency management, virtual environment handling, and package publishing into a cohesive workflow. - -Key advantages: -- **Mature ecosystem**: Extensive plugin ecosystem and community support -- **Integrated toolchain**: Handles everything from dependency resolution to PyPI publishing -- **Proven reliability**: Used by thousands of production projects -- **Rich configuration**: Extensive customization options for complex projects - -Poetry uses a SAT solver for dependency resolution, ensuring that all package requirements are satisfied before installation begins. This prevents the conflicts that plague `requirements.txt` users. - -### uv - The New Performance King - -uv represents the next generation of Python tooling. Developed by Astral (the team behind Ruff), it's written in Rust and focuses on speed and simplicity. While newer than Poetry, it's rapidly gaining adoption due to its exceptional performance. - -Key advantages: -- **Blazing performance**: 10-100x faster than pip for most operations -- **Universal toolchain**: Combines package management, Python installation, and virtual environments -- **Modern architecture**: Built from the ground up with current Python standards -- **Seamless compatibility**: Works with existing `requirements.txt` and `pyproject.toml` files - -### Performance Comparison - -Let's look at concrete benchmarks for a typical Django project with 50 dependencies: - -| Operation | pip | Poetry | uv | -|-----------|-----|---------|-----| -| Fresh install | 45s | 32s | 3s | -| Cached install | 12s | 8s | 0.5s | -| Lock file generation | N/A | 15s | 1s | -| Memory usage | 150MB | 200MB | 50MB | - -These performance improvements aren't just nice-to-have—they fundamentally change developer experience. Faster dependency resolution means shorter feedback loops, more frequent updates, and less time waiting for CI/CD pipelines. - -## Poetry Deep Dive - -### Smart Dependency Resolution - -Poetry's dependency resolver is built on a SAT (Boolean satisfiability) solver, similar to those used in advanced package managers like Conda and APT. This means Poetry can: - -1. **Analyze all constraints simultaneously**: Instead of installing packages sequentially, Poetry builds a complete dependency graph first -2. **Detect conflicts before installation**: No more failed deployments due to incompatible packages -3. **Find optimal solutions**: When multiple valid solutions exist, Poetry chooses the one that maximizes compatibility - -Here's how Poetry handles the Django conflict we discussed earlier: - -```bash -$ poetry init -$ poetry add django@^4.2.0 -$ poetry add django-extensions@^3.2.1 - -Creating virtualenv myproject in /Users/dev/myproject/.venv -Updating dependencies -Resolving dependencies... (0.5s) - -Because django-extensions (3.2.1) depends on Django (>=3.2,<4.2) - and no versions of django-extensions match >3.2.1,<4.0.0, -django-extensions is forbidden. - -Would you like to try django-extensions@^3.1.0 instead? [y/N] -``` - -Poetry not only detects the conflict but suggests a resolution. This proactive approach prevents the production surprises that plague `requirements.txt` users. - -### Lock File System - -Poetry's lock file (`poetry.lock`) is the secret to reproducible environments. Unlike `requirements.txt`, which only specifies direct dependencies, the lock file captures the exact version of every package in your dependency tree: - -```toml -# poetry.lock (excerpt) -[[package]] -name = "requests" -version = "2.31.0" -description = "Python HTTP for Humans." -category = "main" -optional = false -python-versions = ">=3.7" - -[package.dependencies] -certifi = ">=2017.4.17" -charset-normalizer = ">=2,<4" -idna = ">=2.5,<4" -urllib3 = ">=1.21.1,<3" - -[[package]] -name = "urllib3" -version = "1.26.16" -description = "HTTP library with thread-safe connection pooling" -# ... exact hash and metadata -``` - -This lock file ensures that every developer, CI system, and production server installs identical package versions. The lock file also includes cryptographic hashes for security verification. - -**Pro tip**: Always commit `poetry.lock` to version control, but never edit it manually. Poetry manages this file automatically. - -### Dependency Groups Management - -One of Poetry's most powerful features is dependency groups, which cleanly separate different types of dependencies: - -```toml -# pyproject.toml -[tool.poetry.dependencies] -python = "^3.11" -django = "^4.2.0" -psycopg2-binary = "^2.9.0" -celery = "^5.3.0" - -[tool.poetry.group.dev.dependencies] -pytest = "^7.4.0" -black = "^23.7.0" -mypy = "^1.5.0" -pytest-django = "^4.5.0" - -[tool.poetry.group.docs.dependencies] -mkdocs = "^1.5.0" -mkdocs-material = "^9.0.0" - -[tool.poetry.group.test.dependencies] -coverage = "^7.2.0" -pytest-cov = "^4.1.0" -``` - -You can install specific groups as needed: - -```bash -# Production: only main dependencies -poetry install --only=main - -# Development: main + dev dependencies -poetry install --with=dev - -# Documentation: main + docs dependencies -poetry install --with=docs - -# Everything for local development -poetry install -``` - -This approach eliminates the security and performance issues of installing development tools in production. - -### Virtual Environment Integration - -Poetry automatically creates and manages virtual environments, eliminating the manual `python -m venv` and `source activate` dance: - -```bash -# Poetry handles everything automatically -poetry install # Creates venv if needed, installs dependencies -poetry shell # Activates the virtual environment -poetry run python app.py # Runs commands in the venv without activation -poetry run pytest # Run tests in the isolated environment -``` - -The virtual environment is created in a predictable location (`{cache-dir}/virtualenvs/{project-name}-{hash}`) and automatically activated when needed. This eliminates common issues like accidentally installing packages globally or using the wrong Python version. - -### Publishing and Packaging - -Poetry integrates package building and publishing, making it easy to distribute your code: - -```bash -# Build distribution packages -poetry build - -# Publish to PyPI -poetry publish - -# Publish to private repository -poetry publish -r private-repo -``` - -The `pyproject.toml` file contains all metadata needed for packaging: - -```toml -[tool.poetry] -name = "my-awesome-package" -version = "0.1.0" -description = "A fantastic Python package" -authors = ["Your Name "] -license = "MIT" -readme = "README.md" -repository = "https://github.com/yourusername/my-awesome-package" -keywords = ["awesome", "package"] - -[tool.poetry.scripts] -my-cli = "my_package.cli:main" -``` - -This integrated approach eliminates the need for separate `setup.py`, `setup.cfg`, and `MANIFEST.in` files. - -## uv - The Future of Python Package Management - -### Blazing Fast Performance - -uv's performance improvements aren't just incremental—they're transformational. Built in Rust with a focus on speed, uv leverages modern techniques like parallel downloads, efficient caching, and optimized dependency resolution. - -Real-world benchmark from a Django e-commerce project with 73 dependencies: - -```bash -# Fresh installation (no cache) -$ time pip install -r requirements.txt -real 1m23.451s -user 0m45.123s -sys 0m8.234s - -$ time poetry install -real 0m47.892s -user 0m32.156s -sys 0m6.789s - -$ time uv pip install -r requirements.txt -real 0m3.234s -user 0m1.456s -sys 0m0.789s -``` - -The speed improvement comes from several optimizations: - -1. **Parallel downloads**: uv downloads multiple packages simultaneously -2. **Efficient wheel caching**: Intelligent cache management reduces redundant work -3. **Optimized resolution**: Rust-based dependency resolver is orders of magnitude faster -4. **Minimal overhead**: Lower memory footprint and CPU usage - -This performance boost has real productivity impacts. Faster CI/CD pipelines mean quicker feedback loops. Reduced waiting time means developers stay in flow state longer. - -### Universal Python Toolchain - -uv aims to be a complete Python toolchain, replacing multiple tools with a single, fast binary: - -```bash -# Python version management (replaces pyenv) -uv python install 3.11 -uv python install 3.12 -uv python list - -# Virtual environment management (replaces venv) -uv venv myproject --python 3.11 -uv venv myproject --python 3.12.0 - -# Package management (replaces pip) -uv pip install requests -uv pip install -r requirements.txt -uv pip install -e . - -# Project management (competes with Poetry) -uv init myproject -uv add requests -uv add pytest --dev -uv run python main.py -``` - -This unified approach reduces context switching and simplifies tool management across different environments. - -### Modern Workflow Examples - -uv's workflow is designed to be intuitive for developers coming from any background: - -```bash -# Start a new project -uv init my-fastapi-app -cd my-fastapi-app - -# Add production dependencies -uv add fastapi uvicorn sqlalchemy psycopg2-binary - -# Add development dependencies -uv add pytest black ruff mypy --dev - -# Create and activate virtual environment -uv venv -source .venv/bin/activate # or `uv shell` in future versions - -# Run your application -uv run uvicorn main:app --reload - -# Run tests -uv run pytest - -# Sync dependencies (like poetry install) -uv sync -``` - -The generated `pyproject.toml` is clean and standards-compliant: - -```toml -[project] -name = "my-fastapi-app" -version = "0.1.0" -description = "Add your description here" -dependencies = [ - "fastapi>=0.104.1", - "uvicorn>=0.24.0", - "sqlalchemy>=2.0.23", - "psycopg2-binary>=2.9.9", -] - -[tool.uv] -dev-dependencies = [ - "pytest>=7.4.3", - "black>=23.11.0", - "ruff>=0.1.6", - "mypy>=1.7.1", -] - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" -``` - -### Migration and Compatibility - -uv excels at compatibility with existing projects. It can work with `requirements.txt`, `pyproject.toml`, and even `poetry.lock` files: - -```bash -# Migrate from requirements.txt -uv pip install -r requirements.txt -uv pip freeze > requirements-lock.txt - -# Work with existing Poetry projects -uv sync # Reads poetry.lock if available - -# Generate lock file from pyproject.toml -uv lock - -# Install from lock file -uv sync --locked -``` - -Docker integration is seamless: - -```dockerfile -# Dockerfile with uv -FROM python:3.11-slim - -# Install uv -COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv - -# Copy dependency files -COPY pyproject.toml uv.lock ./ - -# Install dependencies -RUN uv sync --frozen --no-cache - -# Copy application code -COPY . . - -# Run application -CMD ["uv", "run", "python", "main.py"] -``` - -### Future-Proof Architecture - -uv is built on modern Python standards and actively developed: - -- **PEP 621 compliance**: Native `pyproject.toml` support -- **PEP 631 support**: Dependency groups specification -- **Active development**: Regular releases with new features -- **Strong backing**: Supported by Astral, a well-funded company -- **Growing adoption**: Increasing use in major open-source projects - -The tool is designed to evolve with Python's ecosystem while maintaining backward compatibility. - -## Migration Guide & Best Practices - -### Step-by-Step Migration Process - -#### From requirements.txt to Poetry - -1. **Install Poetry**: -```bash -curl -sSL https://install.python-poetry.org | python3 - -``` - -2. **Initialize Poetry in existing project**: -```bash -cd your-existing-project -poetry init -``` - -Poetry will ask about your project details and scan existing `requirements.txt` files. - -3. **Add dependencies**: -```bash -# Add from requirements.txt -poetry add $(cat requirements.txt | grep -v "^#" | tr '\n' ' ') - -# Or add individually for better control -poetry add django fastapi celery -poetry add --group dev pytest black mypy -``` - -4. **Generate lock file**: -```bash -poetry install -``` - -5. **Update your workflow**: -```bash -# Replace pip commands -poetry add package-name # instead of pip install -poetry remove package-name # instead of pip uninstall -poetry show # instead of pip list -poetry run python script.py # instead of python script.py -``` - -#### From requirements.txt to uv - -1. **Install uv**: -```bash -curl -LsSf https://astral.sh/uv/install.sh | sh -``` - -2. **Initialize project**: -```bash -cd your-existing-project -uv init --name your-project-name -``` - -3. **Migrate dependencies**: -```bash -# Install from existing requirements.txt -uv add $(cat requirements.txt | grep -v "^#" | grep -v "^-" | tr '\n' ' ') - -# Add dev dependencies separately -uv add pytest black ruff --dev -``` - -4. **Create lock file**: -```bash -uv lock -``` - -### Team Adoption Strategy - -**Phase 1: Pilot Project (1-2 weeks)** -- Choose a small, non-critical project for initial migration -- Have 1-2 team members learn the new tool thoroughly -- Document any issues and solutions -- Measure performance improvements - -**Phase 2: Team Training (1 week)** -- Conduct hands-on workshops with the pilot team as instructors -- Create internal documentation and cheat sheets -- Set up shared development environments -- Practice common workflows - -**Phase 3: Gradual Rollout (4-6 weeks)** -- Migrate projects in order of complexity (simple to complex) -- Update CI/CD pipelines incrementally -- Maintain fallback procedures for critical projects -- Collect feedback and iterate on processes - -**Phase 4: Full Adoption (2-4 weeks)** -- Migrate remaining projects -- Update team standards and documentation -- Remove old tooling and processes -- Celebrate the improved developer experience! - -### Production Best Practices - -**Version Pinning Strategy**: -```toml -# pyproject.toml - Use caret constraints for flexibility -[tool.poetry.dependencies] -django = "^4.2.0" # Allows 4.2.0 to <5.0.0 -requests = "^2.31.0" # Allows 2.31.0 to <3.0.0 - -# For critical dependencies, pin exactly -psycopg2-binary = "2.9.7" # Exact version -``` - -**Lock File Management**: -- Always commit lock files (`poetry.lock`, `uv.lock`) to version control -- Regularly update dependencies: `poetry update` or `uv sync --upgrade` -- Use dependabot or similar tools for automated updates -- Test updates in staging before production - -**Docker Optimization**: -```dockerfile -# Multi-stage build with Poetry -FROM python:3.11-slim as builder - -RUN pip install poetry -COPY pyproject.toml poetry.lock ./ -RUN poetry config virtualenvs.create false \ - && poetry install --only=main --no-dev - -FROM python:3.11-slim as runtime -COPY --from=builder /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages -COPY . . -CMD ["python", "main.py"] -``` - -**Monitoring and Alerting**: -- Monitor dependency update frequency -- Set up security scanning for vulnerabilities -- Alert on failed dependency installations -- Track build times and performance metrics - -## Conclusion & Next Steps - -The Python ecosystem has evolved far beyond the simple scripts that `requirements.txt` was designed to handle. Modern applications require sophisticated dependency management that can handle complex version constraints, ensure reproducible environments, and provide fast, reliable installations. - -**Key Benefits Recap**: -- **Reliability**: Lock files ensure identical environments across all deployments -- **Security**: Cryptographic verification and vulnerability scanning -- **Performance**: Dramatically faster installation and resolution times -- **Developer Experience**: Simplified workflows and better tooling integration -- **Maintainability**: Automated dependency management and conflict resolution - -**My Recommendations**: -- **For new projects**: Start with **uv**. Its performance and modern architecture make it the best choice for greenfield development -- **For existing projects**: Migrate to **Poetry** first if you need maximum stability and ecosystem support, then consider uv once your team is comfortable with modern dependency management -- **For teams**: Begin with a pilot project and gradually roll out to build confidence and expertise - -**Learning Resources**: -- [Poetry Documentation](https://python-poetry.org/docs/) -- [uv Documentation](https://docs.astral.sh/uv/) -- [PEP 621 - Storing project metadata in pyproject.toml](https://peps.python.org/pep-0621/) -- [Python Packaging User Guide](https://packaging.python.org/) - -**Take Action Today**: -1. **Experiment**: Create a small test project with Poetry or uv -2. **Measure**: Benchmark installation times on your current projects -3. **Plan**: Identify a pilot project for migration -4. **Share**: Discuss these tools with your team and start building consensus - -The transition away from `requirements.txt` isn't just a technical upgrade—it's an investment in your team's productivity, your application's reliability, and your project's long-term maintainability. The tools exist, the ecosystem is ready, and the benefits are immediate. - -Stop fighting with dependency conflicts at 3 AM. Your future self will thank you. - ---- - -*Have you migrated to modern Python dependency management? Share your experiences and tips in the comments below. Let's help the entire Python community move forward together.* diff --git a/blog/2025-09-07-inverted-hyde.md b/blog/2025-09-07-inverted-hyde.md deleted file mode 100644 index eea8161..0000000 --- a/blog/2025-09-07-inverted-hyde.md +++ /dev/null @@ -1,215 +0,0 @@ ---- -title: "Inverted HyDE: Solving Real-World Dense Retrieval Challenges" -authors: hieunv -tags: [RAG, dense retrieval, search, LLM, information retrieval] -description: An innovative approach to dense retrieval that addresses practical limitations of HyDE by flipping the script - generating hypothetical queries offline instead of hypothetical documents in real-time. -image: /img/blog/inverted-hyde.jpeg -comments: true # for Disqus ---- - -Dense retrieval systems have revolutionized how we search through large document collections, but the gap between theoretical breakthroughs and production reality often reveals unexpected challenges. While HyDE (Hypothetical Document Embeddings) showed impressive results in research settings, its real-world deployment faces critical bottlenecks that limit its practical adoption. Enter Inverted HyDE - a clever twist that maintains the core benefits while addressing the fundamental production constraints. - - - -## Introduction - -Dense retrieval has become the backbone of modern search systems, from enterprise knowledge bases to customer support platforms. Unlike traditional keyword-based search, dense retrieval uses neural embeddings to capture semantic similarity, enabling more nuanced understanding of user queries and document content. - -The original HyDE approach, introduced by Gao et al., proposed an elegant solution to a fundamental mismatch in retrieval systems: queries and documents often exist in different linguistic spaces. A user might ask "How do I reset my password?" while the relevant document contains procedural text like "Navigate to Settings, select Account Security, then click Reset Credentials." HyDE bridges this gap by generating a hypothetical document that answers the query, then using this synthetic document for retrieval instead of the original query. - -While theoretically sound and empirically promising, HyDE's real-world implementation reveals critical limitations that make it challenging to deploy in production environments where latency, reliability, and cost matter as much as accuracy. - -## The Reality Check: HyDE's Industrial Challenges - -Despite its theoretical elegance, HyDE faces two major obstacles in production deployments that significantly impact its viability: - -### Latency Issues: The Real-Time Generation Bottleneck - -Every user query in HyDE requires real-time LLM generation to create the hypothetical document. This introduces several latency concerns: - -- **Additional network round-trips**: Each query now requires a call to an LLM service before the actual retrieval can begin -- **Generation time overhead**: LLM requests typically take 1-5 seconds, dramatically increasing query response times -- **Queue congestion**: During peak usage, LLM API rate limits can create cascading delays -- **Timeout risks**: LLM generation failures require fallback mechanisms, complicating the retrieval pipeline - -In user-facing applications where sub-second response times are expected, this additional latency can significantly degrade user experience. Search systems now require 1-5 more seconds just for the preprocessing step, representing a much more significant increase in response time. - -### Reliability Problems: The Domain-Specific Query Challenge - -LLMs, despite their impressive capabilities, struggle with domain-specific or highly technical queries. This creates reliability issues in specialized environments: - -- **Domain knowledge gaps**: LLMs may lack sufficient training data for niche industries, leading to poor hypothetical document generation -- **Complex query failures**: Multi-part questions or queries with specific constraints often result in generic or irrelevant hypothetical documents -- **"Sorry, I don't know" responses**: When LLMs cannot generate meaningful content, they may return empty responses or disclaimers, breaking the retrieval pipeline -- **Inconsistent quality**: The same query might generate different quality hypothetical documents depending on model temperature and prompt variations - -These reliability issues are particularly problematic in enterprise environments where users expect consistent performance across diverse query types and specialized domains. - -## Inverted HyDE: Flipping the Script - -Inverted HyDE addresses these production challenges through a fundamental shift in approach: instead of generating hypothetical documents from queries at query time, we generate hypothetical queries from documents during indexing time. - -### The Core Concept - -The inverted approach works as follows: - -1. **Offline Processing**: For each document in your corpus, use an LLM to generate multiple hypothetical queries that the document could answer -2. **Query Enrichment**: Store these generated queries alongside or instead of the original document content -3. **Runtime Matching**: When a user submits a query, match it against the pre-generated query space rather than the original document space -4. **Retrieval**: Return the documents associated with the most similar hypothetical queries - -This approach transforms the matching problem from "query-to-document" similarity to "query-to-query" similarity, which often produces more accurate results due to better linguistic alignment. - -### Example in Practice - -Consider a technical documentation page about API authentication: - -**Original Document**: "The authentication endpoint accepts POST requests with client_id and client_secret parameters. Upon successful validation, it returns a JSON response containing an access_token with a 3600-second expiration..." - -**Generated Hypothetical Queries**: -- "How do I authenticate with the API?" -- "What parameters does the auth endpoint need?" -- "How long do access tokens last?" -- "What format does the authentication response use?" - -When a user searches for "API authentication process," the system matches against these pre-generated queries rather than the technical documentation text, leading to more accurate retrieval. - -## Why This Works Better - -Inverted HyDE offers several key advantages over the original approach: - -### Keep the original speed characteristics - -The most immediate benefit is the complete elimination of query-time LLM generation. All hypothetical content is generated offline during document processing, meaning: - -- **Query response times**: Retrieval returns to its original speed characteristics -- **No LLM API dependencies**: The runtime system operates independently of external LLM services -- **Predictable performance**: Query latency becomes deterministic and independent of LLM service availability - -### Improved Reliability - -By moving generation offline, we gain several reliability advantages: - -- **Quality control opportunities**: Generated queries can be reviewed, filtered, and improved before indexing -- **Consistent performance**: The same document always has the same set of hypothetical queries -- **Graceful degradation**: Even if query generation fails for some documents, the system continues to function -- **Domain specialization**: More time and computational resources can be invested in generating high-quality domain-specific queries - -### Domain Agnostic Performance - -The offline approach allows for domain-specific optimizations: - -- **Specialized prompts**: Different document types can use tailored query generation prompts -- **Expert review**: Domain experts can validate and improve generated queries before deployment -- **Iterative improvement**: Query generation can be refined based on user feedback and search analytics - -### Linguistic Alignment Benefits - -Query-to-query matching provides superior semantic alignment: - -- **Natural language consistency**: Both user queries and generated queries are in natural question format -- **Intent preservation**: Generated queries capture the intent and information need rather than just keywords -- **Contextual nuance**: Questions naturally encode the context and specificity of information needs - -## Implementation Considerations - -Successfully deploying Inverted HyDE requires careful attention to several practical aspects: - -### Storage and Indexing Implications - -The inverted approach changes storage requirements: - -- **Increased storage needs**: Each document now stores multiple generated queries (typically 3-10 per document) -- **Index structure modifications**: Vector databases need to accommodate query-document associations -- **Metadata management**: Systems must track which queries belong to which documents and maintain these relationships - -**Storage Strategy Options**: -``` -Option 1: Separate query index -- Store generated queries in a dedicated index -- Maintain document ID mappings -- Allows independent optimization of query and document storage - -Option 2: Enriched document storage -- Append generated queries to document metadata -- Single index with enriched content -- Simpler architecture but larger storage footprint -``` - -### Query Generation Strategies - -Effective implementation requires thoughtful query generation: - -**Diversity Strategies**: -- Generate questions at different specificity levels (broad vs. narrow) -- Create queries for different user personas (beginner vs. expert) -- Include both direct questions and contextual queries - -**Quality Control Mechanisms**: -- Implement automated filtering for generic or low-quality queries -- Use similarity thresholds to avoid near-duplicate generated queries -- Establish human review processes for critical document collections - -### Update Cycles and Content Management - -Document changes require coordinated updates: - -- **Incremental updates**: When documents change, regenerate only affected queries -- **Batch processing**: Optimize LLM usage through batch query generation -- **Version control**: Maintain query generation history for rollback capabilities - -### Performance Optimization - -Several techniques can optimize the inverted approach: - -- **Query clustering**: Group similar generated queries to reduce index size -- **Selective generation**: Focus query generation on high-value documents -- **Hybrid approaches**: Combine generated queries with original document content for comprehensive coverage - -## Potential Extensions and Variations - -The inverted HyDE approach opens several avenues for advanced implementations: - -### Hybrid Real-Time and Offline Generation - -Combine the benefits of both approaches: - -- **Primary offline generation**: Use inverted HyDE as the primary retrieval mechanism -- **Fallback real-time generation**: For queries that don't match well against generated queries, fall back to traditional HyDE -- **Adaptive thresholds**: Dynamically decide between offline and real-time generation based on query complexity - -### Multi-Perspective Query Generation - -Generate queries from different viewpoints: - -- **Role-based queries**: Generate questions that different user roles might ask about the same content -- **Temporal queries**: Create queries for different time contexts (implementation vs. troubleshooting vs. maintenance) -- **Complexity tiers**: Generate queries at different technical complexity levels - -### Domain-Specific vs. General Query Handling - -Implement specialized processing pipelines: - -- **Domain detection**: Classify documents by domain and apply specialized query generation -- **Industry-specific prompts**: Use tailored prompts for medical, legal, technical, or other specialized content -- **Expert validation workflows**: Implement domain expert review for critical document collections - -### Continuous Learning and Improvement - -Build feedback loops for ongoing optimization: - -- **Query analytics**: Monitor which generated queries lead to successful retrievals -- **User feedback integration**: Use click-through rates and user satisfaction to improve query generation -- **A/B testing frameworks**: Systematically test different query generation strategies - -## Conclusion - -Inverted HyDE represents a pragmatic evolution of the original HyDE concept, addressing real-world production constraints while maintaining its core benefits. By shifting the computational burden from query time to indexing time, this approach eliminates latency bottlenecks and reliability issues that plague real-time generation systems. - -The key insight is recognizing that production search systems have different constraints than research environments. While the original HyDE optimized for retrieval accuracy, Inverted HyDE optimizes for the complete production equation: accuracy, latency, reliability, and operational complexity. - -This approach has broader implications for how we think about retrieval system design. Rather than always seeking real-time optimization, we can often achieve better overall performance by intelligently preprocessing and enriching our data offline. The principle of "doing expensive work once, offline" versus "doing cheap work many times, online" applies broadly across search and retrieval systems. - -As dense retrieval continues to mature, approaches like Inverted HyDE demonstrate the importance of bridging the gap between research innovations and production realities. The most impactful advances often come not from entirely new algorithms, but from clever reimaginations of existing techniques that better align with real-world constraints. - -For teams implementing dense retrieval in production environments, Inverted HyDE offers a compelling path forward - one that maintains the semantic benefits of hypothesis-based retrieval while respecting the operational requirements that ultimately determine system success. diff --git a/blog/authors.yml b/blog/authors.yml deleted file mode 100644 index d497635..0000000 --- a/blog/authors.yml +++ /dev/null @@ -1,11 +0,0 @@ -hieunv: - name: Hieu Nguyen - title: AI Engineer - url: https://behitek.com - image_url: https://avatars.githubusercontent.com/u/12376486 - page: true - socials: - github: behitek - linkedin: behitek - x: behitek_ - \ No newline at end of file diff --git a/docs/algorithms/bfs.md b/docs/algorithms/bfs.md deleted file mode 100644 index cd69486..0000000 --- a/docs/algorithms/bfs.md +++ /dev/null @@ -1,265 +0,0 @@ ---- -title: Breadth-First Search ---- -## 1. Introduction - -### What is BFS? - -Breadth-First Search (BFS) is one of the fundamental algorithms used for traversing or searching through graphs and trees. It explores nodes layer by layer, moving horizontally across the breadth of the data structure before descending to the next level. The BFS algorithm uses a queue to keep track of the nodes that are yet to be explored, ensuring that it processes nodes at the same level before moving to the next level. - -In essence, BFS works like a wave radiating outward from the source node, exploring each neighboring node before moving on to their neighbors. - -### Where is BFS used? - -BFS is widely used in a variety of applications: -- **Shortest Path Calculation**: BFS is optimal for finding the shortest path in unweighted graphs, as it explores all nodes layer by layer. -- **Social Networks**: It can be used to discover the degrees of separation between users. -- **Web Crawling**: BFS can be used to crawl websites, starting from a given web page and exploring its links. -- **Maze Solving**: BFS can find the shortest path to the exit in mazes and puzzles. - -### BFS vs. [Depth-First Search (DFS)](/docs/algorithms/dfs) - -BFS and DFS are both algorithms for traversing or searching graphs and trees, but they operate differently: -- **BFS** explores all nodes at the present depth level before moving on to nodes at the next depth level. -- **DFS** dives deep into one branch of the graph, exploring as far as possible before backtracking and exploring the next branch. - -![BFS vs. DFS traversal comparison](/img/docs/bfs-vs-dfs.png) -
BFS vs. DFS traversal comparison, source: [Medium](https://medium.com/basecs/breaking-down-breadth-first-search-cebe696709d9)
- - -## 2. Key Concepts of BFS - -### Graphs and Trees in BFS - -BFS is a versatile algorithm applicable to both **graphs** and **trees**: -- A **graph** is a collection of nodes (or vertices) connected by edges, which may be directed or undirected. -- A **tree** is a special type of graph without cycles, typically representing hierarchical data structures. - -BFS can be applied to both directed and undirected graphs. In the case of trees, BFS is used to explore nodes level by level, making it ideal for finding the shortest path or traversing all nodes in a structured way. - -### Nodes, Edges, and Levels - -In BFS: -- **Nodes (or vertices)** represent entities (like people, web pages, or puzzle states). -- **Edges** are the connections between nodes. -- **Levels** refer to the distance from the source node. BFS explores each level in its entirety before moving on to the next. - -### Queue Data Structure in BFS - -BFS relies heavily on the **queue** data structure. A queue operates on a **First-In, First-Out (FIFO)** principle, which aligns perfectly with the layer-by-layer traversal of BFS. The nodes are enqueued as they are discovered and dequeued when they are processed. - -[IMAGE description="Illustration of how a queue is used in BFS to manage node exploration, showing enqueuing and dequeuing operations."] - -![BFS using queue](/img/docs/bfs-and-queue.png) -
- How a queue is used in BFS to manage BFS node exploration, source: [Edureka](https://www.edureka.co/blog/breadth-first-search-algorithm/) -
- -:::tip - -Read more about [Queue Data Structure](/docs/data-structures/queue), note that this post in Vietnamese language. - -::: - - -## 3. How BFS Works: Step-by-Step Explanation - -### Exploring Neighboring Nodes - -BFS begins at the source node and explores all its neighboring nodes. These nodes are added to the queue for further exploration. - -1. Start at the source node. -2. Explore all directly connected nodes (neighbors). -3. Mark them as visited to avoid reprocessing. - -### Layer-by-Layer Traversal - -Once all nodes at the current level are explored, BFS proceeds to the next layer: -1. Dequeue a node from the queue. -2. Explore its unvisited neighbors, adding them to the queue. -3. Repeat until the queue is empty. - -### Termination Condition - -BFS terminates when there are no more nodes left to explore (i.e., when the queue is empty). It also stops early if the target node is found (in cases such as pathfinding). - - -## 4. BFS Algorithm - -### Pseudocode for BFS - -Below is the pseudocode for BFS: - -```cpp -BFS(graph, start_node): - Initialize an empty queue Q - Initialize an empty set visited - Enqueue start_node into Q - Add start_node to visited - - while Q is not empty: - current_node = Dequeue Q - for each neighbor of current_node: - if neighbor is not in visited: - Enqueue neighbor into Q - Add neighbor to visited -``` - -### Time and Space Complexity - -- **Time Complexity**: `O(V + E)`, where V is the number of vertices and E is the number of edges. Each vertex and edge is processed once. -- **Space Complexity**: `O(V)` for the visited set and the queue, which at worst may hold all the nodes. - - -## 5. Implementing BFS in C++ - -### BFS for Trees - -Below is an implementation of BFS for a tree structure in C++: - -```cpp -#include -#include -#include - -using namespace std; - -struct Node { - int data; - Node* left; - Node* right; - - Node(int val) : data(val), left(nullptr), right(nullptr) {} -}; - -void BFS(Node* root) { - if (!root) return; - - queue q; - q.push(root); - - while (!q.empty()) { - Node* current = q.front(); - q.pop(); - - cout << current->data << " "; - - if (current->left) q.push(current->left); - if (current->right) q.push(current->right); - } -} - -int main() { - Node* root = new Node(1); - root->left = new Node(2); - root->right = new Node(3); - root->left->left = new Node(4); - root->left->right = new Node(5); - - cout << "BFS Traversal: "; - BFS(root); - - return 0; -} -``` - -Output: -``` -BFS Traversal: 1 2 3 4 5 -``` - -### BFS for Graphs - -Here is an implementation of BFS for a graph in C++: - -```cpp -#include -#include -#include - -using namespace std; - -void BFS(int start, const vector>& graph, vector& visited) { - queue q; - q.push(start); - visited[start] = true; - - while (!q.empty()) { - int current = q.front(); - q.pop(); - - cout << current << " "; - - for (int neighbor : graph[current]) { - if (!visited[neighbor]) { - q.push(neighbor); - visited[neighbor] = true; - } - } - } -} - -int main() { - int nodes = 6; - vector> graph(nodes); - - // Add edges - graph[0] = {1, 2}; - graph[1] = {0, 3, 4}; - graph[2] = {0, 4}; - graph[3] = {1, 5}; - graph[4] = {1, 2, 5}; - graph[5] = {3, 4}; - - vector visited(nodes, false); - - cout << "BFS Traversal starting from node 0: "; - BFS(0, graph, visited); - - return 0; -} -``` - -Output: -``` -BFS Traversal starting from node 0: 0 1 2 3 4 5 -``` - - -## 6. Applications of BFS - -### Shortest Path in Unweighted Graphs - -BFS is often used to find the shortest path between two nodes in an unweighted graph. By exploring nodes level by level, BFS ensures that the first time a target node is reached, it is via the shortest path. - -### Solving Puzzles (e.g., Maze Problems) - -BFS can be applied to grid-based puzzles like mazes, where each cell in the maze is considered a node, and edges represent possible movements between cells. BFS can then explore the maze and find the shortest path to the exit. - -:::tip - -There is a cool video on [YouTube](https://www.youtube.com/watch?v=rop0W4QDOUI) that demonstrates how BFS can be used to solve a maze. - -::: - -### Web Crawling and Social Networks - -In web crawling, BFS explores web pages by visiting each link in a breadth-first manner. This approach ensures that all immediate links are explored before moving deeper. BFS is also used in social networks to discover connections between users, measuring degrees of separation between individuals. - - -## 7. Conclusion - -In this tutorial, we've covered the basics of BFS, including its concepts, working mechanism, and implementation in C++. We also explored various applications, challenges, and optimizations of BFS. Mastering BFS is crucial for understanding graph traversal algorithms and solving problems in areas like pathfinding, network analysis, and puzzles. - -To strengthen your understanding of BFS, try solving these problems: - -1. **Beginner** - - Find the shortest path in an unweighted graph. -2. **Intermediate** - - Solve a maze using BFS. -3. **Advanced** - - Implement bidirectional BFS for large graphs. - -We have a competitive programming platform, [LCOJ](https://luyencode.net), where you can practice these problems and more. - -Happy coding! \ No newline at end of file diff --git a/docs/algorithms/dfs.md b/docs/algorithms/dfs.md deleted file mode 100644 index 8fae183..0000000 --- a/docs/algorithms/dfs.md +++ /dev/null @@ -1,3 +0,0 @@ ---- -title: Depth-First Search ---- \ No newline at end of file diff --git a/docs/data-structures/hash-table.md b/docs/data-structures/hash-table.md deleted file mode 100644 index d12c99d..0000000 --- a/docs/data-structures/hash-table.md +++ /dev/null @@ -1,328 +0,0 @@ ---- -title: Hash Table (Bảng băm) -tags: [data structures, hash table, vietnamese] -description: Bảng băm là một cấu trúc dữ liệu dùng để lưu trữ các cặp key-value. Các key được băm thành các chỉ số trong bảng băm. -image: /img/docs/hash-table.jpeg ---- - -Trong khoa học máy tính, **Bảng băm** (Tiếng anh: Hash table) là một cấu trúc dữ liệu sử dụng hàm băm để ánh xạ từ giá trị xác định, được gọi là khóa (ví dụ như tên của một người), đến giá trị tương ứng (ví dụ như số điện thoại của họ). Do đó, bảng băm là một mảng kết hợp. Hàm băm được sử dụng để chuyển đổi từ khóa thành chỉ số (giá trị băm) trong mảng lưu trữ các giá trị tìm kiếm. - -## Bảng băm là gì? - -Khi nói đến "Bảng băm", hay hash table, chúng ta đang nói đến tên một cấu trúc dữ liệu. - -"Bảng băm" sử dụng 1 "hàm băm", hay hash function để mã hóa các dữ liệu đầu vào sang miền giá trị mới. Hàm băm không phải phải lúc nào cũng giải mã được. - -Thuật ngữ "băm / hash" là một kỹ thuật được sử dụng để định danh một đối tượng cụ thể trong một nhóm các đối tượng tương tự. Một số ví dụ về việc sử dụng bảng băm trên thực tế: -- Trong trường đại học, mỗi sinh viên được chỉ định một mã sinh viên không giống nhau và qua mã sinh viên đó có thể truy xuất các thông tin của sinh viên đó. -- Trong thư viện, mỗi một cuốn sách một mã số riêng và mã số đó có thể được dùng để xác định các thông tin của sách, chẳng hạn như vị trí chính xác của sách trong thư viện hay thể loại của sách đó,… - -![Thuật toán mã hóa mật khẩu là một hàm băm / hash function](/img/docs/image-1709647922747.png) - -Trong cả 2 ví dụ trên, các sinh viên và các cuốn sách được “băm” thành các mã số duy nhất(không trùng lặp). - -Giả sử rằng bạn có một đối tượng và bạn muốn gán cho nó một cái khóa (key) để giúp tìm kiếm dễ dàng hơn. Để lưu giữ cặp ``(nó thường được gọi là `` hơn), bạn có thể sử dụng mảng bình thường để làm việc này; Với chỉ số mảng là khóa và giá trị tại chỉ số đó là giá trị tương ứng của khóa. Tuy nhiên, trong trường hợp phạm vi của khóa lớn và không thể sử dụng chỉ số mảng được, khi đó bạn sẽ cần tới “băm”(hashing). - -Quan sát ví dụ dưới đây để hiểu cách hàm băm hoạt động trong thực tế. - -```cpp -// Cho chuỗi ký tự S bao gồm các chữ cái a-z, A-Z. Hay đếm số lần xuất hiện của mỗi ký tự trong chuỗi S và in ra màn hình theo thứ tự từ điển. - -#include -using namespace std; - -// Bởi vì các ký tự a-z, A-Z có mã ASCII từ 65-90 và 97-122 nên ta có thể sử dụng mã ASCII của ký tự để làm hàm băm - -int hashFunction(char c){ - return (int)c; // Trả về mã ASCII của ký tự -} - -int main(){ - string s; - cin >> s; - int hash[256] = {0}; - for(int i = 0; i < s.length(); i++){ - hash[hashFunction(s[i])]++; - } - for(int i = 0; i < 256; i++){ - if(hash[i] > 0){ - cout << (char)i << " " << hash[i] << endl; - } - } - return 0; -} -``` - -## Hàm băm là gì? - -Hàm băm là bất kỳ hàm nào có thể được sử dụng để ánh xạ tập dữ liệu có kích thước tùy ý thành tập dữ liệu có kích thước cố định và đưa vào bảng băm. Các giá trị được trả về bởi hàm băm được gọi là giá trị băm. - -Một hàm băm được đánh giá tốt nếu nó đạt được các yêu cầu cơ bản sau: - -- Dễ tính toán: Nó phải dễ tính toán và bản thân nó không phải là một thuật toán -- Phân bố đồng đều: Nó cần phải phân phối đồng đều trên bảng băm, không xảy ra việc tập trung thành các cụm -- Ít va chạm: Va chạm xảy ra khi các cặp phần tử được ánh xạ tới cùng một giá trị băm. - -> Chú ý: Bất kể hàm băm có tốt đên đâu, va chạm vẫn có thể xảy ra. Vì vậy, để duy trì hiệu suất của bảng băm, điều quan trọng là phải quản lý va chạm thông qua các kỹ thuật giải quyết va chạm. - -### Tại sao phải cần hàm băm đủ tốt? -Hãy đi vào một ví dụ để dễ hình dung nhé. Giả sử rằng bạn cần lưu giữ các string sau đây trong bảng băm: `{“abcdef”, “bcdefa”, “cdefab” , “defabc” }`. - -Để tính toán chỉ mục lưu giữ các string này, chúng ta dùng một hàm băm như sau: Chỉ mục của mỗi string sẽ được tính bằng tổng giá trị ASCII của các ký tự trong nó sau đó lấy dư với 5. - -Do 5 là số nguyên tố và nó lớn hơn số lượng phần tử, nó có khả năng lập ra các chỉ mục khác nhau(giảm va chạm). Số nguyên tố luôn là lựa chọn tốt nếu bạn muốn dùng phép lấy phần dư. Các giá trị ASCII của a, b, c, d, e và f lần lượt là 97, 98, 99, 100, 101 và 102. - -Dễ nhận thấy, các string kia chỉ là các hoán vị của cùng 1 chuỗi, do đó chỉ mục được tạo ra của chúng đều giống nhau và đều bằng 597 % 5 = 2. - -Lúc này, hàm băm sẽ tính toán và tất cả các string kia đều có cùng 1 chỉ mục. Khi đó, bạn có thể tạo 1 list tại chỉ số đó để lưu các string trong list đó. Như hình ảnh minh họa này: - -![Bảng băm hoạt động ra sao](/img/docs/image-1709649177256.png) - -Bạn thấy đó, bạn vẫn sẽ mất `O(n)` để tìm kiếm một string cụ thể. Điều đó có nghĩa hàm băm này chưa thực sự tốt. - -Hãy thử với 1 hàm băm khác nhé. Chỉ mục của các string sẽ được tính bằng tổng của các mã ASCII của từng ký tự theo sau là vị trí của ký tự đó trong string. Sau đó đem chia dư cho số nguyên tố 2069. - -``` -String Hash function Index -abcdef (971 + 982 + 993 + 1004 + 1015 + 1026)%2069 38 -bcdefa (981 + 992 + 1003 + 1014 + 1025 + 976)%2069 23 -cdefab (991 + 1002 + 1013 + 1024 + 975 + 986)%2069 14 -defabc (1001 + 1012 + 1023 + 974 + 985 + 996)%2069 11 -``` - -Lúc này, các giá trị sau khi mã hóa (hash) của mỗi chuỗi là không trùng lặp (không va chạm). - -![Bảng băm](/img/docs/image-1709649399104.png) - -## Kỹ thuật xử lý va chạm - -## Separate chaining (open hashing) - -Separate chaining là một kỹ thuật xử lý và chạm phổ biến nhất. Nó thường được cài đặt với danh sách liên kết. Để lưu giữ một phần tử trong bảng băm, bạn phải thêm nó vào một danh sách liên kết ứng với chỉ mục của nó. - -Nếu có sự va chạm xảy ra, các phần tử đó sẽ nằm cùng trong 1 danh sách liên kết (xem ảnh để hiểu hơn). - -![file](/img/docs/image-1709649753757.png) - -Như vậy, kỹ thuật này sẽ đạt được tốc độ tìm kiếm O(1) trong trường hợp tối ưu và O(N) nếu tất cả các phần tử ở cùng 1 danh sách liên kết duy nhất. Đó là do có điều kiện 3 trong tiêu chí hàm băm tốt. - -**Cài đặt:** - -Giả định: Hàm băm sẽ trả về số int trong [0, 19]. - -```cpp -vector hashTable[20]; -int hashTableSize=20; -``` -Thêm dữ liệu vào bảng băm - -```cpp -void insert(string s) -{ - // Compute the index using Hash Function - int index = hashFunc(s); - // Insert the element in the linked list at the particular index - hashTable[index].push_back(s); -} -``` - -Tìm kiếm - -```cpp -void search(string s) -{ - //Compute the index by using the hash function - int index = hashFunc(s); - //Search the linked list at that specific index - for(int i = 0;i < hashTable[index].size();i++) - { - if(hashTable[index][i] == s) - { - cout << s << " is found!" << endl; - return; - } - } - cout << s << " is not found!" << endl; -} -``` - -### Linear probing (open addressing or closed hashing) - -Trong kỹ thuật xử lý va chạm này, chúng ta sẽ không dùng danh sách liên kết để lưu trữ mà chỉ có bản thân array đó thôi. - -Khi thêm vào bảng băm, nếu chỉ mục đó đã có phần tử rồi; Giá trị chỉ mục sẽ được tính toán lại theo cơ chế tuần tự. Giả sử rằng chỉ mục là chỉ số của mảng, khi đó, việc tính toán chỉ mục cho phần tử được tính theo cách sau: -``` -index = index % hashTableSize -index = (index + 1) % hashTableSize -index = (index + 2) % hashTableSize -index = (index + 3) % hashTableSize -``` - -Và cứ thế theo cách như vậy chừng nào index thu được chưa có phần tử được sử dụng. Tất nhiên, không gian chỉ mục phải được đảm bảo để luôn có chỗ cho phần tử mới. - -Như trong ví dụ dưới đây, chuỗi Hashing bị trùng chỉ mục(2) ở lần đầu tính chỉ mục. Do đó, nó được đẩy lên vị trí trống ở phía sau(3). - -![Xử lý trùng lặp bảng băm](/img/docs/image-1709650320251.png) - -**Cài đặt** - -Giả sử rằng: -- Không có nhiều hơn 20 phần tử trong tập dữ liệu -- Hàm băm sẽ trả về một số nguyên từ 0 đến 19 - -```cpp -string hashTable[21]; -int hashTableSize = 21; - -// Thêm dữ liệu -void insert(string s) -{ - //Compute the index using the hash function - int index = hashFunc(s); - //Search for an unused slot and if the index will exceed the hashTableSize then roll back - while(hashTable[index] != "") - index = (index + 1) % hashTableSize; - hashTable[index] = s; -} - -// Tìm kiếm -void search(string s) -{ - //Compute the index using the hash function - int index = hashFunc(s); - //Search for an unused slot and if the index will exceed the hashTableSize then roll back - while(hashTable[index] != s and hashTable[index] != "") - index = (index + 1) % hashTableSize; - //Check if the element is present in the hash table - if(hashTable[index] == s) - cout << s << " is found!" << endl; - else - cout << s << " is not found!" << endl; -} -``` - -### Quadratic Probing - -Ý tưởng cũng khá giống Linear Probing, nhưng cách tính chỉ mục có khác đôi chút: -``` -index = index % hashTableSize -index = (index + 12) % hashTableSize -index = (index + 22) % hashTableSize -index = (index + 32) % hashTableSize -``` - -Và cứ thế cho tới khi tìm được chỉ mục trống. - -**Cài đặt bảng băm dùng Quadratic Probing** - -Giả sử rằng: -- Không có nhiều hơn 20 phần tử trong tập dữ liệu -- Hàm băm sẽ trả về một số nguyên từ 0 đến 19 - -```cpp -string hashTable[21]; -int hashTableSize = 21; - -// Thêm phần tử -void insert(string s) -{ - //Compute the index using the hash function - int index = hashFunc(s); - //Search for an unused slot and if the index will exceed the hashTableSize roll back - int h = 1; - while(hashTable[index] != "") - { - index = (index + h*h) % hashTableSize; - h++; - } - hashTable[index] = s; -} - -// Tìm kiếm -void search(string s) -{ - //Compute the index using the Hash Function - int index = hashFunc(s); - //Search for an unused slot and if the index will exceed the hashTableSize roll back - int h = 1; - while(hashTable[index] != s and hashTable[index] != "") - { - index = (index + h*h) % hashTableSize; - h++; - } - //Is the element present in the hash table - if(hashTable[index] == s) - cout << s << " is found!" << endl; - else - cout << s << " is not found!" << endl; -} -``` - -### Double hashing - -Vẫn giống 2 kỹ thuật ngay phía trên, chỉ khác ở công thức tính khi xảy ra va chạm như sau: -``` -index = (index + 1 * indexH) % hashTableSize; -index = (index + 2 * indexH) % hashTableSize; -``` - -Và cứ tiếp tục cho tới khi tìm được chỉ mục chưa được sử dụng. - -**Cài đặt bảng băm dùng Double hashing** - -Giả sử rằng: -- Không có nhiều hơn 20 phần tử trong tập dữ liệu -- Hàm băm sẽ trả về một số nguyên từ 0 đến 19 - -```cpp -string hashTable[21]; -int hashTableSize = 21; - -// Thêm phần tử -void insert(string s) -{ - //Compute the index using the hash function1 - int index = hashFunc1(s); - int indexH = hashFunc2(s); - //Search for an unused slot and if the index exceeds the hashTableSize roll back - while(hashTable[index] != "") - index = (index + indexH) % hashTableSize; - hashTable[index] = s; -} - -// Tìm kiếm -void search(string s) -{ - //Compute the index using the hash function - int index = hashFunc1(s); - int indexH = hashFunc2(s); - //Search for an unused slot and if the index exceeds the hashTableSize roll back - while(hashTable[index] != s and hashTable[index] != "") - index = (index + indexH) % hashTableSize; - //Is the element present in the hash table - if(hashTable[index] == s) - cout << s << " is found!" << endl; - else - cout << s << " is not found!" << endl; -} -``` - -## Ứng dụng của bảng băm - -Trong các bài toán thông thường, các bạn thường chỉ cần sử dụng cấu trúc dữ liệu được cài đặt sẵn ở các ngôn ngữ lập trình: map, set trong C/C++, Java; Dictionary trong C#, Python. Đó chính là các bảng băm cực kỳ hữu dụng mà chúng ta vẫn hay sử dụng. - -Nếu các bạn để ý thì `multimap` và `multiset` trong C++ cho phép lưu trữ key trùng nhau với giá trị khác nhau đó. Đó là 2 thanh niên đã được thiết lập cơ chế xử lý va chạm. - -Với các bài toán đặc thù, các bạn sẽ phải tự viết cho mình hàm băm và xây dựng cấu trúc dữ liệu bảng băm cho phù hợp. Dưới đây là một số ứng dụng của bảng băm: - -- Associative arrays: Bảng băm thường được sử dụng để cài đặt nhiều loại in-memory tables. Chúng được sử dụng để thực hiện các mảng kết hợp (các mảng có chỉ số là các chuỗi tùy ý hoặc các đối tượng phức tạp khác). -- Lập chỉ mục CSDL: Các bảng băm cũng có thể được sử dụng làm cấu trúc dữ liệu để lập chỉ mục dữ liệu trong các CSDL. -- Caches: Bảng băm dùng để thiết lập cơ chế caches, thường được dùng để tăng tốc độ truy xuất dữ liệu. -- Biểu diễn các đối tượng: Một số ngôn ngữ động, chẳng hạn như Perl, Python, JavaScript và Ruby sử dụng bảng băm để triển khai các đối tượng. -- Hàm băm được sử dụng trong các thuật toán khác nhau để tăng tốc độ tính toán. - -## Bài tập thực hành - -Danh sách bài tâp: https://luyencode.net/problems/?type=6 - -![Bài tập thực hành bảng băm](/img/docs/image-1709652201165.png) \ No newline at end of file diff --git a/docs/data-structures/linkedlist.md b/docs/data-structures/linkedlist.md deleted file mode 100644 index 5e6e2cf..0000000 --- a/docs/data-structures/linkedlist.md +++ /dev/null @@ -1,605 +0,0 @@ ---- -title: Singly Linked List (Danh sách liên kết đơn) -sidebar_label: Singly Linked List (DSLK) -tags: [data structures, linkedlist, vietnamese] -description: Danh sách liên kết đơn là một cấu trúc dữ liệu động, sử dụng con trỏ để quản lý các phần tử. Bài viết này sẽ giúp bạn hiểu rõ hơn về danh sách liên kết đơn và cách triển khai nó bằng ngôn ngữ lập trình C. -image: /img/docs/danh-sach-lien-ket-don.webp ---- - -Danh sách liên kết đơn (Singly Linked List) là một ví dụ đơn giản và hiệu quả về cấu trúc dữ liệu động, sử dụng con trỏ để quản lý các phần tử. Hiểu biết về con trỏ và cấp phát bộ nhớ động là rất quan trọng để nắm bắt cách thức hoạt động của danh sách liên kết. - -Bài viết này sẽ tập trung vào danh sách liên kết đơn để giúp bạn dễ dàng tiếp cận và hiểu rõ hơn. - -## Lý thuyết về danh sách liên kết - -Danh sách liên kết có chức năng tương tự như mảng, cho phép thêm và xóa các phần tử ở bất kỳ vị trí nào. Tuy nhiên, có một số điểm khác biệt chính giữa hai cấu trúc dữ liệu này: - -| Tính năng | Mảng | Danh sách liên kết | -|---|---|---| -| Kích thước | Cố định, cần khai báo trước | Thay đổi linh hoạt khi thêm/xóa phần tử | -| Cấp phát bộ nhớ | Tĩnh, cấp phát trong quá trình biên dịch | Động, cấp phát trong quá trình chạy | -| Thứ tự & sắp xếp | Lưu trữ liên tục trên các ô nhớ | Lưu trữ trên các ô nhớ ngẫu nhiên | -| Truy cập | Truy cập trực tiếp bằng chỉ số: O(1) | Duyệt từ đầu/cuối đến phần tử cần truy cập: O(n) | -| Tìm kiếm | Tuyến tính hoặc nhị phân | Chỉ có thể tìm kiếm tuyến tính | - -## Danh sách liên kết là gì? - -Danh sách liên kết đơn là một tập hợp các Node được phân bố động. Mỗi Node chứa hai thành phần: - -* **Data:** Lưu trữ giá trị của phần tử -* **Next:** Con trỏ trỏ đến Node tiếp theo trong danh sách - -Nếu con trỏ `next` trỏ tới `NULL`, nghĩa là đó là phần tử cuối cùng của danh sách. - -**Hình ảnh minh họa:** - -* **Cấu trúc của một Node:** - -![Cấu trúc của một Node](/img/docs/image-1712068257104.png) - -* **Ví dụ về danh sách liên kết đơn đầy đủ:** - -![Ví dụ về danh sách liên kết đơn đầy đủ](/img/docs/image-1712068326158.png) - -## Cài đặt danh sách liên kết đơn - -### Khai báo linked list - -```c -struct LinkedList { - int data; - struct LinkedList *next; -}; - -typedef struct LinkedList *node; // Dùng node thay cho struct LinkedList để code ngắn gọn hơn -``` - -Trong ví dụ này, `data` lưu trữ giá trị nguyên (int). Bạn có thể sử dụng các kiểu dữ liệu khác như `float`, `char`, hoặc thậm chí là struct do bạn tự định nghĩa. - -### Tạo mới một Node - -```c -node CreateNode(int value) { - node temp; // Khai báo một node tạm thời - temp = (node)malloc(sizeof(struct LinkedList)); // Cấp phát bộ nhớ cho node - temp->next = NULL; // Cho next trỏ tới NULL (ban đầu node chưa liên kết với node nào) - temp->data = value; // Gán giá trị cho node - return temp; // Trả về node mới đã được khởi tạo -} -``` - -### Thêm Node vào danh sách liên kết - -#### Thêm vào đầu - -```c -node AddHead(node head, int value) { - node temp = CreateNode(value); // Tạo node mới với giá trị value - if (head == NULL) { - head = temp; // Nếu danh sách rỗng, node mới trở thành head - } else { - temp->next = head; // Trỏ next của node mới tới head hiện tại - head = temp; // Cập nhật head là node mới - } - return head; // Trả về head mới -} -``` - -#### Thêm vào cuối - -```c -node AddTail(node head, int value) { - node temp = CreateNode(value); // Tạo node mới - if (head == NULL) { - head = temp; // Nếu danh sách rỗng, node mới trở thành head - } else { - node p = head; // Duyệt danh sách từ đầu - while (p->next != NULL) { - p = p->next; // Di chuyển đến node cuối cùng (next trỏ tới NULL) - } - p->next = temp; // Gán next của node cuối cùng trỏ tới node mới - } - return head; // Trả về head -} -``` - -#### Thêm vào vị trí bất kỳ - -```c -node AddAt(node head, int value, int position) { - if (position == 0 || head == NULL) { - head = AddHead(head, value); // Thêm vào đầu nếu vị trí là 0 hoặc danh sách rỗng - } else { - int k = 1; // Biến đếm vị trí - node p = head; - while (p != NULL && k != position) { - p = p->next; // Duyệt đến vị trí cần chèn - ++k; - } - if (k != position) { - head = AddTail(head, value); // Thêm vào cuối nếu vị trí vượt quá danh sách - } else { - node temp = CreateNode(value); // Tạo node mới - temp->next = p->next; // Liên kết node mới với node tiếp theo - p->next = temp; // Liên kết node hiện tại với node mới - } - } - return head; // Trả về head -} -``` - -**Lưu ý:** - -* Vị trí chèn được tính từ 0. -* Thứ tự các thao tác trong hàm `AddAt` rất quan trọng. Nếu bạn gán `p->next = temp` trước, bạn sẽ mất liên kết đến phần sau của danh sách. - -### Xóa Node khỏi danh sách liên kết - -#### Xóa đầu - -```c -node DelHead(node head) { - if (head == NULL) { - printf("\nDanh sách rỗng, không có gì để xóa!"); - } else { - head = head->next; // Cập nhật head là node tiếp theo - } - return head; // Trả về head mới -} -``` - -#### Xóa cuối - -```c -node DelTail(node head) { - if (head == NULL || head->next == NULL) { - return DelHead(head); // Xóa đầu nếu danh sách rỗng hoặc chỉ có 1 phần tử - } - node p = head; - while (p->next->next != NULL) { - p = p->next; // Duyệt đến node kế cuối (node có next trỏ tới node cuối cùng) - } - p->next = NULL; // Cho next của node kế cuối trỏ tới NULL - return head; // Trả về head -} -``` - -#### Xóa ở vị trí bất kỳ - -```c -node DelAt(node head, int position) { - if (position == 0 || head == NULL || head->next == NULL) { - head = DelHead(head); // Xóa đầu nếu vị trí là 0 hoặc danh sách rỗng/chỉ có 1 phần tử - } else { - int k = 1; // Biến đếm vị trí - node p = head; - while (p->next->next != NULL && k != position) { - p = p->next; // Duyệt đến node trước vị trí cần xóa - ++k; - } - if (k != position) { - head = DelTail(head); // Xóa cuối nếu vị trí vượt quá danh sách - } else { - p->next = p->next->next; // Bỏ qua node cần xóa - } - } - return head; // Trả về head -} -``` - -**Lưu ý:** - -* Vị trí xóa được tính từ 0. -* Hàm chỉ duyệt đến node gần cuối (cuối - 1) để đảm bảo liên kết sau khi xóa. - -### Lấy giá trị ở vị trí bất kỳ - -```c -int Get(node head, int index) { - int k = 0; // Biến đếm vị trí - node p = head; - while (p != NULL && k != index) { - p = p->next; // Duyệt đến vị trí cần lấy giá trị - ++k; - } - return p->data; // Trả về giá trị của node -} -``` - -**Lưu ý:** - -* Hàm trả về giá trị ở vị trí cuối cùng nếu `index` vượt quá chiều dài danh sách. -* Hàm mặc định `index` là số nguyên không âm. Bạn nên kiểm tra `index` trước khi gọi hàm. - -### Tìm kiếm trong danh sách liên kết - -```c -int Search(node head, int value) { - int position = 0; // Biến lưu vị trí - for (node p = head; p != NULL; p = p->next) { - if (p->data == value) { - return position; // Trả về vị trí tìm thấy - } - ++position; // Tăng vị trí nếu chưa tìm thấy - } - return -1; // Trả về -1 nếu không tìm thấy -} -``` - -### Duyệt danh sách liên kết - -```c -void Traverser(node head) { - printf("\n"); - for (node p = head; p != NULL; p = p->next) { - printf("%5d", p->data); // In giá trị của mỗi node - } -} -``` - -### Một số hàm bổ trợ khác - -#### Hàm khởi tạo Node head - -```c -node InitHead() { - node head = NULL; // Khởi tạo head trỏ tới NULL (danh sách rỗng) - return head; -} -``` - -#### Hàm lấy số phần tử của DSLK - -```c -int Length(node head) { - int length = 0; // Biến đếm số phần tử - for (node p = head; p != NULL; p = p->next) { - ++length; // Tăng biến đếm cho mỗi node - } - return length; // Trả về số phần tử -} -``` - -#### Hàm nhập danh sách liên kết - -```c -node Input() { - node head = InitHead(); // Khởi tạo danh sách rỗng - int n, value; - do { - printf("\nNhập số lượng phần tử n = "); - scanf("%d", &n); - } while (n <= 0); // Nhập số lượng phần tử cho đến khi hợp lệ - - for (int i = 0; i < n; ++i) { - printf("\nNhập giá trị cần thêm: "); - scanf("%d", &value); - head = AddTail(head, value); // Thêm phần tử vào cuối danh sách - } - return head; // Trả về head của danh sách đã nhập -} -``` - -## Code hoàn chỉnh của danh sách liên kết đơn -Bạn có thể tham khảo code đầy đủ của danh sách liên kết đơn tại đây. - -```c -#include -#include - -struct LinkedList{ - int data; - struct LinkedList *next; - }; - -typedef struct LinkedList *node; //Từ giờ dùng kiểu dữ liệu LinkedList có thể thay bằng node cho ngắn gọn - -node CreateNode(int value){ - node temp; // declare a node - temp = (node)malloc(sizeof(struct LinkedList)); // Cấp phát vùng nhớ dùng malloc() - temp->next = NULL;// Cho next trỏ tới NULL - temp->data = value; // Gán giá trị cho Node - return temp;//Trả về node mới đã có giá trị -} - -node AddTail(node head, int value){ - node temp,p;// Khai báo 2 node tạm temp và p - temp = CreateNode(value);//Gọi hàm createNode để khởi tạo node temp có next trỏ tới NULL và giá trị là value - if(head == NULL){ - head = temp; //Nếu linked list đang trống thì Node temp là head luôn - } - else{ - p = head;// Khởi tạo p trỏ tới head - while(p->next != NULL){ - p = p->next;//Duyệt danh sách liên kết đến cuối. Node cuối là node có next = NULL - } - p->next = temp;//Gán next của thằng cuối = temp. Khi đó temp sẽ là thằng cuối(temp->next = NULL mà) - } - return head; -} - - -node AddHead(node head, int value){ - node temp = CreateNode(value); // Khởi tạo node temp với data = value - if(head == NULL){ - head = temp; // //Nếu linked list đang trống thì Node temp là head luôn - }else{ - temp->next = head; // Trỏ next của temp = head hiện tại - head = temp; // Đổi head hiện tại = temp(Vì temp bây giờ là head mới mà) - } - return head; -} - -node AddAt(node head, int value, int position){ - if(position == 0 || head == NULL){ - head = AddHead(head, value); // Nếu vị trí chèn là 0, tức là thêm vào đầu - }else{ - // Bắt đầu tìm vị trí cần chèn. Ta sẽ dùng k để đếm cho vị trí - int k = 1; - node p = head; - while(p != NULL && k != position){ - p = p->next; - ++k; - } - - if(k != position){ - // Nếu duyệt hết danh sách lk rồi mà vẫn chưa đến vị trí cần chèn, ta sẽ mặc định chèn cuối - // Nếu bạn không muốn chèn, hãy thông báo vị trí chèn không hợp lệ - head = AddTail(head, value); - // printf("Vi tri chen vuot qua vi tri cuoi cung!\n"); - }else{ - node temp = CreateNode(value); - temp->next = p->next; - p->next = temp; - } - } - return head; -} - -node DelHead(node head){ - if(head == NULL){ - printf("\nCha co gi de xoa het!"); - }else{ - head = head->next; - } - return head; -} - -node DelTail(node head){ - if (head == NULL || head->next == NULL){ - return DelHead(head); - } - node p = head; - while(p->next->next != NULL){ - p = p->next; - } - p->next = p->next->next; // Cho next bằng NULL - // Hoặc viết p->next = NULL cũng được - return head; -} - -node DelAt(node head, int position){ - if(position == 0 || head == NULL || head->next == NULL){ - head = DelHead(head); // Nếu vị trí chèn là 0, tức là thêm vào đầu - }else{ - // Bắt đầu tìm vị trí cần chèn. Ta sẽ dùng k để đếm cho vị trí - int k = 1; - node p = head; - while(p->next->next != NULL && k != position){ - p = p->next; - ++k; - } - - if(k != position){ - // Nếu duyệt hết danh sách lk rồi mà vẫn chưa đến vị trí cần chèn, ta sẽ mặc định xóa cuối - // Nếu bạn không muốn xóa, hãy thông báo vị trí xóa không hợp lệ - head = DelTail(head); - // printf("Vi tri xoa vuot qua vi tri cuoi cung!\n"); - }else{ - p->next = p->next->next; - } - } - return head; -} - -int Get(node head, int index){ - int k = 0; - node p = head; - while(p != NULL && k != index){ - p = p->next; - } - return p->data; -} - -int Search(node head, int value){ - int position = 0; - for(node p = head; p != NULL; p = p->next){ - if(p->data == value){ - return position; - } - ++position; - } - return -1; -} - -node DelByVal(node head, int value){ - int position = Search(head, value); - while(position != -1){ - DelAt(head, position); - position = Search(head, value); - } - return head; -} - -void Traverser(node head){ - printf("\n"); - for(node p = head; p != NULL; p = p->next){ - printf("%5d", p->data); - } -} - -node InitHead(){ - node head; - head = NULL; - return head; -} - -int Length(node head){ - int length = 0; - for(node p = head; p != NULL; p = p->next){ - ++length; - } - return length; -} - -node Input(){ - node head = InitHead(); - int n, value; - do{ - printf("\nNhap so luong phan tu n = "); - scanf("%d", &n); - }while(n <= 0); - - for(int i = 0; i < n; ++i){ - printf("\nNhap gia tri can them: "); - scanf("%d", &value); - head = AddTail(head, value); - } - return head; -} - - - -int main(){ - printf("\n==Tao 1 danh sach lien ket=="); - node head = Input(); - Traverser(head); - - printf("\n==Thu them 1 phan tu vao linked list=="); - head = AddAt(head, 100, 2); - Traverser(head); - - printf("\n==Thu xoa 1 phan tu khoi linked list=="); - head = DelAt(head, 1); - Traverser(head); - - printf("\n==Thu tim kiem 1 phan tu trong linked list=="); - int index = Search(head, 5); - printf("\nTim thay tai chi so %d", index); - - printf("\nBan co the thu them nhe!"); - -} -``` - -Kết quả chạy: - -``` -==Tao 1 danh sach lien ket== -Nhap so luong phan tu n = 5 - -Nhap gia tri can them: 1 - -Nhap gia tri can them: 2 - -Nhap gia tri can them: 3 - -Nhap gia tri can them: 4 - -Nhap gia tri can them: 5 - - 1 2 3 4 5 -==Thu them 1 phan tu vao linked list== - 1 2 100 3 4 5 -==Thu xoa 1 phan tu khoi linked list== - 1 100 3 4 5 -==Thu tim kiem 1 phan tu trong linked list== -Tim thay tai chi so 4 -Ban co the thu them nhe! -Process returned 0 (0x0) execution time : 4.166 s -Press any key to continue. -``` - -**Lưu ý:** -- Code ví dụ sử dụng ngôn ngữ C. -- Bạn có thể bổ sung thêm các chức năng khác cho danh sách liên kết đơn tùy theo nhu cầu của bạn. - -## Câu hỏi thường gặp về Danh sách liên kết đơn - -**1. Danh sách liên kết đơn là gì?** - -- Danh sách liên kết đơn là một cấu trúc dữ liệu tuyến tính, trong đó mỗi phần tử (gọi là nút) chứa dữ liệu và một liên kết (con trỏ) trỏ đến phần tử tiếp theo trong danh sách. -- Nút đầu tiên được gọi là "đầu" của danh sách, và nút cuối cùng có liên kết trỏ đến NULL, đánh dấu kết thúc của danh sách. - -**2. Ưu điểm và nhược điểm của danh sách liên kết đơn so với mảng?** - -**Ưu điểm:** - -* **Kích thước động:** Danh sách liên kết đơn có thể phát triển hoặc thu nhỏ kích thước một cách linh hoạt trong thời gian chạy, không cần khai báo kích thước cố định như mảng. -* **Chèn và xóa hiệu quả:** Việc chèn hoặc xóa phần tử ở đầu hoặc giữa danh sách liên kết đơn có thể được thực hiện hiệu quả hơn so với mảng, vì không cần phải dịch chuyển các phần tử khác. - -**Nhược điểm:** - -* **Truy cập ngẫu nhiên kém:** Không thể truy cập trực tiếp vào phần tử bất kỳ trong danh sách liên kết đơn như mảng. Để truy cập một phần tử cụ thể, cần phải duyệt qua các phần tử từ đầu danh sách cho đến khi tìm thấy phần tử mong muốn. -* **Sử dụng nhiều bộ nhớ hơn:** Danh sách liên kết đơn cần thêm bộ nhớ để lưu trữ các liên kết (con trỏ) giữa các nút, trong khi mảng chỉ lưu trữ dữ liệu. - -**3. Các thao tác cơ bản trên danh sách liên kết đơn?** - -* **Tạo danh sách:** Khởi tạo một danh sách liên kết đơn rỗng. -* **Chèn phần tử:** Thêm một phần tử mới vào đầu, cuối hoặc vị trí bất kỳ trong danh sách. -* **Xóa phần tử:** Loại bỏ một phần tử khỏi danh sách dựa vào vị trí hoặc giá trị của nó. -* **Tìm kiếm phần tử:** Tìm kiếm một phần tử trong danh sách dựa vào giá trị của nó. -* **Duyệt danh sách:** Truy cập và xử lý từng phần tử trong danh sách theo thứ tự. - -**4. Ứng dụng của danh sách liên kết đơn?** - -* **Quản lý bộ nhớ động:** Danh sách liên kết đơn có thể được sử dụng để quản lý các khối bộ nhớ khả dụng, cho phép phân bổ và giải phóng bộ nhớ hiệu quả. -* **Triển khai ngăn xếp và hàng đợi:** Danh sách liên kết đơn có thể được sử dụng để triển khai các cấu trúc dữ liệu ngăn xếp (Stack) và hàng đợi (Queue). -* **Xử lý danh sách các đối tượng:** Danh sách liên kết đơn có thể được sử dụng để lưu trữ và quản lý danh sách các đối tượng trong các ứng dụng phần mềm. - -**5. Làm thế nào để đảo ngược một danh sách liên kết đơn?** - -Có nhiều cách để đảo ngược một danh sách liên kết đơn, bao gồm: - -* **Đệ quy:** Sử dụng đệ quy để duyệt qua danh sách và đảo ngược các liên kết giữa các nút. -* **Vòng lặp:** Sử dụng vòng lặp để duyệt qua danh sách và thay đổi các liên kết giữa các nút. -* **Sử dụng ba con trỏ:** Sử dụng ba con trỏ để theo dõi các nút trước đó, hiện tại và tiếp theo trong danh sách và thay đổi liên kết của chúng. - -**6. Danh sách liên kết đơn có thể được sử dụng để triển khai cấu trúc dữ liệu nào khác?** - -Danh sách liên kết đơn có thể được sử dụng để triển khai các cấu trúc dữ liệu khác như: - -* [**Ngăn xếp (Stack):**](/data-structures/stack/) Chèn và xóa phần tử ở đầu danh sách. -* [**Hàng đợi (Queue):**](/data-structures/queue/) Chèn phần tử ở cuối danh sách và xóa phần tử ở đầu danh sách. -* **Danh sách liên kết kép (Doubly Linked List):** Mỗi nút có thêm một liên kết trỏ đến nút trước đó. -* **Danh sách liên kết vòng (Circular Linked List):** Nút cuối cùng trỏ đến nút đầu tiên, tạo thành một vòng tròn. - -**7. Làm cách nào để phát hiện chu kỳ trong danh sách liên kết đơn?** - -Có thể sử dụng thuật toán Floyd's Cycle Finding Algorithm (hay còn gọi là thuật toán "rùa và thỏ") để phát hiện chu kỳ trong danh sách liên kết đơn. - -**8. Độ phức tạp thời gian của các thao tác cơ bản trên danh sách liên kết đơn là gì?** - -* **Truy cập phần tử:** O(n) - cần duyệt qua các phần tử từ đầu danh sách. -* **Chèn/Xóa phần tử ở đầu:** O(1) - thao tác trực tiếp trên nút đầu. -* **Chèn/Xóa phần tử ở cuối:** O(n) - cần duyệt qua toàn bộ danh sách để tìm nút cuối. -* **Tìm kiếm phần tử:** O(n) - cần duyệt qua các phần tử cho đến khi tìm thấy phần tử mong muốn. - -**9. Khi nào nên sử dụng danh sách liên kết đơn thay vì mảng?** - -Nên sử dụng danh sách liên kết đơn khi: - -* **Kích thước dữ liệu không xác định trước:** Danh sách liên kết đơn có thể phát triển hoặc thu nhỏ linh hoạt. -* **Thường xuyên chèn hoặc xóa phần tử ở đầu hoặc giữa danh sách:** Thao tác này hiệu quả hơn so với mảng. -* **Không cần truy cập ngẫu nhiên vào các phần tử:** Danh sách liên kết đơn không hỗ trợ truy cập ngẫu nhiên hiệu quả. - -**10. Làm cách nào để so sánh hai danh sách liên kết đơn?** - -Có thể so sánh hai danh sách liên kết đơn bằng cách duyệt qua từng phần tử của mỗi danh sách và so sánh giá trị của chúng cho đến khi tìm thấy sự khác biệt hoặc đến cuối danh sách. - -**11. Thực hành cấu trúc dữ liệu và giải thuật ở đâu?** -- https://luyencode.net/ -- https://leetcode.com/tag/linked-list/ - -Hi vọng bài viết này đã giúp bạn hiểu rõ hơn về danh sách liên kết đơn. Chúc bạn thành công! - \ No newline at end of file diff --git a/docs/data-structures/queue.md b/docs/data-structures/queue.md deleted file mode 100644 index a2fc69a..0000000 --- a/docs/data-structures/queue.md +++ /dev/null @@ -1,494 +0,0 @@ ---- -title: Queue (Hàng đợi) -tags: [data structures, queue, vietnamese] -description: Hàng đợi (Queue) là cấu trúc dữ liệu đúng như cái tên nó mô tả, giống như hành động xếp hàng của chúng ta, nhưng không có chuyện ai đó chen vào giữa hàng hay chờ lâu quá bỏ hàng đi về. -image: /img/docs/queue.jpeg ---- - -**Hàng đợi** (tiếng Anh: Queue) là cấu trúc dữ liệu đúng như cái tên nó mô tả, giống như hành động xếp hàng của chúng ta, nhưng không có chuyện ai đó chen vào giữa hàng hay chờ lâu quá bỏ hàng đi về. - -![Hình ảnh hàng đợi trong thực tế](/img/docs/image-1709219607693.png) - -Hàng đợi hoạt động theo nguyên tắc (FIFO) - First-In-First-Out, đến trước được phục vụ trước; Khác với [Ngăn xếp](/docs/data-structures/stack) theo nguyên tắc LIFO mà chúng ta đã tìm hiểu trước đó. - -## Hàng đợi (queue) là gì? - -Như đã trình bày ở trên, hàng đợi là cấu trúc dữ liệu chỉ cho phép can thiệp ở 2 đầu, một đầu là thêm dữ liệu mới, thì đầu còn lại là lấy dữ liệu ra - đồng nghĩa là dữ liệu đã ở trong hàng đợi lâu nhất. - -Như vậy, với cấu trúc dữ liệu hàng đợi, chúng có 2 phương thức để chúng ta thao tác: -- Thêm dữ liệu vào 1 đầu, tùy vào ngôn ngữ lập trình mà nó có tên gọi khác nhau, có thể là `push`, `add`, `enqueue`, ... nhưng chỉ là khác tên phương thức. -- Lấy dữ liệu ra ở *đầu còn lại*, phương thức này cũng có nhiều cái tên như `pop`, `remove`, `dequeue`, ... - -![Mô phỏng hoạt động hàng đợi](/img/docs/image-1709351216668.png) - -Quan sát ảnh phía trên, khi bạn: -- Thêm phần tử vào cuối hàng đợi, chỉ số có `rear` sẽ tăng lên. -- Khi bạn xóa phần tử khỏi hàng đợi, chỉ số `front` cũng sẽ tăng lên. -- Vì hàng đợi là dãy số liên tiếp từ `front` đến `rear`, nên để lấy size thì bạn sẽ tính khoảng cách giữa `rear` và `front`. - -> :books: **Lưu ý**: Hàng đợi có 2 đầu và việc chọn đầu nào để thêm dữ liệu cũng được, khi đó đầu còn lại sẽ đảm nhiệm vai trò còn lại. - -Tương tự [Ngăn xếp](/docs/data-structures/stack), chúng ta cũng có các hàm hỗ trợ như -- `IsFull()` để kiểm tra hàng đợi đã đầy -- `IsEmpty()` để kiểm tra hàng đợi chưa có dữ liệu -- `Size()` để lấy kích thước hiện tại của hàng đợi - -> :warning: Lý thuyết trên đây là hàng đợi phiên bản gốc, chúng ta cũng có biến thể hàng đợi 2 đầu, khi đó chức năng thêm và lấy dữ liệu đều có trên cả 2 đầu, sẽ tìm hiểu ở mục tiếp theo. - -## Cài đặt hàng đợi - -Ở mục này, chúng ta sẽ thực hiện cài đặt các chức năng của Queue đã nói ở phần trước. Mục này tôi sẽ cùng các bạn đi cài đặt queue bằng mảng trong C/C++, bởi vì nó sẽ đơn giản hơn, giúp các bạn dễ hiểu hơn. Các cách cài đặt khác sẽ được trình bày ở phần sau. - -Để cài đặt được Queue, chúng ta sẽ cần sử dụng các biến như sau: - -- `queue[]`: Một mảng một chiều lưu dữ liệu của hàng đợi, sẽ khai báo trong hàm main sau. -- `MAX_SIZE`: Số lượng phần tử tối đa có thể lưu trữ trong hàng đợi -- `front`: Chỉ số của phần tử ở đang đầu queue. Nó sẽ là chỉ số của phần tử sẽ bị xóa ở lần tiếp theo -- `rear`: Chỉ số của phần tử tiếp theo sẽ được thêm vào cuối của queue - -```cpp -const int MAX_SIZE = 7; -int front = 0; // chỉ số đầu -int rear = 0; // chỉ số cuối -``` - -Bây giờ ta sẽ đi vào cài đặt từng chức năng của Hàng đợi. - -### Các hàm phụ trợ - -```cpp -bool isEmpty() { - return front == rear; -} - -bool isFull() { - return rear == MAX_SIZE; -} - -int Size() { - // mình đang cài đặt rear là chỉ số mảng của phần tử tiếp theo, nên không cần cộng thêm 1 ở đây. - // Ví dụ front đang là 0, mảng có 3 phần tử (0, 1, 2) thì rear cuả mình đang là 3. - return rear - front; -} -``` - -## Thêm vào cuối hàng đợi - -Nếu hàng đợi chưa đầy, chúng ta sẽ thêm phần tử cần thêm vào cuối(rear) của hàng đợi. Ngược lại, thông báo lỗi. - -```cpp -void push(int queue[], int value) { - if (isFull()) { - printf("Queue is full\n"); - return; - } - // queue[rear++] = value; // tương tự 2 lệnh dưới - queue[rear] = value; - rear++; -} -``` - -### Lấy dữ liệu ra khỏi hàng đợi - -Nếu hàng đợi có ít nhất 1 phần tử, chúng ta sẽ tiến hành lấy và xóa bỏ phần tử ở đầu của hàng đợi bằng cách tăng front lên 1 giá trị. - -```cpp -int pop(int queue[]) { - if (isEmpty()) { - printf("Queue is empty\n"); - return NULL; - } - int value = queue[front]; - front++; // Chỗ này là cộng chứ không phải trừ - return value; -} -``` - -### Mã nguồn kèm hàm main - -```cpp -// Hàng đợi - C++ -#include - -const int MAX_SIZE = 7; -int front = 0; // chỉ số đầu -int rear = 0; // chỉ số cuối - -bool isEmpty() { - return front == rear; -} - -bool isFull() { - return rear == MAX_SIZE; -} - -int size() { - return rear - front; -} - -void push(int queue[], int value) { - if (isFull()) { - printf("Queue is full\n"); - return; - } - // queue[rear++] = value; - queue[rear] = value; - rear++; -} - -int pop(int queue[]) { - if (isEmpty()) { - printf("Queue is empty\n"); - return NULL; - } - int value = queue[front]; - front++; // Chỗ này là cộng chứ không phải trừ - return value; -} - -int main(){ - int queue[MAX_SIZE]; - push(queue, 9); - push(queue, 5); - push(queue, 7); - push(queue, 3); - printf("Size: %d\n", size()); - push(queue, 4); - printf("Size: %d\n", size()); - int value = pop(queue); - printf("Pop: %d\n", value); - printf("Size: %d\n", size()); - return 0; -} -``` - -Chạy chương trình: - -``` -Size: 4 -Size: 5 -Pop: 9 -Size: 4 -``` - -## Thực hành với hàng đợi - -Để hiểu rõ hơn cấu trúc dữ liệu queue, chúng ta sẽ đi vào một bài toán cụ thể như sau: - -Bạn có một chuỗi ký tự. Hãy lấy ký tự ở đầu của chuỗi và thêm nó vào cuối chuỗi. Hãy cho tôi thấy sự thay đỗi của chuỗi sau khi thực hiện hành động trên N lần. - -Ý tưởng: Chuỗi ký tự trên có thể xem xét như là một hàng đợi. Tại mỗi bước, chúng ta sẽ pop(xóa) phần tử ở đầu chuỗi và thực hiện push(thêm) phần tử đó cuối chuỗi. Lặp lại N lần bước công việc này, chúng ta sẽ có câu trả lời. - -Lời giải: - -```cpp - // Ngăn xếp (Stack) - CPP -#include - -const int MAX_SIZE = 7; -int front = 0; // chỉ số đầu -int rear = 0; // chỉ số cuối - -bool isEmpty() { - return front == rear; -} - -bool isFull() { - return rear == MAX_SIZE; -} - -int size() { - return rear - front; -} - -void push(char queue[], char value) { - if (isFull()) { - printf("Queue is full\n"); - return; - } - // queue[rear++] = value; - queue[rear] = value; - rear++; -} - -char pop(char queue[]) { - if (isEmpty()) { - printf("Queue is empty\n"); - return NULL; - } - char value = queue[front]; - front++; // Chỗ này là cộng chứ không phải trừ - return value; -} - -int main(){ - char queue[MAX_SIZE]; - push(queue, 'a'); - push(queue, 'b'); - push(queue, 'c'); - push(queue, 'd'); - printf("Size: %d\n", size()); - int N = 3; // thực hiện 3 lần - for (int i = 0; i < N; i++) { - char c = pop(queue); - printf("pop: %c\n", c); - push(queue, c); - } - // print all - while (!isEmpty()) { - printf("%c ", pop(queue)); - } - return 0; -} -``` - -Kết quả: -``` -Size: 4 -pop: a -pop: b -pop: c -d a b c -``` -## Ứng dụng của hàng đợi - -Hàng đợi được sử dụng rất nhiều và nó giống như cách mà chúng ta xếp hàng ở nơi *tốc độ xử lý chậm hơn nhu cầu thực tế*. Thực tế này cũng xảy ra trong lập trình (hoặc chí ít là chúng ta áp dụng để đề phòng cho trường hợp đó). -- Máy in sử dụng hàng đợi để quản lý các nhiệm vụ in, lệnh nào tới trước sẽ được xử lý trước. -- Hệ điều hành cũng dùng hàng đợi để xử lý các công việc của nó. -- Thuật toán [tìm kiếm theo chiều rộng - BFS](/algorithms/bfs) cũng dùng hàng đợi để cài đặt. -- Và rất nhiều công cụ / thư viện áp dụng cơ chế hàng đợi như: Load Balancer (Cân bằng tải), Kafka / RabbitMQ, Redis, ... để phục vụ cho phát triển phần mềm. - -![ứng dụng của hàng đợi](/img/docs/image-1709360804528.png) - -## Circular queue(Hàng đợi vòng) -Đây là cách cài đặt nâng cao giúp hàng đợi tận dụng tối đa bộ nhớ. - -Quan sát cài đặt phía trên, khi lấy (pop) dữ liệu khỏi hàng đợi, chúng ta tăng chỉ số `front` lên, thêm thì chúng ta tăng chỉ số `rear` lên, như vậy vùng nhớ từ chỉ số `0` đến `front` sẽ bị lãng phí. - -Cách cài đặt này sẽ khắc phục điểm yếu đó. - -Hàng đợi vòng là một cải tiến của hàng đợi tiêu chuẩn. Trong hàng đợi tiêu chuẩn, khi một phần tử bị xóa khỏi hàng đợi, các vị trí bị xóa đó sẽ không được tái sử dụng. Hàng đợi vòng sinh ra để khắc phục sự lãng phí này. - -Hàng đợi vòng sử dụng lại các vị trí đã bị xóa ở đầu mảng -![file](/img/docs/image-1709361031869.png) - -Trong khi thêm phần tử vào hàng đợi vòng, nếu chỉ số `rear` đã ở vị trí cuối của mảng, khi đó bạn vẫn có thể thêm vào hàng đợi bằng cách thêm vào phía đầu của mảng (đó là các vị trí ở đầu mảng đã bị xóa đi và chưa được dùng). - -Để cài đặt hàng đợi vòng, ngoài các biến sử dụng trong hàng đợi tiêu chuẩn, chúng ta cần thêm một biến khác nữa để lưu số lượng phần tử đang có trong hàng đợi, đặt nó là `count` - -Bây giờ chúng ta cùng đi viết các hàm cho hàng đợi vòng nhé. - -```cpp -void push(char queue[], int value) { - if (isFull()) { - printf("Queue is full\n"); - return; - } - queue[rear] = value; - rear = (rear + 1) % MAX_SIZE; - count++; -} -``` - -Mình giải thích tại sao lại là `(rear + 1) % MAX_SIZE`: Giả sử khi `rear = arraySize - 1`, khi đó với hàng đợi tiêu chuẩn bạn sẽ không thể thêm nữa. Nhưng với hàng đợi vòng, ta sẽ thêm chừng nào `count < MAX_SIZE`. `MAX_SIZE` là số phần tử tối đa có thể có, do vậy, `count < MAX_SIZE` nghĩa là còn ô trống để insert vào queue. - -Chỉ số được insert vào queue như sau(giả sử `MAX_SIZE = 3` nhé): - -- Nếu `rear = 0`, giá trị rear thực là `(0 + 1) % 3 = 1` -- Nếu `rear = 1`, giá trị rear thực là `(1 + 1) % 3 = 2` -- Nếu `rear = 2`, giá trị rear thực là `(2 + 1) % 3 = 0` - -> :warning: Các bạn cần nhớ: `rear` là chỉ số của phần tử sẽ được insert ở lần tiếp theo. Mỗi khi pop, `count` sẽ phải giảm đi 1. Ngược lại, nếu push thành công, `count` sẽ tăng lên 1. - -Giải thích này cũng đúng với front trong hàm pop dưới đây. - -```cpp -int pop(char queue[]) { - if (isEmpty()) { - printf("Queue is empty\n"); - return NULL; - } - int value = queue[front]; - front = (front + 1) % MAX_SIZE; - count--; - return value; -} -``` - -Dưới đây là cài đặt đầy đủ đi kèm hàm main -```cpp -#include - -const int MAX_SIZE = 4; -int front = 0; // chỉ số đầu -int rear = 0; // chỉ số cuối -int count = 0; - -bool isEmpty() { - return count == 0; -} - -bool isFull() { - return count == MAX_SIZE; -} - -int size() { - return count; -} - -void push(char queue[], int value) { - if (isFull()) { - printf("Queue is full\n"); - return; - } - queue[rear] = value; - rear = (rear + 1) % MAX_SIZE; - count++; -} - -int pop(char queue[]) { - if (isEmpty()) { - printf("Queue is empty\n"); - return NULL; - } - int value = queue[front]; - front = (front + 1) % MAX_SIZE; - count--; - return value; -} - -int main(){ - char queue[MAX_SIZE]; - push(queue, 1); - push(queue, 2); - push(queue, 3); - push(queue, 4); - push(queue, 5); // full here - printf("Size: %d\n", size()); - printf("Pop: %d\n", pop(queue)); - push(queue, 5); // now not full - printf("Size: %d\n", size()); - return 0; -} -``` - -Kết quả thực thi -``` -Queue is full -Size: 4 -Pop: 1 -Size: 4 -``` - -## Hàng đợi 2 đầu - -Trong hàng đợi tiêu chuẩn phía trên, dữ liệu được thêm ở cuối và xóa đi ở đầu của hàng đợi. Nhưng với Double-ended queue, dữ liệu có thể thêm hoặc xóa ở cả đầu(front) và cuối(rear) của hàng đợi. - -Nào, hãy cùng tôi đi cài đặt các hàm cho các chức năng của queue 2 đầu nào. - -```cpp -// Hàng đợi 2 đầu - -#include - -#define MAX_SIZE 100 - -int top = 0; -int rear = 0; - -int size() { - return rear - top; -} - -int isEmpty() { - return size() == 0; -} - -int isFull() { - return size() == MAX_SIZE; -} - -void push_rear(int queue[], int value) { - if (isFull()) { - printf("Queue is full\n"); - return; - } - queue[rear++] = value; -} - -void push_front(int queue[], int value) { - if (isFull()) { - printf("Queue is full\n"); - return; - } - for (int i = rear; i > top; i--) { - queue[i] = queue[i - 1]; - } - queue[top] = value; - rear++; -} - -int pop_rear(int queue[]) { - if (isEmpty()) { - printf("Queue is empty\n"); - return -1; - } - return queue[--rear]; -} - -int pop_front(int queue[]) { - if (isEmpty()) { - printf("Queue is empty\n"); - return -1; - } - return queue[top++]; -} - -int get_rear(int queue[]) { - if (isEmpty()) { - printf("Queue is empty\n"); - return -1; - } - return queue[rear - 1]; -} - -int get_front(int queue[]) { - if (isEmpty()) { - printf("Queue is empty\n"); - return -1; - } - return queue[top]; -} - -int main() { - int queue[MAX_SIZE]; - push_rear(queue, 1); - push_rear(queue, 2); - push_rear(queue, 3); - push_rear(queue, 4); - push_rear(queue, 5); - printf("Front: %d\n", get_front(queue)); - printf("Rear: %d\n", get_rear(queue)); - printf("Pop front: %d\n", pop_front(queue)); - printf("Pop rear: %d\n", pop_rear(queue)); - printf("Front: %d\n", get_front(queue)); - printf("Rear: %d\n", get_rear(queue)); - return 0; -} -``` - -Kết quả thực thi: -``` -Front: 1 -Rear: 5 -Pop front: 1 -Pop rear: 5 -Front: 2 -Rear: 4 -``` - -## Bài tập thực hành - -Các bạn hãy thực hành các bài tập về hàng đợi trên LCOJ [tại đây](https://luyencode.net/problems/?type=20) nhé. - ![file](/img/docs/image-1709363915571.png) - \ No newline at end of file diff --git a/docs/data-structures/stack.md b/docs/data-structures/stack.md deleted file mode 100644 index c6b58ce..0000000 --- a/docs/data-structures/stack.md +++ /dev/null @@ -1,499 +0,0 @@ ---- -title: Stack (Ngăn xếp) -tags: [data structures, stack, vietnamese] -description: Ngăn xếp (Stack) là một cấu trúc dữ liệu khá cơ bản trong lập trình, hoạt động theo nguyên tắc “LIFO” (Last-In-First-Out) - phần tử được thêm vào cuối cùng sẽ bị lấy ra đầu tiên. Vẫn mông lung nhỉ? -image: /img/docs/stack.jpeg ---- - -**Ngăn xếp (Stack)** là một cấu trúc dữ liệu khá cơ bản trong lập trình, hoạt động theo nguyên tắc “LIFO” (Last-In-First-Out) - phần tử được thêm vào cuối cùng sẽ bị lấy ra đầu tiên. Vẫn mông lung nhỉ? - -Quan sát hình phía dưới trong khoanh vùng màu đỏ, bạn chắc không xa lạ với 3 nút này ở góc trên, bên trái của trình duyệt. Trong đó có 2 nút *go back* và *go forward*. Mỗi nút này được cài đặt bằng một ngăn xếp đó, cụ thể (với nút go back): -- Mỗi webpage bạn từng truy cập sẽ được đưa vào ngăn xếp. -- Khi click *go back*, ngăn xếp sẽ trả ra (và xóa khỏi ngăn xếp) webpage gần nhất bạn từng truy cập. - -![Ngăn xếp được ứng dụng trong quản lý lịch sử trình duyệt](/img/docs/image-1708960777450.17.44.png) - -## Ngăn xếp (stack) là gì? - -Trong khoa học máy tính, một ngăn xếp (còn gọi là bộ xếp chồng, tiếng Anh: Stack) là một cấu trúc dữ liệu trừu tượng hoạt động theo nguyên lý “vào sau ra trước” (Last In First Out (LIFO). Tức là, phần tử cuối cùng được chèn vào ngăn xếp sẽ là phần tử đầu tiên được lấy ra khỏi ngăn xếp. - -Một ví dụ trực quan, bạn có một chồng sách và bạn để nó trong một cái hộp như hình phía dưới. Giả sử hộp này vừa khít các cuốn sách. Khi đó, bạn có các thao tác: - -- Thêm một cuốn sách vào hộp (push) -- Lấy một cuốn sách khỏi hộp, bạn chỉ lấy được cuốn trên cùng(pop) - -![Minh họa ngăn xếp (stack)](/img/docs/image-1708961743652.png) - -:::info - -Như vậy, có 2 hành động duy nhất có thể thao tác với 1 ngăn xếp: -- Push: Thêm một phần tử vào đỉnh của ngăn xếp, và -- Pop: Lấy ra một phần tử đầu tiên ở đỉnh của ngăn xếp. - -::: - -Chúng ta có thể có thêm các phương thức để kiểm tra kích thước của ngăn xếp: -- IsEmpty: Kiểm tra ngăn xếp trống hay không. Ngăn xếp trống là ngăn xếp không có phần tử nào. -- IsFull: Kiểm tra ngăn xếp đã đầy hay chưa. Thao tác này không phải lúc nào cũng có. -- Size: Lấy số lượng phần tử stack đang có. - -## Cài đặt ngăn xếp (cơ bản) -Tại mục hướng dẫn này, mình sẽ cùng các bạn đi chi tiết từng thao tác của cấu trúc dữ liệu ngăn xếp. Chúng ta sẽ triển khai cài đặt ngăn xếp các số nguyên sử dụng mảng với ngôn ngữ C/C++. Ở mục tiếp theo, tôi sẽ cung cấp cho các bạn một số cách cài đặt khác (nâng cao). - -Chúng ta sẽ sử dụng mảng 1 chiều kiểu số nguyên (`int`) làm nơi lưu trữ ngăn xếp, một biến `capacity` để lưu kích thước tối đa của ngăn xếp và một biến `top` để lưu chỉ số của phần tử ở trên cùng của Stack, cũng là số lượng phần tử đang có của ngăn xếp khi cộng thêm 1. - -Đầu tiên, hãy khai báo các kiểu dữ liệu cần thiết - -```cpp -const int STACK_CAPACITY = 3; // Số phần tử tối đa stack có thể lưu, thực hành nên để số nhỏ -int top = -1; // Khởi tạo chưa có phần tử nào trong stack, nên chỉ số mảng để -1 -``` - -### Các hàm kiểm tra kích thước - -Hàm `IsFull()` sẽ kiểm tra xem stack hiện tại đã đầy hay chưa. Nếu chỉ số top của stack đang bằng với capacity - 1, tức stack đã đầy. - -```cpp -bool IsFull() { - if (top >= STACK_CAPACITY - 1) { - return true; - } else { - return false; - } -} -``` - -Khởi tạo stack không có phần tử nào, ta sẽ gán chỉ số `top = -1` để đánh dấu. Khi xóa hết phần tử, ta cũng đưa `top` về giá trị `-1`. Như vậy, để kiểm tra stack có đang rỗng hay không rất đơn giản. Ta chỉ cần so sánh giá trị `top` có bằng `-1` hay không mà thôi. - -Dưới đây là cài đặt cho hàm kiểm tra stack rỗng(IsEmpty): - -```cpp -bool IsEmpty() { - if (top == -1) { - return true; - } else { - return false; - } -} -``` - -Bạn cũng có thể viết thêm hàm lấy số lượng phần tử đang có trong ngăn xếp. -```cpp -int Size(){ - return top + 1; -} -``` - -## Thêm phần tử (Push) - -Chúng ta sẽ chỉ có thể push(thêm phần tử) vào đỉnh stack khi stack chưa đầy. Nếu stack đầy, chúng ta sẽ đưa ra thông báo và không thực hiện push. Ngược lại, ta sẽ tăng top lên một đơn vị và gán giá trị cho phần tử tại chỉ số top. - -```cpp -void Push(int stack[], int value) { - if (IsFull() == true) { - printf("\nStack is full. Can not add more!"); - } else { - ++top; - stack[top] = value; - } -} -``` - -## Lấy phần tử ra (Pop) - -Chúng ta sẽ chỉ có thể pop (lấy phần tử ra và xóa) khỏi đỉnh stack khi stack không trống. Nếu stack trống, chúng ta sẽ đưa ra thông báo và không thực hiện pop. Ngược lại, ta sẽ giảm giá trị top đi một đơn vị. - -```cpp - void Pop() { - if (IsEmpty() == true) { - printf("\nStack is empty. Nothing to pop!"); - } else { - --top; - } -} -``` - -## Mã nguồn ngăn xếp đầy đủ - -Lắp ghép các phần phía trên và thêm chúng vào hàm `main()`, ta có được ví dụ minh họa đầy đủ sau: - -```cpp -#include - -const int STACK_CAPACITY = 3; -int top = -1; - -bool IsFull() { - if (top >= STACK_CAPACITY - 1) { - return true; - } else { - return false; - } -} - -bool IsEmpty() { - if (top == -1) { - return true; - } else { - return false; - } -} - -void Push(int stack[], int value) { - if (IsFull() == true) { - printf("\nStack is full. Can not add more!"); - } else { - ++top; - stack[top] = value; - } -} - -void Pop() { - if (IsEmpty() == true) { - printf("\nStack is empty. Nothing to pop!"); - } else { - --top; - } -} - -int Top(int stack[]) { - return stack[top]; -} - -int Size() { - return top + 1; -} - -int main() { - int stack[STACK_CAPACITY]; - - // pushing element 5 in the stack . - Push(stack, 5); - - printf("\nCurrent size of stack is %d", Size()); - - Push(stack, 10); - Push(stack, 24); - - printf("\nCurrent size of stack is %d", Size()); - - // As the stack is full, further pushing will show an overflow condition. - Push(stack, 12); - - //Accessing the top element - printf("\nThe current top element in stack is %d", Top(stack)); - - //Removing all the elements from the stack - for (int i = 0; i < 3; i++) - Pop(); - printf("\nCurrent size of stack is %d", Size()); - - //As the stack is empty , further popping will show an underflow condition. - Pop(); -} -``` - -Nếu bạn chạy thử code trên, bạn sẽ nhận được kết quả: -``` -Current size of stack is 1 -Current size of stack is 3 -Stack is full. Can not add more! -The current top element in stack is 24 -Current size of stack is 0 -Stack is empty. Nothing to pop! -``` -Hình ảnh dưới đây sẽ giải thích chi tiết hơn về quá trình hoạt động của stack ở chương trình trên theo từng bước đã được đánh số thứ tự. - - ![Giải thích ngăn xếp](/img/docs/image-1708963398048.png) - - ## Thực hành với ngăn xếp - - Chúng ta sẽ sử dụng stack vào bài toán kiểm tra dãy ngoặc có hợp lệ hay không. - -Bạn có một dãy các dấu ngoặc bao gồm ngoặc đóng ‘)’ và ngoặc mở ‘(‘. Bạn phải kiểm tra xem dãy ngoặc đó có hợp lệ hay không. - -Một dãy ngoặc hợp lệ thì sẽ không thừa dấu ngoặc hoặc không có dấu ngoặc lẻ loi, chẳng hạn như: (), (()), ((()))() là các dãy ngoặc hợp lệ. Còn ((, ()), (()( là các dãy ngoặc không hợp lệ. - -Bạn có thể giải quyết bài toán này với stack. Hãy xem chúng ta làm như thế nào nhé. - -**Ý tưởng**: Duyệt qua từng dấu ngoặc trong dãy ngoặc; Sử dụng một stack để push các dấu ngoặc mở vào stack, mỗi khi gặp dấu ngoặc đóng, thực hiện pop một phần tử khỏi stack. Dãy ngoặc sẽ không hợp lệ khi bạn không thể pop hoặc khi kết thúc duyệt mà stack vẫn chưa rỗng. - -```cpp -#include - -int top; -void check(char str[], int n, char stack[]) { - for (int i = 0; i < n; i++) { - if (str[i] == '(') { - top = top + 1; - stack[top] = '('; - } - if (str[i] == ')') { - if (top == -1) { - top = top - 1; - break; - } else { - top = top - 1; - } - } - } - if (top == -1) - printf("String is balanced!\n"); - else - printf("String is unbalanced!\n"); -} - -int main() { - //balanced parenthesis string. - char str[] = "(())()"; - - // unbalanced string . - char str1[] = "((()"; - char stack[15]; - top = -1; - check(str, 9, stack); //Passing balanced string - top = -1; - check(str1, 5, stack); //Passing unbalanced string - return 0; - -} -``` - -Kết quả: -``` -String is balanced! -String is unbalanced! -``` - -> :loudspeaker: Ngoài ra, các bạn hãy tự thử sức với các [bài tập về ngăn xếp trên Luyện Code](https://luyencode.net/problems/?type=25) nhé. - -## Cài đặt ngăn xếp nâng cao - -### Cài đặt ngăn xếp với mảng cấp phát động (con trỏ) - -```cpp -#include -#include -#include - -typedef struct Stack { - int top; - unsigned capacity; - int * array; -} -Stack; - -Stack * createStack(unsigned capacity) { - struct Stack * stack = (Stack * ) malloc(sizeof(Stack)); - stack -> capacity = capacity; - stack -> top = -1; - stack -> array = (int * ) malloc(stack -> capacity * sizeof(int)); - return stack; -} - -int Full(Stack * stack) { - return stack -> top == stack -> capacity - 1; -} - -int Empty(Stack * stack) { - return stack -> top == -1; -} -void push(Stack * stack, int item) { - if (Full(stack)) - return; - stack -> array[++stack -> top] = item; - printf("%d pushed to stack\n", item); -} -int pop(Stack * stack) { - if (Empty(stack)) - return INT_MIN; - return stack -> array[stack -> top--]; -} -int peek(Stack * stack) { - if (Empty(stack)) - return INT_MIN; - return stack -> array[stack -> top]; -} -int main() { - Stack * stack = createStack(100); - - push(stack, 14); - push(stack, 25); - push(stack, 38); - push(stack, 48); - - printf("%d popped from stack\n", pop(stack)); - - printf("Top item is %d\n", peek(stack)); - - return 0; -} -//output -/*14 pushed to stack -25 pushed to stack -38 pushed to stack -48 pushed to stack -48 popped from stack -Top item is 38*/ -``` - -### Sử dụng hướng đối tượng C++ - -```cpp -#define Maxsize 10 -#include - -using namespace std; - -class stacks { - public: int Top = -1; - int s[10]; - void push(int); - void pop(); - void print(); -}; -void stacks::push(int x) { - if (Top == Maxsize) { - cout << "Error : cannot insert"; - } else { - Top++; - s[Top] = x; - } -} -void stacks::pop() { - if (Top < 0) { - cout << "Error : cannot delete"; - } else { - Top--; - return; - } -} -void stacks::print() { - int i; - cout << "Stack:"; - for (i = Top; i >= 0; i--) { - cout << s[i]; - } -} -int main() { - stacks o; - int e, i; - for (i = 0; i < 5; i++) { - cin >> e; - o.push(e); - } - //o.pop(); - o.print(); -} -``` - - ### Sử dụng [danh sách liên kết](/data-structures/linkedlist) - -Danh sách liên kết là cấu trúc dữ liệu khá phù hợp để cài đặt ngăn xếp đấy. - -```cpp -#include - -using namespace std; - -struct Stack { - int data; - Stack * next; -}* TOS = NULL; - -void Push(int value) { - Stack * new_node = new Stack(); - new_node -> data = value; - new_node -> next = TOS; - TOS = new_node; -} - -void Pop() { - if (TOS == NULL) - cout << "Stack is empty" << endl; - else { - Stack * temp; - temp = TOS; - TOS = TOS -> next; - temp -> next = NULL; - delete temp; - } -} - -void Print_Stack() { - if (TOS == NULL) - cout << "Stack is empty" << endl; - else { - cout << "Top of stack is " << TOS -> data << endl; - - if (TOS -> next != NULL) { - cout << "Other Elements are : "; - Stack * current = TOS -> next; - - while (current) { - cout << current -> data << " "; - current = current -> next; - } - - cout << endl; - } - } -} - -int main() { - for (int i = 0; i < 5; i++) - Push(i); - - Print_Stack(); - - for (int i = 0; i < 6; i++) - Pop(); - - Print_Stack(); - - return 0; -} - -/* Output -Top of stack is 4 -Other Elements are : 3 2 1 0 -Stack is empty -Stack is empty -*/ -``` - -### Sử dụng C++ STL - -Cách này code ngắn nhất, bởi vì dùng thư viện `` của [C++ STL](https://en.cppreference.com/w/cpp/standard_library). Sau khi bạn đã hiểu (tự cài đặt được), thì công việc sau này chỉ cần vài dòng code là xong. - -```cpp -#include -#include # thư viện này đó - -int main() { - std::stack < int > mystack; - - for (int i = 0; i < 5; ++i) mystack.push(i); - - std::cout << "Popping out elements..."; - while (!mystack.empty()) { - std::cout << ' ' << mystack.top(); - mystack.pop(); - } - std::cout << '\n'; - - return 0; -} -``` - -## Tổng kết - -Stack là một trong các cấu trúc dữ liệu căn bản mà mọi lập trình viên cần biết. Lúc mới học mình thắc mắc như này: "Tự nhiên đang lưu mảng chả có ràng buộc gì, tự nhiên đẻ ra thằng ngăn xếp ràng buộc, kiểu make it compilcated ^^". Nhưng thực tế thì cuộc sống cần có "quy tắc" :stuck_out_tongue_closed_eyes: - -![Ứng dụng của stack](/img/docs/image-1708964875915.png) - -Mình liệt kê thêm một số ứng dụng của ngăn xếp mà mình nghĩ bạn đã từng dùng qua mà không biết: -- Dùng cài đặt [thuật toán tìm kiếm theo chiều sâu - DFS](/algorithms/dfs) trong đồ thị. -- Chức năng undo/redo của các ứng dụng /phần mềm (MS Word chẳng hạn). -- Kiểm tra cú pháp hợp lệ (có thẻ mở đã có thẻ đóng chưa), đại khái giống bài tập thực hành ở trên. HTML là một ngôn ngữ cần thẻ mở / thẻ đóng như thế. -- Truy vết ngược (lưu lại nhật ký, và truy vết lại từ thông tin gần nhất để tìm ra nguyên nhân sâu xa). Xem ảnh phía trên, trackback message của một chương trình Python khi xảy ra lỗi `IndexError`. \ No newline at end of file diff --git a/docs/git.md b/docs/git.md deleted file mode 100644 index 2aef496..0000000 --- a/docs/git.md +++ /dev/null @@ -1,123 +0,0 @@ ---- -title: Git Working Rules -tags: [git] -description: My personal Git cheat sheet -image: https://upload.wikimedia.org/wikipedia/commons/thumb/e/e0/Git-logo.svg/1024px-Git-logo.svg.png ---- - -### 👥 Roles - -* **Repository Owner** - - * Manage repo access - * Prevent direct commits to `develop`, `test`, `uat`, `prod` - * Review merge important branches: `test`, `uat`, `prod` - -* **Maintainers (Lead Devs)** - - * Manage `release` & `hotfix` branches - * Review & merge pull requests (PRs) - * Help define working process - -* **Developers** - - * Create `feature/*`, `bugfix/*`, or `hotfix/*` branches from `develop` - * Submit PRs to `develop` when done. - * Include detailed description in PRs. - * Review and improve PRs. - -* **DevOps** - - * Manage deployments and system config - * Deploy code from `develop`, `test`, `uat`, `prod` - ---- - -### 🔁 Git Flow - -> Dev is the default branch. All development starts from here. - -1. **`develop`** – Active development (default branch) - - * All new work branches are created from here - * Only `develop` can merge into `test` - -2. **`test`** – Internal QA/testing - - * Only receives merges from `develop` - * Can merge into `uat` - -3. **`uat`** – Pre-release, client/user testing - - * Only receives merges from `test` - * Can merge into `prod` - -4. **`prod`** – Final, stable version - - * Only receives merges from `uat` - ---- - -### 🪴 Branching Rules - -* You **must use one of the following prefixes** for branches: - - * `feature/*` – For new features or enhancements - * `bugfix/*` – For fixing bugs found during development - * `hotfix/*` – For urgent, production-critical fixes -* All branches must be created from `develop` -* Example: `feature/user-login`, `bugfix/payment-crash`, `hotfix/api-down` - ---- - -### ✍️ Committing - -* Write **clear, meaningful** commit messages -* Avoid **unrelated changes** in a single commit - ---- - -### 🔀 Merging - -* All changes go through **Merge Requests (MRs)** -* MRs must be **code reviewed** -* **Use rebase on feature branches** to keep history clean -* **Use merge into `develop` and above** to retain full collaboration history -* Merge paths must follow environment flow: - - * `develop` → `test` → `uat` → `prod` -* Auto-merge only if tests pass (future feature) -* Delete merged branches (except protected ones) - ---- - -### 🏷️ Versioning & Clean Code - -* Use **tags** for releases -* Use `.gitignore` for non-tracked files (e.g. `.env`) -* Never commit **sensitive info** (keys, tokens) -* Document environment variables in `README` -* No **absolute paths** in code (e.g. `/home/user/...`) - ---- - -### 💡 Best Practices - -* **CI/CD**: Auto-test & build on commit -* **Conflict resolution**: Resolve before merge -* **Repo size**: Keep small (limit: 5–20MB) -* **Code conventions**: Follow team style guides -* **Code reviews**: Review before merging -* **Documentation**: Keep everything documented -* **Backup**: Regularly back up repo -* **Hooks**: Use Git hooks to validate code before commit -* **Cleanup**: Remove unused branches regularly - ---- - -### 📘 References - -* [Atlassian Git Flow Guide](https://www.atlassian.com/git/tutorials/comparing-workflows/gitflow-workflow) -* [Git Cheat Sheet (GitHub)](https://education.github.com/git-cheat-sheet-education.pdf) -* [Learn Git Branching (interactive)](https://learngitbranching.js.org/) -* [Pro Git Book](https://git-scm.com/book/en/v2) diff --git a/docs/index.md b/docs/index.md deleted file mode 100644 index 558bfad..0000000 --- a/docs/index.md +++ /dev/null @@ -1,7 +0,0 @@ ---- -title: Introduction -id: index -slug: "/" ---- - -The tutorials are organized into the following sections: \ No newline at end of file diff --git a/docs/python-convention.md b/docs/python-convention.md deleted file mode 100644 index abfbc9f..0000000 --- a/docs/python-convention.md +++ /dev/null @@ -1,216 +0,0 @@ ---- -title: Python Code Convention -tags: [python, coding-convention] -description: My personal Python code convention -image: /img/docs/python.jpeg ---- - -## 1. Target Audience - -Developers building Python services, tools, or pipelines. - ---- - -## 2. Coding Style - -Follow [PEP 8](https://peps.python.org/pep-0008/) style. Key rules: - -### Indentation and Line Length - -* 4 spaces (no tabs) -* Max 79 characters per line - -### Imports - -* Group in order: standard lib → third-party → internal -* One import per line - -### Whitespace - -* Space around operators and after commas -* No space inside brackets - -### Naming Conventions - -* Variables/functions: `snake_case` -* Classes: `PascalCase` -* Constants: `ALL_CAPS` -* Protected/private: `_name`, `__name` - -### Comments & Docstrings - -* Use docstrings for all public code -* Use complete sentences -* Add `# TODO:` or `# NOTE:` where needed - ---- - -## 3. Project Structure & Environment - -Use modern Python project tools for clarity and maintainability: - -| Tool | Use Case | -| -------- | ------------------------------- | -| `poetry` | Dependency & virtualenv manager | -| `uv` | Fast `pip` & `venv` alternative | -| `conda` | (Optional) Data/ML workflows | - -### Recommended Layout - -```bash -your_project/ -├── pyproject.toml -├── src/ -│ └── your_module/ -└── tests/ -``` - -> Using `src/` layout helps prevent import issues and improves test isolation. - ---- - -## 4. Path Handling - -Always avoid `abspath`. Use `pathlib` with relative paths. - -### ❌ Not recommended: - -```python -import os -path = os.path.abspath(os.path.join(os.path.dirname(__file__), "file.txt")) -``` - -### ✅ Recommended: - -```python -from pathlib import Path - -path = Path(__file__).parent / "file.txt" -``` - ---- - -## 5. Linting & Formatting - -Use [`ruff`](https://docs.astral.sh/ruff/) with [`pre-commit`](https://pre-commit.com/). - -### Setup Steps - -1. Install: - - ```bash - pip install pre-commit - ``` - -2. Create `.pre-commit-config.yaml`: - - ```yaml - repos: - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v5.0.0 - hooks: - - id: trailing-whitespace - - id: check-yaml - - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.8 - hooks: - - id: ruff - ``` - -3. Install hooks: - - ```bash - pre-commit install - ``` - -### Ruff Config (`pyproject.toml`) - -```toml -[tool.ruff] -select = ["E", "F"] -ignore = ["E501"] # Ignore line length for now -``` - -### Usage - -```bash -ruff check src/ -ruff check src/ --fix -``` - ---- - -## 6. Editor Integration - -### Visual Studio Code - -* Enable **Format on Save** -* Install the **Ruff** extension - ---- - -## 7. Python Version - -All code should support **Python 3.11+**. Set it explicitly in `pyproject.toml`: - -```toml -[tool.poetry.dependencies] -python = ">=3.11" -``` - ---- - -## 8. Testing Conventions - -* Use `pytest` -* Place tests in the `tests/` directory -* Name tests `test_*.py` -* Test function names should describe expected behavior - -Example: - -```python -def test_parse_valid_config(): - ... -``` - ---- - -## 9. Logging and Config Management - -Use the team's **standardized logging and config templates**: - -* Import from our shared utility module -* Follow the documented interfaces in the shared repo - -> 🧩 *\[PLACEHOLDER: Insert internal repo link or code snippet here]* -> Example usage: -> -> ```python -> from our_utils.logging import get_logger -> from our_utils.config import load_config -> ``` - ---- - -## 10. Git Ignore Rules - -Common `.gitignore` entries: - -```gitignore -.venv/ -__pycache__/ -*.py[cod] -.ruff_cache/ -.mypy_cache/ -``` - ---- - -## 11. References - -* [PEP 8 – Style Guide for Python Code](https://peps.python.org/pep-0008/) -* [Ruff Docs](https://docs.astral.sh/ruff/) -* [Pre-commit Docs](https://pre-commit.com/) -* [Hypermodern Python Guide](https://cjolowicz.github.io/posts/hypermodern-python-01-setup/) -* [Pytest Docs](https://docs.pytest.org/en/stable/) diff --git a/docusaurus.config.js b/docusaurus.config.js deleted file mode 100644 index 6e1fbc0..0000000 --- a/docusaurus.config.js +++ /dev/null @@ -1,176 +0,0 @@ -import autoprefixer from "autoprefixer"; -import katex from "rehype-katex"; -import math from "remark-math"; -import tailwind from "tailwindcss"; - -const internetProfiles = { - linkedin: { - label: "LinkedIn", - href: "https://www.linkedin.com/in/behitek/", - }, - github: { - label: "GitHub", - href: "https://github.com/behitek", - }, - twitter: { - label: "Twitter", - href: "https://twitter.com/behitek_", - }, - email: { - label: "Email", - href: "mailto:hello@behitek.com", - }, - blog: { - label: "Blog", - to: "blog", - }, - docs: { - label: "Docs", - to: "docs", - }, - projects: { - label: "Projects", - to: "projects", - }, - resume: { - label: "Resume", - href: "https://behitek.com/pdf/resume.pdf", - }, - lcoj: { - label: "Luyện Code (LCOJ)", - href: "https://luyencode.net", - }, -}; - -module.exports = { - title: "Behitek", - tagline: - "A drop in the AI ocean", - url: "https://behitek.com", - baseUrl: "/", - onBrokenLinks: "warn", - favicon: "img/favicon.ico", - organizationName: "behitek", - projectName: "behitek.github.io", - themeConfig: { - colorMode: { - defaultMode: "light", - disableSwitch: false, - respectPrefersColorScheme: true, - }, - navbar: { - hideOnScroll: true, - title: "Behitek", - logo: { - alt: "Behitek", - src: "img/logo.png", - target: "_self", - }, - items: [ - { - to: "docs/", - activeBasePath: "docs", - label: "Docs", - position: "right", - }, - { to: "blog/", label: "Blog", position: "right" }, - { to: "minesweeper/", label: "Games", position: "right" }, - { - href: "https://behitek.com/pdf/resume.pdf", - label: "Resume", - position: "right", - }, - ], - }, - footer: { - links: [ - { - title: "Connect", - items: [ - internetProfiles.linkedin, - internetProfiles.github, - internetProfiles.twitter, - internetProfiles.email, - ], - }, - { - title: "Discover", - items: [ - internetProfiles.blog, - internetProfiles.docs, - internetProfiles.projects, - internetProfiles.resume, - ], - }, - { - title: "Products", - items: [ - internetProfiles.lcoj, - ], - }, - ], - // Please adjust the following to your needs - copyright: `Copyright © ${new Date().getFullYear()} Behitek.com • Last updated on ${new Date().toLocaleDateString("en-SG")}`, - }, - algolia: { - appId: 'WHUI4DPU2B', - apiKey: '8040908c4adfc070dff77bb85b621ea7', - indexName: 'behitek', - // TODO, fix not working if contextualSearch is true - // https://docusaurus.io/docs/search#using-algolia-docsearch - contextualSearch: false, - } - }, - presets: [ - [ - "@docusaurus/preset-classic", - { - docs: { - sidebarPath: require.resolve("./sidebars.js"), - disableVersioning: false, - editCurrentVersion: false, - remarkPlugins: [math], - rehypePlugins: [katex], - // editUrl: "https://github.com/behitek/behitek.github.io/tree/main/", - }, - blog: { - showReadingTime: true, - blogSidebarCount: 10, // Show up to 10 recent posts in sidebar - blogSidebarTitle: 'Recent posts', - remarkPlugins: [math], - rehypePlugins: [katex], - // Please change this to your repo. - // editUrl: "https://github.com/behitek/behitek.github.io/tree/main/", - }, - theme: { - customCss: require.resolve("./src/css/custom.css"), - }, - }, - ], - ], - stylesheets: [ - { - href: 'https://cdn.jsdelivr.net/npm/katex@0.16.10/dist/katex.min.css', - type: 'text/css', - crossorigin: 'anonymous', - }, - ], - plugins: [ - async function tailwindPlugin(context, options) { - return { - name: "docusaurus-tailwindcss", - configurePostCss(postcssOptions) { - postcssOptions.plugins.push(tailwind); - postcssOptions.plugins.push(autoprefixer); - return postcssOptions; - }, - }; - }, - [ - '@docusaurus/plugin-google-tag-manager', - { - containerId: 'G-Q2VZMYFCJH', - }, - ], - ], -}; diff --git a/new-site/.github/workflows/deploy.yml b/new-site/.github/workflows/deploy.yml deleted file mode 100644 index b417a9d..0000000 --- a/new-site/.github/workflows/deploy.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: Deploy to GitHub Pages - -on: - push: - branches: [main, astro-main] - workflow_dispatch: - -permissions: - contents: read - pages: write - id-token: write - -concurrency: - group: "pages" - cancel-in-progress: false - -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: Checkout - uses: actions/checkout@v4 - - - name: Setup Node.js - uses: actions/setup-node@v4 - with: - node-version: '20' - cache: 'npm' - - - name: Install dependencies - run: npm ci - - - name: Build with Astro - run: npm run build - - - name: Upload artifact - uses: actions/upload-pages-artifact@v3 - with: - path: ./dist - - deploy: - needs: build - runs-on: ubuntu-latest - environment: - name: github-pages - url: ${{ steps.deployment.outputs.page_url }} - steps: - - name: Deploy to GitHub Pages - id: deployment - uses: actions/deploy-pages@v4 diff --git a/new-site/.gitignore b/new-site/.gitignore deleted file mode 100644 index f965de2..0000000 --- a/new-site/.gitignore +++ /dev/null @@ -1,29 +0,0 @@ -# Dependencies -node_modules/ - -# Build outputs -dist/ -.astro/ - -# Environment -.env -.env.* -!.env.example - -# IDE -.vscode/ -.idea/ -*.swp -*.swo -*~ - -# OS -.DS_Store -Thumbs.db - -# Logs -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* -lerna-debug.log* diff --git a/new-site/README.md b/new-site/README.md deleted file mode 100644 index 1bee22f..0000000 --- a/new-site/README.md +++ /dev/null @@ -1,365 +0,0 @@ -# Behitek Portfolio - Astro Edition - -> A blazing-fast, modern portfolio website for an AI Engineer, built with Astro 4.x and TailwindCSS. - -🌐 **Live Site:** [behitek.com](https://behitek.com) -🎨 **Framework:** Astro 4.x -💅 **Styling:** TailwindCSS 3.x -📝 **Content:** MDX for blog posts - ---- - -## ✨ Features - -### Core Features -- ⚡ **Lightning Fast**: < 1s load time with minimal JavaScript -- 🌙 **Dark Mode**: Smooth theme switching with persistence -- 📱 **Fully Responsive**: Mobile-first design -- 🎨 **Modern UI**: Glass morphism, gradient accents, smooth animations -- 🔍 **SEO Optimized**: Complete meta tags, OpenGraph, Twitter cards -- 📊 **RSS Feed**: Subscribe to blog updates -- ♿ **Accessible**: WCAG 2.1 AA compliant - -### Blog Features -- 📝 10+ technical articles about AI/ML, NLP, RAG, Python -- 🌐 **Bilingual Support**: English & Vietnamese posts (tagged) -- 🏷️ **Smart Filtering**: Filter by language and category -- 📖 **Table of Contents**: Auto-generated with active section highlighting -- 📈 **Reading Progress**: Visual progress bar -- 💬 **Comments**: Giscus (GitHub Discussions) -- 🔗 **Share Buttons**: Twitter, LinkedIn, Copy link -- ⏱️ **Reading Time**: Estimated time for each post - -### Project Showcase -- 🚀 Featured projects: LCOJ, Inverted HyDE, hoc-bash -- 📂 Categorized: Product, Research, Tutorial, Tool, Fun -- 🏷️ Tech stack tags -- 🔗 Links to GitHub, live demos, blog posts - -### Technical Highlights -- 🎯 TypeScript for type safety -- 🎨 Custom Tailwind theme with emerald green branding -- 📦 Content Collections for type-safe content -- 🖼️ Optimized images with Astro Image -- 🤖 Google Tag Manager integration -- 🚀 GitHub Actions deployment - ---- - -## 🚀 Quick Start - -### Prerequisites -- Node.js 20.x or higher -- npm or pnpm - -### Installation - -```bash -# Install dependencies -npm install - -# Start development server -npm run dev - -# Build for production -npm run build - -# Preview production build -npm run preview -``` - -### Development Commands - -```bash -npm run dev # Start dev server (http://localhost:4321) -npm run build # Build for production -npm run preview # Preview production build -npm run astro # Run Astro CLI commands -``` - ---- - -## 📁 Project Structure - -``` -/ -├── public/ # Static assets -│ ├── images/ # Images (copied from old site) -│ │ ├── blog/ # Blog post images -│ │ └── me.jpeg # Profile picture -│ ├── favicon.ico -│ ├── CNAME # Custom domain config -│ └── .nojekyll # GitHub Pages config -│ -├── src/ -│ ├── components/ # Reusable components -│ │ ├── Navbar.astro -│ │ ├── Footer.astro -│ │ ├── ThemeToggle.astro -│ │ ├── BlogCard.astro -│ │ ├── ProjectCard.astro -│ │ ├── LanguageBadge.astro -│ │ ├── TableOfContents.astro -│ │ ├── Giscus.astro -│ │ └── SEO.astro -│ │ -│ ├── layouts/ # Page layouts -│ │ ├── BaseLayout.astro -│ │ └── BlogPostLayout.astro -│ │ -│ ├── pages/ # Routes -│ │ ├── index.astro # Homepage -│ │ ├── blog/ -│ │ │ ├── index.astro # Blog listing -│ │ │ └── [...slug].astro # Blog post pages -│ │ ├── projects.astro -│ │ ├── contact.astro -│ │ ├── 404.astro -│ │ └── rss.xml.ts # RSS feed -│ │ -│ ├── content/ # Content collections -│ │ ├── blog/ # Blog posts (MDX) -│ │ │ └── inverted-hyde.mdx -│ │ ├── projects/ # Projects (JSON) -│ │ │ ├── lcoj.json -│ │ │ ├── inverted-hyde.json -│ │ │ └── hoc-bash.json -│ │ └── config.ts # Content collection schemas -│ │ -│ ├── styles/ # Global styles -│ │ └── global.css # Tailwind + custom styles -│ │ -│ └── utils/ # Utility functions -│ ├── constants.ts -│ └── helpers.ts -│ -├── .github/ -│ └── workflows/ -│ └── deploy.yml # GitHub Actions deployment -│ -├── astro.config.mjs # Astro configuration -├── tailwind.config.mjs # Tailwind configuration -├── tsconfig.json # TypeScript configuration -└── package.json -``` - ---- - -## 🎨 Design System - -### Color Palette - -```css -Primary (Emerald Green): - - #10B981 (Main) - - #059669 (Dark) - - #34D399 (Light) - -Accent: - - #06B6D4 (Cyan) - For highlights - - #8B5CF6 (Purple) - For AI/ML content - -Neutral (Slate): - - Light mode: #FFFFFF background, #0F172A text - - Dark mode: #0F172A background, #F8FAFC text -``` - -### Typography - -- **Headings**: Inter (Google Fonts) -- **Body**: System fonts for performance -- **Code**: JetBrains Mono / Fira Code - -### Components - -- **Glass Morphism**: Navbar with backdrop blur -- **Bento Grid**: About section layout -- **Cards**: Elevated cards with hover effects -- **Badges**: Language (EN/VI) and category badges -- **Buttons**: Primary (filled) and secondary (outlined) - ---- - -## 📝 Content Management - -### Adding Blog Posts - -1. Create new MDX file in `src/content/blog/`: - -```markdown ---- -title: "Your Post Title" -description: "Brief description" -date: 2025-11-12 -author: "Hieu Nguyen" -language: "en" # or "vi" -category: "AI/ML" # AI/ML, Python, Tutorial, Data -tags: ["tag1", "tag2"] -image: "/images/blog/your-image.jpg" -draft: false ---- - -Your content here... -``` - -2. Add images to `public/images/blog/` -3. Blog post will be automatically generated at `/blog/your-post-slug` - -### Adding Projects - -1. Create JSON file in `src/content/projects/`: - -```json -{ - "title": "Project Name", - "description": "Project description", - "category": "Product", - "tech": ["Python", "FastAPI", "Docker"], - "links": { - "website": "https://example.com", - "github": "https://github.com/..." - }, - "featured": true, - "order": 1 -} -``` - -2. Project will appear on homepage and projects page - ---- - -## 🔧 Configuration - -### Giscus Comments - -1. Enable GitHub Discussions in your repository -2. Visit [giscus.app](https://giscus.app) -3. Configure and get your repo ID and category ID -4. Update `src/components/Giscus.astro`: - -```astro -script.setAttribute('data-repo', 'behitek/behitek.github.io'); -script.setAttribute('data-repo-id', 'YOUR_REPO_ID'); -script.setAttribute('data-category-id', 'YOUR_CATEGORY_ID'); -``` - -### Google Tag Manager - -Update in `src/layouts/BaseLayout.astro`: - -```html - -``` - -### Custom Domain - -Update `public/CNAME` with your domain: - -``` -behitek.com -``` - ---- - -## 🚀 Deployment - -### GitHub Pages (Recommended) - -1. Push to main branch or designated branch -2. GitHub Actions will automatically build and deploy -3. Enable GitHub Pages in repo settings: - - Settings → Pages - - Source: GitHub Actions - -### Manual Deployment - -```bash -# Build -npm run build - -# Deploy dist/ folder to your hosting provider -``` - ---- - -## 📊 Performance - -### Lighthouse Scores (Target) -- Performance: 95+ -- Accessibility: 95+ -- Best Practices: 95+ -- SEO: 100 - -### Bundle Size -- JavaScript: ~5-10 KB (vs 200 KB with Docusaurus) -- CSS: ~20 KB (with TailwindCSS purging) -- First Load: < 1 second - ---- - -## 🛠️ Tech Stack - -- **Framework**: [Astro 4.x](https://astro.build) -- **Styling**: [TailwindCSS 3.x](https://tailwindcss.com) -- **Content**: MDX with Content Collections -- **Fonts**: Google Fonts (Inter) -- **Icons**: Emoji + SVG -- **Comments**: [Giscus](https://giscus.app) -- **Analytics**: Google Tag Manager -- **Hosting**: GitHub Pages -- **CI/CD**: GitHub Actions - ---- - -## 📚 Migration from Docusaurus - -See [MIGRATION_GUIDE.md](./MIGRATION_GUIDE.md) for detailed migration steps. - -**Summary**: -- Copy blog posts from `/blog/*.md` to `/src/content/blog/*.mdx` -- Update frontmatter format -- Fix image paths: `/img/blog` → `/images/blog` -- Remove `` tags -- Test each post - ---- - -## 🤝 Contributing - -This is a personal portfolio, but suggestions and bug reports are welcome! - -1. Fork the repository -2. Create your feature branch -3. Commit your changes -4. Push to the branch -5. Open a Pull Request - ---- - -## 📄 License - -© 2025 Hieu Nguyen. All rights reserved. - ---- - -## 🙏 Acknowledgments - -- Built with [Astro](https://astro.build) -- Styled with [TailwindCSS](https://tailwindcss.com) -- Comments powered by [Giscus](https://giscus.app) -- Hosted on [GitHub Pages](https://pages.github.com) - ---- - -## 📧 Contact - -- **Email**: hello@behitek.com -- **LinkedIn**: [linkedin.com/in/behitek](https://linkedin.com/in/behitek) -- **GitHub**: [github.com/behitek](https://github.com/behitek) -- **Twitter**: [@behitek_](https://twitter.com/behitek_) - ---- - -**Built with ❤️ by Hieu Nguyen** diff --git a/new-site/package-lock.json b/new-site/package-lock.json deleted file mode 100644 index 4af4ea4..0000000 --- a/new-site/package-lock.json +++ /dev/null @@ -1,8352 +0,0 @@ -{ - "name": "behitek-portfolio", - "version": "1.0.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "behitek-portfolio", - "version": "1.0.0", - "dependencies": { - "@astrojs/check": "^0.9.3", - "@astrojs/mdx": "^3.1.7", - "@astrojs/rss": "^4.0.7", - 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+ "devDependencies": { + "@tailwindcss/typography": "^0.5.15", + "@types/node": "^22.7.5", + "prettier": "^3.3.3", + "prettier-plugin-astro": "^0.14.1", + "prettier-plugin-tailwindcss": "^0.6.8" } } diff --git a/new-site/public/.nojekyll b/public/.nojekyll similarity index 100% rename from new-site/public/.nojekyll rename to public/.nojekyll diff --git a/new-site/public/CNAME b/public/CNAME similarity index 100% rename from new-site/public/CNAME rename to public/CNAME diff --git a/new-site/public/favicon.ico b/public/favicon.ico similarity index 100% rename from new-site/public/favicon.ico rename to public/favicon.ico diff --git a/new-site/public/images/blog/1709987562856.jpeg b/public/images/blog/1709987562856.jpeg similarity index 100% rename from new-site/public/images/blog/1709987562856.jpeg rename to public/images/blog/1709987562856.jpeg diff --git a/new-site/public/images/blog/RAG_FT_Table.jpg b/public/images/blog/RAG_FT_Table.jpg similarity index 100% rename from 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--git a/new-site/public/images/blog/stop-using-requirements.jpg b/public/images/blog/stop-using-requirements.jpg similarity index 100% rename from new-site/public/images/blog/stop-using-requirements.jpg rename to public/images/blog/stop-using-requirements.jpg diff --git a/new-site/public/images/blog/thuat-toan-beam-search.webp b/public/images/blog/thuat-toan-beam-search.webp similarity index 100% rename from new-site/public/images/blog/thuat-toan-beam-search.webp rename to public/images/blog/thuat-toan-beam-search.webp diff --git a/new-site/public/images/me.jpeg b/public/images/me.jpeg similarity index 100% rename from new-site/public/images/me.jpeg rename to public/images/me.jpeg diff --git a/sidebars.js b/sidebars.js deleted file mode 100644 index 0e8af82..0000000 --- a/sidebars.js +++ /dev/null @@ -1,36 +0,0 @@ -module.exports = { - // By default, Docusaurus generates a sidebar from the docs folder structure - // tutorialSidebar: [{ type: 'autogenerated', dirName: '.' }], - docs: [ - { - type: "doc", - id: "index", - }, - { - type: "category", - label: "Data Structures", - items: [ - "data-structures/stack", - "data-structures/queue", - "data-structures/linkedlist", - "data-structures/hash-table", - ] - }, - { - type: "category", - label: "Algorithms", - items: [ - "algorithms/bfs", - "algorithms/dfs", - ] - }, - { - type: "doc", - id: "git", - }, - { - type: "doc", - id: "python-convention", - }, - ] -}; diff --git a/new-site/src/components/BlogCard.astro b/src/components/BlogCard.astro similarity index 100% rename from new-site/src/components/BlogCard.astro rename to src/components/BlogCard.astro diff --git a/new-site/src/components/Footer.astro b/src/components/Footer.astro similarity index 100% rename from new-site/src/components/Footer.astro rename to src/components/Footer.astro diff --git a/new-site/src/components/Giscus.astro b/src/components/Giscus.astro similarity index 100% rename from new-site/src/components/Giscus.astro rename to src/components/Giscus.astro diff --git a/new-site/src/components/LanguageBadge.astro b/src/components/LanguageBadge.astro similarity index 100% rename from new-site/src/components/LanguageBadge.astro rename to src/components/LanguageBadge.astro diff --git a/new-site/src/components/LanguageSwitcher.astro b/src/components/LanguageSwitcher.astro similarity index 100% rename from new-site/src/components/LanguageSwitcher.astro rename to src/components/LanguageSwitcher.astro diff --git a/new-site/src/components/Navbar.astro b/src/components/Navbar.astro similarity index 100% rename from new-site/src/components/Navbar.astro rename to src/components/Navbar.astro diff --git a/new-site/src/components/ProjectCard.astro b/src/components/ProjectCard.astro similarity index 100% rename from new-site/src/components/ProjectCard.astro rename to src/components/ProjectCard.astro diff --git a/new-site/src/components/SEO.astro b/src/components/SEO.astro similarity index 100% rename from new-site/src/components/SEO.astro rename to src/components/SEO.astro diff --git a/new-site/src/components/SocialIcon.astro b/src/components/SocialIcon.astro similarity index 100% rename from new-site/src/components/SocialIcon.astro rename to src/components/SocialIcon.astro diff --git a/new-site/src/components/TableOfContents.astro b/src/components/TableOfContents.astro similarity index 100% rename from new-site/src/components/TableOfContents.astro rename to src/components/TableOfContents.astro diff --git a/new-site/src/components/ThemeToggle.astro b/src/components/ThemeToggle.astro similarity index 100% rename from new-site/src/components/ThemeToggle.astro rename to src/components/ThemeToggle.astro diff --git a/new-site/src/content/blog/beam-search.mdx b/src/content/blog/beam-search.mdx similarity index 100% rename from new-site/src/content/blog/beam-search.mdx rename to src/content/blog/beam-search.mdx diff --git a/new-site/src/content/blog/bert-finetuned.mdx b/src/content/blog/bert-finetuned.mdx similarity index 100% rename from new-site/src/content/blog/bert-finetuned.mdx rename to src/content/blog/bert-finetuned.mdx diff --git a/new-site/src/content/blog/flux-lora.mdx b/src/content/blog/flux-lora.mdx similarity index 100% rename from new-site/src/content/blog/flux-lora.mdx rename to src/content/blog/flux-lora.mdx diff --git a/new-site/src/content/blog/inverted-hyde.mdx b/src/content/blog/inverted-hyde.mdx similarity index 100% rename from new-site/src/content/blog/inverted-hyde.mdx rename to src/content/blog/inverted-hyde.mdx diff --git a/new-site/src/content/blog/opensource-github-copilot.mdx b/src/content/blog/opensource-github-copilot.mdx similarity index 100% rename from new-site/src/content/blog/opensource-github-copilot.mdx rename to src/content/blog/opensource-github-copilot.mdx diff --git a/new-site/src/content/blog/python-313-free-threaded.mdx b/src/content/blog/python-313-free-threaded.mdx similarity index 100% rename from new-site/src/content/blog/python-313-free-threaded.mdx rename to src/content/blog/python-313-free-threaded.mdx diff --git a/new-site/src/content/blog/python-decorator.mdx b/src/content/blog/python-decorator.mdx similarity index 100% rename from new-site/src/content/blog/python-decorator.mdx rename to src/content/blog/python-decorator.mdx diff --git a/new-site/src/content/blog/rag-in-production.mdx b/src/content/blog/rag-in-production.mdx similarity index 100% rename from new-site/src/content/blog/rag-in-production.mdx rename to src/content/blog/rag-in-production.mdx diff --git a/new-site/src/content/blog/selenium-capture-http-request.mdx b/src/content/blog/selenium-capture-http-request.mdx similarity index 100% rename from new-site/src/content/blog/selenium-capture-http-request.mdx rename to src/content/blog/selenium-capture-http-request.mdx diff --git a/new-site/src/content/blog/stop-using-requirements.mdx b/src/content/blog/stop-using-requirements.mdx similarity index 100% rename from new-site/src/content/blog/stop-using-requirements.mdx rename to src/content/blog/stop-using-requirements.mdx diff --git a/new-site/src/content/config.ts b/src/content/config.ts similarity index 100% rename from new-site/src/content/config.ts rename to src/content/config.ts diff --git a/new-site/src/content/projects/behivest.json b/src/content/projects/behivest.json similarity index 100% rename from new-site/src/content/projects/behivest.json rename to src/content/projects/behivest.json diff --git a/new-site/src/content/projects/beli5.json b/src/content/projects/beli5.json similarity index 100% rename from new-site/src/content/projects/beli5.json rename to src/content/projects/beli5.json diff --git a/new-site/src/content/projects/lcoj.json b/src/content/projects/lcoj.json similarity index 100% rename from new-site/src/content/projects/lcoj.json rename to src/content/projects/lcoj.json diff --git a/src/css/custom.css b/src/css/custom.css deleted file mode 100644 index 0a4edc6..0000000 --- a/src/css/custom.css +++ /dev/null @@ -1,202 +0,0 @@ -/* stylelint-disable docusaurus/copyright-header */ -/** - * Any CSS included here will be global. The classic template - * bundles Infima by default. Infima is a CSS framework designed to - * work well for content-centric websites. - */ - @tailwind base; - @tailwind components; - @tailwind utilities; - -/* Modern green theme color palette */ -:root { - /* Primary colors - Modern emerald/teal green */ - --ifm-color-primary: #10B981; - --ifm-color-primary-dark: #059669; - --ifm-color-primary-darker: #047857; - --ifm-color-primary-darkest: #065F46; - --ifm-color-primary-light: #34D399; - --ifm-color-primary-lighter: #6EE7B7; - --ifm-color-primary-lightest: #D1FAE5; - - /* Secondary colors - Warm earth tones */ - --ifm-color-secondary: #78716C; - --ifm-color-secondary-dark: #57534E; - --ifm-color-secondary-darker: #44403C; - --ifm-color-secondary-darkest: #1C1917; - --ifm-color-secondary-light: #A8A29E; - --ifm-color-secondary-lighter: #E7E5E4; - --ifm-color-secondary-lightest: #FAFAF9; - - /* Background colors */ - --ifm-background-color: #FFFFFF; - --ifm-navbar-background-color: #FFFFFF; - --ifm-footer-background-color: #F9FAFB; - - /* Accent colors */ - --ifm-color-success: #10B981; - --ifm-color-warning: #F59E0B; - --ifm-color-danger: #EF4444; - --ifm-link-color: #10B981; - - /* Typography */ - --ifm-code-font-size: 95%; - --ifm-font-family-base: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', sans-serif; - - /* Spacing and borders */ - --ifm-global-radius: 0.5rem; - --ifm-button-border-radius: 0.5rem; - --ifm-card-border-radius: 0.75rem; - - /* Shadows */ - --ifm-global-shadow-lw: 0 1px 2px 0 rgb(0 0 0 / 0.05); - --ifm-global-shadow-md: 0 4px 6px -1px rgb(0 0 0 / 0.1), 0 2px 4px -2px rgb(0 0 0 / 0.1); - --ifm-global-shadow-tl: 0 20px 25px -5px rgb(0 0 0 / 0.1), 0 8px 10px -6px rgb(0 0 0 / 0.1); -} - -/* Dark theme overrides */ -[data-theme='dark'] { - --ifm-color-primary: #34D399; - --ifm-color-primary-dark: #10B981; - --ifm-color-primary-darker: #059669; - --ifm-color-primary-darkest: #047857; - --ifm-color-primary-light: #6EE7B7; - --ifm-color-primary-lighter: #A7F3D0; - --ifm-color-primary-lightest: #D1FAE5; - - --ifm-background-color: #0F1419; - --ifm-navbar-background-color: #1C2128; - --ifm-footer-background-color: #1C2128; -} - -.docusaurus-highlight-code-line { - background-color: rgb(72, 77, 91); - display: block; - margin: 0 calc(-1 * var(--ifm-pre-padding)); - padding: 0 var(--ifm-pre-padding); -} - -h1 { - font-size: 2.4rem !important; -} - -h2 { - font-size: 2rem !important; -} - -h3 { - font-size: 1.6rem !important; -} - -h4 { - font-size: 1.2rem !important; -} - -p { - font-size: 1rem; -} - -.admonition a, -.admonition a:hover { - color: var(--ifm-alert-color); - text-decoration: underline; -} - -.wideBodyMaxWidth { - max-width: 640px; -} - -.contentTableContainer table { - display: table; - text-align: left; - width: 100%; - margin: 0; -} - -.contentTableContainer th:first-child { - width: 5%; -} - -.contentTableContainer td:first-child { - text-align: right; -} - -.contentTableContainer th:last-child { - width: 25%; -} - -.button:hover:any-link { - color: var(--ifm-button-color); -} - -/* Modern component styles */ -.navbar { - box-shadow: var(--ifm-global-shadow-lw); - border-bottom: 1px solid var(--ifm-color-secondary-lightest); -} - -.card { - border-radius: var(--ifm-card-border-radius); - box-shadow: var(--ifm-global-shadow-md); - border: 1px solid var(--ifm-color-secondary-lightest); - transition: all 0.2s ease-in-out; -} - -.card:hover { - box-shadow: var(--ifm-global-shadow-tl); - transform: translateY(-2px); -} - -.button--primary { - background: linear-gradient(135deg, var(--ifm-color-primary) 0%, var(--ifm-color-primary-dark) 100%); - border: none; - transition: all 0.2s ease-in-out; -} - -.button--primary:hover { - background: linear-gradient(135deg, var(--ifm-color-primary-dark) 0%, var(--ifm-color-primary-darker) 100%); - transform: translateY(-1px); - box-shadow: var(--ifm-global-shadow-md); -} - -/* Modern typography */ -h1, h2, h3, h4, h5, h6 { - font-weight: 600; - line-height: 1.2; -} - -/* Improved code blocks */ -.prism-code { - border-radius: var(--ifm-global-radius); - box-shadow: var(--ifm-global-shadow-lw); -} - -/* Modern pagination */ -.pagination-nav__link { - border-radius: var(--ifm-global-radius); - border: 1px solid var(--ifm-color-secondary-lighter); - transition: all 0.2s ease-in-out; -} - -.pagination-nav__link:hover { - box-shadow: var(--ifm-global-shadow-md); - transform: translateY(-1px); -} - -@media screen and (max-width: 966px) { - h1 { - font-size: 2.4rem; - } - - h2 { - font-size: 1.6rem; - } - - h3 { - font-size: 1.4rem; - } - - p { - font-size: 1rem; - } -} diff --git a/src/data/_Projects.js b/src/data/_Projects.js deleted file mode 100644 index cb1610b..0000000 --- a/src/data/_Projects.js +++ /dev/null @@ -1,34 +0,0 @@ -import React from "react"; - -const projects = [ - { - category: "Project", - title: "Sample", - slug: "#sample", - subtitle: - "An example.", - period: "November 2020", - tech: "AWS CloudFormation", - description: ( - <> -

- 1. This is a sample project. -

-

- 2. This is a sample project. -

-

- 3. This is a sample project. -

- - ), - links: [ - { - name: "GitHub repository", - link: "https://github.com/behitek", - }, - ], - }, -]; - -export default projects; diff --git a/src/data/_SocialLinks.js b/src/data/_SocialLinks.js deleted file mode 100644 index 16a7e13..0000000 --- a/src/data/_SocialLinks.js +++ /dev/null @@ -1,27 +0,0 @@ -import { faGithub, faLinkedin, faTwitter } from "@fortawesome/free-brands-svg-icons"; -import { faPaperPlane } from "@fortawesome/free-solid-svg-icons"; - -const socialLinks = [ - { - faIcon: faLinkedin, - alt: "Linkedin", - url: "https://www.linkedin.com/in/behitek", - }, - { - faIcon: faGithub, - alt: "GitHub", - url: "https://github.com/behitek", - }, - { - faIcon: faTwitter, - alt: "Twitter", - url: "https://twitter.com/behitek_", - }, - { - faIcon: faPaperPlane, - alt: "Email", - url: "mailto:hello@behitek.com", - }, -]; - -export default socialLinks; \ No newline at end of file diff --git a/new-site/src/env.d.ts b/src/env.d.ts similarity index 100% rename from new-site/src/env.d.ts rename to src/env.d.ts diff --git a/new-site/src/i18n/en.ts b/src/i18n/en.ts similarity index 100% rename from new-site/src/i18n/en.ts rename to src/i18n/en.ts diff --git a/new-site/src/i18n/utils.ts b/src/i18n/utils.ts similarity index 100% rename from new-site/src/i18n/utils.ts rename to src/i18n/utils.ts diff --git a/new-site/src/i18n/vi.ts b/src/i18n/vi.ts similarity index 100% rename from new-site/src/i18n/vi.ts rename to src/i18n/vi.ts diff --git a/new-site/src/layouts/BaseLayout.astro b/src/layouts/BaseLayout.astro similarity index 100% rename from new-site/src/layouts/BaseLayout.astro rename to src/layouts/BaseLayout.astro diff --git a/new-site/src/layouts/BlogPostLayout.astro b/src/layouts/BlogPostLayout.astro similarity index 100% rename from new-site/src/layouts/BlogPostLayout.astro rename to src/layouts/BlogPostLayout.astro diff --git a/new-site/src/pages/404.astro b/src/pages/404.astro similarity index 100% rename from new-site/src/pages/404.astro rename to src/pages/404.astro diff --git a/src/pages/bank/index.js b/src/pages/bank/index.js deleted file mode 100644 index 94b94de..0000000 --- a/src/pages/bank/index.js +++ /dev/null @@ -1,25 +0,0 @@ -// index.js -import Layout from '@theme/Layout'; -import React from 'react'; -import styles from './styles.module.css'; - -export default function QRCode() { - return ( - -
-
-

Scan to Pay

-
- Payment QR Code -
-
-
- - ); -} \ No newline at end of file diff --git a/src/pages/bank/styles.module.css b/src/pages/bank/styles.module.css deleted file mode 100644 index f70382b..0000000 --- a/src/pages/bank/styles.module.css +++ /dev/null @@ -1,75 +0,0 @@ -/* styles.module.css */ -.container { - display: flex; - justify-content: center; - align-items: center; - min-height: 100vh; - background: linear-gradient(45deg, #FF6B6B, #4ECDC4); - animation: gradientFlow 15s ease infinite; - background-size: 400% 400%; - position: relative; - overflow: hidden; -} - -.container::before { - content: ""; - position: absolute; - top: 0; - left: 0; - right: 0; - bottom: 0; - background: url("data:image/svg+xml,%3Csvg width='100' height='100' viewBox='0 0 100 100' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M11 18c3.866 0 7-3.134 7-7s-3.134-7-7-7-7 3.134-7 7 3.134 7 7 7zm48 25c3.866 0 7-3.134 7-7s-3.134-7-7-7-7 3.134-7 7 3.134 7 7 7zm-43-7c1.657 0 3-1.343 3-3s-1.343-3-3-3-3 1.343-3 3 1.343 3 3 3zm63 31c1.657 0 3-1.343 3-3s-1.343-3-3-3-3 1.343-3 3 1.343 3 3 3zM34 90c1.657 0 3-1.343 3-3s-1.343-3-3-3-3 1.343-3 3 1.343 3 3 3zm56-76c1.657 0 3-1.343 3-3s-1.343-3-3-3-3 1.343-3 3 1.343 3 3 3zM12 86c2.21 0 4-1.79 4-4s-1.79-4-4-4-4 1.79-4 4 1.79 4 4 4zm28-65c2.21 0 4-1.79 4-4s-1.79-4-4-4-4 1.79-4 4 1.79 4 4 4zm23-11c2.76 0 5-2.24 5-5s-2.24-5-5-5-5 2.24-5 5 2.24 5 5 5zm-6 60c2.21 0 4-1.79 4-4s-1.79-4-4-4-4 1.79-4 4 1.79 4 4 4zm29 22c2.76 0 5-2.24 5-5s-2.24-5-5-5-5 2.24-5 5 2.24 5 5 5zM32 63c2.76 0 5-2.24 5-5s-2.24-5-5-5-5 2.24-5 5 2.24 5 5 5zm57-13c2.76 0 5-2.24 5-5s-2.24-5-5-5-5 2.24-5 5 2.24 5 5 5zm-9-21c1.105 0 2-.895 2-2s-.895-2-2-2-2 .895-2 2 .895 2 2 2zM60 91c1.105 0 2-.895 2-2s-.895-2-2-2-2 .895-2 2 .895 2 2 2zM35 41c1.105 0 2-.895 2-2s-.895-2-2-2-2 .895-2 2 .895 2 2 2z' fill='rgba(255,255,255,0.1)' fill-rule='evenodd'/%3E%3C/svg%3E"); - opacity: 0.5; - animation: movePattern 30s linear infinite; -} - -@keyframes gradientFlow { - 0% { background-position: 0% 50% } - 50% { background-position: 100% 50% } - 100% { background-position: 0% 50% } -} - -@keyframes movePattern { - 0% { transform: translateY(0) } - 100% { transform: translateY(-100%) } -} - -.qrSection { - background: rgba(255, 255, 255, 0.95); - backdrop-filter: blur(10px); - border-radius: 20px; - box-shadow: 0 8px 32px rgba(0, 0, 0, 0.1); - padding: 2rem; - max-width: 400px; - width: 90%; - z-index: 1; - transform: translateY(0); - transition: transform 0.3s ease; -} - -.qrSection:hover { - transform: translateY(-5px); -} - -.title { - text-align: center; - margin-bottom: 2rem; - color: #2d3436; - font-size: 2rem; - font-weight: 700; -} - -.qrWrapper { - display: flex; - justify-content: center; - padding: 1rem; - background: white; - border-radius: 12px; - box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.05); -} - -.qrImage { - max-width: 280px; - width: 100%; - height: auto; -} \ No newline at end of file diff --git a/new-site/src/pages/blog/[...slug].astro b/src/pages/blog/[...slug].astro similarity index 100% rename from new-site/src/pages/blog/[...slug].astro rename to src/pages/blog/[...slug].astro diff --git a/new-site/src/pages/blog/index.astro b/src/pages/blog/index.astro similarity index 100% rename from new-site/src/pages/blog/index.astro rename to src/pages/blog/index.astro diff --git a/src/pages/coc/index.js b/src/pages/coc/index.js deleted file mode 100644 index fc05921..0000000 --- a/src/pages/coc/index.js +++ /dev/null @@ -1,455 +0,0 @@ -import Layout from '@theme/Layout'; -import React, { useEffect, useState } from 'react'; -import styles from './styles.module.css'; - -// API configuration for Clash of Clans -export const API_CONFIG = { - BASE_URL: 'https://coc-apis.behitek.com', - CLAN_TAG: '%232G9YRCRV2', - endpoints: { - currentWar: '/clans/{tag}/currentwar', - clanInfo: '/clans/{tag}', - warLeague: '/clans/{tag}/currentwar/leaguegroup' - } -}; - -// Helper function to format endpoints with clan tag -export const getEndpoint = (endpoint) => { - return `${API_CONFIG.BASE_URL}${endpoint.replace('{tag}', API_CONFIG.CLAN_TAG)}`; -}; - -export default function ClanProfile() { - const [warData, setWarData] = useState(null); - const [clanData, setClanData] = useState(null); - const [isLoading, setIsLoading] = useState(true); - const [error, setError] = useState(null); - - const clanLinks = { - clanLink: "https://link.clashofclans.com/?action=OpenClanProfile&tag=2g9yrcrv2", - zaloLink: "https://zalo.me/g/wblyoe887", - }; - - useEffect(() => { - const fetchWarData = async () => { - try { - const response = await fetch(getEndpoint(API_CONFIG.endpoints.currentWar)); - const warApiData = await response.json(); - setWarData(warApiData); - } catch (err) { - console.error('Error fetching war data:', err); - } - }; - - fetchWarData(); - }, []); - - useEffect(() => { - const fetchClanData = async () => { - try { - const response = await fetch(getEndpoint(API_CONFIG.endpoints.clanInfo)); - const data = await response.json(); - setClanData(data); - setIsLoading(false); - } catch (err) { - setError(err.message); - setIsLoading(false); - } - }; - - fetchClanData(); - }, []); - - const clanPerks = { - donationWaitTime: "10 min", - donationLimits: "50 | 3 | 2", - donationLevelBoost: "+2", - treasuryCapacity: "+50%", - warLoot: "+25%" - }; - - if (isLoading) { - return ( - -
-
- Loading... -
Loading clan data...
-
-
- - ); - } - - if (error) { - return ( - -
-
-
Error loading clan data: {error}
-
-
- - ); - } - - return ( - -
-
- {/* Rest of your JSX remains the same */} -
-

{clanData.name || "N/A"}

- -
- League - {clanData.warLeague?.name || "N/A"} -
- {/* Description */} -
- {clanData.description} -
- {/* Action Buttons */} -
- - -
-
-
- -
- {/* Clan Badge and Labels */} -
- Clan Badge -
- {clanData.labels?.map((label) => ( - {label.name} - ))} -
-
- - {/* Stats Section */} -
-
- {Object.entries({ - 'Total Points:': `${clanData.clanPoints || 0} | ${clanData.clanVersusPoints || 0}`, - 'Wars Won:': `${clanData.warWins || 0} (${((clanData.warWins / (clanData.warWins + clanData.warLosses + (clanData.warTies || 0))) * 100).toFixed(1)}%)`, - 'War Win Streak:': clanData.warWinStreak || 0, - 'War Results:': ( - - {clanData.warWins || 0} - {' | '} - {clanData.warTies || 0} - {' | '} - {clanData.warLosses || 0} - - ), - 'Members:': `${clanData.members || 0} / 50`, - 'Status:': clanData.type ? clanData.type.charAt(0).toUpperCase() + clanData.type.slice(1) : "N/A", - 'War Frequency:': clanData.warFrequency ? clanData.warFrequency.charAt(0).toUpperCase() + clanData.warFrequency.slice(1) : "N/A", - 'Location:': ( - - {clanData.location?.countryCode && ( - Flag - )} - {clanData.location?.name || "N/A"} - - ), - }).map(([label, value]) => ( -
- {label} - {value} -
- ))} -
-
- - {/* Clan Perks */} -
-

Clan Perks

-
- {Object.entries({ - 'Donation Wait Time:': clanPerks.donationWaitTime, - 'Donation Limit (Troops|Spells|Sieges):': clanPerks.donationLimits, - 'Donation Level Boost:': clanPerks.donationLevelBoost, - 'Treasury Capacity:': clanPerks.treasuryCapacity, - 'War Loot:': clanPerks.warLoot, - }).map(([label, value]) => ( -
- {label} - {value} -
- ))} -
-
-
- - {/* Current War Section */} - {warData && ( -
-
-

Current War

- -
- {warData.state === 'notInWar' ? ( -
-
-
- - {(warLeagueStatus => { - if (warLeagueStatus === 'inactive' || warLeagueStatus === 'error') { - return ( - <> - 🏰 -

Not Currently In War

-

The clan is currently not participating in any war

- - ); - } - return null; - })()} -
- -
-
- ) : ( -
-
-
- Clan Badge -

{warData.clan.name}

-
-
- ⚔ {warData.clan.attacks}/{warData.teamSize * warData.attacksPerMember} -
-
-
- ⭐ {warData.clan.stars}/{warData.teamSize * 3} -
-
- {Object.entries( - warData.clan.members?.reduce((acc, member) => { - const th = member.townhallLevel; - acc[th] = (acc[th] || 0) + 1; - return acc; - }, {}) || {} - ).sort((a, b) => b[0] - a[0]).map(([th, count]) => ( -
- {`TH${th}`} - {count} -
- ))} -
-
- -
-
- vs -

{warData.state === 'preparation' ? 'Preparation Day' : 'Battle Day'}

-

-

{warData.teamSize} vs {warData.teamSize}

-
- -
- -
-
- Opponent Badge -

{warData.opponent.name}

-
-
- ⚔ {warData.opponent.attacks}/{warData.teamSize * warData.attacksPerMember} -
-
-
- ⭐ {warData.opponent.stars}/{warData.teamSize * 3} -
-
- {Object.entries(warData.opponent.members?.reduce((acc, member) => { - const th = member.townhallLevel; - acc[th] = (acc[th] || 0) + 1; - return acc; - }, {}) || {} - ).sort((a, b) => b[0] - a[0]).map(([th, count]) => ( -
- {`TH${th}`} - {count} -
- ))} -
-
-
- )} -
- )} -
-
- - ); -} - -function CheckWarLeague() { - const [warLeagueStatus, setWarLeagueStatus] = useState(null); - - useEffect(() => { - const checkWarLeague = async () => { - try { - const response = await fetch(getEndpoint(API_CONFIG.endpoints.warLeague)); - if (!response.ok) { - throw new Error(`HTTP error! status: ${response.status}`); - } - const data = await response.json(); - setWarLeagueStatus(data.state === 'notInWar' ? 'inactive' : 'active'); - } catch (err) { - console.error('Error checking war league status:', err); - setWarLeagueStatus('error'); - } - }; - - checkWarLeague(); - }, []); - - if (warLeagueStatus === 'active') { - return ( -
- ⚔️ -

Participating in War League

-

The clan is currently in CWL (Clan War League)

-
- ); - } - - return warLeagueStatus; -} - -function WarTimer({ warData }) { - const [timeRemaining, setTimeRemaining] = useState('N/A'); - - useEffect(() => { - const updateTime = () => { - if (!warData.startTime && !warData.endTime) { - setTimeRemaining('N/A'); - return; - } - - const now = new Date(); - let targetTime; - - if (warData.state === 'preparation') { - targetTime = new Date(warData.startTime.replace(/^(\d{4})(\d{2})(\d{2})T(\d{2})(\d{2})(\d{2})/, '$1-$2-$3T$4:$5:$6')); - } else { - targetTime = new Date(warData.endTime.replace(/^(\d{4})(\d{2})(\d{2})T(\d{2})(\d{2})(\d{2})/, '$1-$2-$3T$4:$5:$6')); - } - - const diffMs = targetTime.getTime() - now.getTime(); - - if (diffMs > 0) { - const hours = Math.floor(diffMs / (1000 * 60 * 60)); - const minutes = Math.floor((diffMs % (1000 * 60 * 60)) / (1000 * 60)); - setTimeRemaining(`${hours}h ${minutes}m`); - } else { - setTimeRemaining('N/A'); - } - }; - - // Initial update - updateTime(); - - // Set up interval to update every 20 seconds - const interval = setInterval(updateTime, 20000); - - // Cleanup interval on unmount - return () => clearInterval(interval); - }, [warData.startTime, warData.endTime, warData.state]); - - return timeRemaining; -} \ No newline at end of file diff --git a/src/pages/coc/styles.module.css b/src/pages/coc/styles.module.css deleted file mode 100644 index cc338de..0000000 --- a/src/pages/coc/styles.module.css +++ /dev/null @@ -1,376 +0,0 @@ -.wrapper { - background-color: #1a1a1a; - min-height: 100vh; - color: #ffffff; - padding: 2rem 0; -} - -.container { - max-width: 1200px; - margin: 0 auto; - padding: 0 1rem; -} - -.header { - text-align: center; - margin-bottom: 2rem; -} - -.header h1 { - font-size: 2.5rem; - margin-bottom: 0.5rem; - font-weight: bold; -} - -.tag { - font-size: 1.3rem; - color: #888; - margin-bottom: 1rem; -} - -.header hr { - border: 0; - border-top: 1px solid #333; - margin: 2rem 0; -} - -.leagueBadge { - display: inline-flex; - align-items: center; - gap: 0.5rem; - background: #2a2a2a; - padding: 0.5rem 1rem; - border-radius: 4px; -} - -.leagueBadge img { - width: 24px; - height: 24px; -} - -.content { - display: grid; - grid-template-columns: 200px 1fr 400px; - gap: 2rem; - margin-bottom: 2rem; -} - -.badgeSection { - text-align: center; - align-content: space-around; -} - -.mainBadge { - width: 200px; - height: 200px; - margin-bottom: 1rem; -} - -.clanLabels { - display: flex; - justify-content: center; - gap: 0.5rem; -} - -.clanLabels img { - width: 40px; - height: 40px; - border-radius: 4px; - padding: 4px; - background: #2a2a2a; -} - -.statsSection, .perksSection { - background: #2a2a2a; - border-radius: 8px; - padding: 1.5rem; -} - -.perksSection h3 { - margin-top: 0; - margin-bottom: 1rem; - font-size: 1.2rem; -} - -.statRow { - display: flex; - justify-content: space-between; - align-items: center; - padding: 0.25rem 0; - border-bottom: 1px solid #333; -} - -.statRow:last-child { - border-bottom: none; -} - -.textSuccess { - color: #74d67e; -} - -.textDanger { - color: #f76608; -} - -.textGray { - color: #CCCCCC; -} - -.location { - display: flex; - align-items: center; - gap: 0.5rem; -} - -.location img { - width: 20px; - height: 15px; -} - -.description { - margin-top: 2rem; - color: #ccc; - display: grid; - justify-content: center; - text-align: justify; - white-space: pre-line; -} - -.warSection { - margin-top: 2rem; - background: #2a2a2a; - border-radius: 8px; - padding: 1.5rem; -} - -.warSection h3 { - margin: 0; - font-size: 1.2rem; -} - -.warSection > div { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 2rem; -} - -.refreshButton { - padding: 0.5rem !important; - background: none !important; -} - - -.warContent { - display: flex; - flex-wrap: wrap; - justify-content: center; - align-items: center; - gap: 2rem; - width: 100%; -} - -.warTeam { - flex: 1; - min-width: 250px; - text-align: center; -} - -.warInfo { - flex: 0 1 auto; - min-width: 200px; - max-width: 400px; - text-align: center; - display: flex; - flex-direction: column; - justify-content: center; - align-items: center; - margin: 0 auto; -} - -@media (max-width: 768px) { - .warContent { - flex-direction: column; - align-items: center; - } - - .warTeam { - width: 100%; - } - - .warInfo { - width: 100%; - margin: 1rem 0; - } -} - -.warTeam img { - width: 40px; - height: 40px; - vertical-align: middle; -} - -.warTeam h4 { - margin: 0; - font-size: 1.1rem; - display: inline-flex; - align-items: center; -} - -.warProgress { - display: flex; - align-items: center; - gap: 0.5rem; - margin-bottom: 1rem; -} - -.warProgress img { - width: 20px; - height: 20px; - vertical-align: middle; -} - -.progressBar { - flex: 1; - height: 8px; - background: #1a1a1a; - border-radius: 4px; - overflow: hidden; -} - -.progressFill { - height: 100%; - background: #4CAF50; - transition: width 0.3s ease; -} - -.townHallDistribution { - display: flex; - justify-content: center; - gap: 0.5rem; -} - -.townHall { - /* background: #1a1a1a; */ - padding: 0.5rem; - border-radius: 4px; - font-size: 0.9rem; - display: flex; - flex-direction: column; - align-items: center; - gap: 0.25rem; -} - -.townHall img { - width: 32px; - height: 32px; -} - -.warInfo { - text-align: center; -} - -.warStatus { - margin-bottom: 1rem; -} - -.warStatus span { - font-size: 1.5rem; - font-weight: bold; - color: #888; -} - -.warStatus p { - margin: 0.5rem 0; - color: #888; -} - -.warDetailsBtn { - background: #1a1a1a; - border: none; - color: #fff; - padding: 0.5rem 1rem; - border-radius: 4px; - cursor: pointer; - transition: background-color 0.3s ease; -} - -.warDetailsBtn:hover { - background: #333; -} - -@media (max-width: 1024px) { - .content { - grid-template-columns: 1fr; - gap: 1.5rem; - } - - .badgeSection { - margin-bottom: 1rem; - } -} - -@media (max-width: 768px) { - .header h1 { - font-size: 2rem; - } - - .tag { - font-size: 1.2rem; - } - - .statRow { - font-size: 0.9rem; - } -} - -@keyframes spin { - 0% { transform: rotate(0deg); } - 100% { transform: rotate(360deg); } -} - -@keyframes fadeOut { - 0% { opacity: 1; } - 50% { opacity: 1; } - 100% { opacity: 0; } -} - -.refreshButton { - position: relative; -} - -.refreshButton svg { - transition: transform 0.3s ease; -} - -.refreshButton.processing svg { - animation: spin 1s linear infinite; -} - -.refreshButton.success svg, -.refreshButton.failure svg { - opacity: 0; -} - -.refreshButton.success::after, -.refreshButton.failure::after { - content: ''; - position: absolute; - top: 50%; - left: 50%; - transform: translate(-50%, -50%); - width: 20px; - height: 20px; - background-size: contain; - background-repeat: no-repeat; - animation: fadeOut 2s ease forwards; - opacity: 1; -} - -.refreshButton.success::after { - background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20' fill='%234CAF50'%3E%3Cpath fill-rule='evenodd' d='M16.707 5.293a1 1 0 010 1.414l-8 8a1 1 0 01-1.414 0l-4-4a1 1 0 011.414-1.414L8 12.586l7.293-7.293a1 1 0 011.414 0z' clip-rule='evenodd'/%3E%3C/svg%3E"); -} - -.refreshButton.failure::after { - background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20' fill='%23f44336'%3E%3Cpath fill-rule='evenodd' d='M4.293 4.293a1 1 0 011.414 0L10 8.586l4.293-4.293a1 1 0 111.414 1.414L11.414 10l4.293 4.293a1 1 0 01-1.414 1.414L10 11.414l-4.293 4.293a1 1 0 01-1.414-1.414L8.586 10 4.293 5.707a1 1 0 010-1.414z' clip-rule='evenodd'/%3E%3C/svg%3E"); -} \ No newline at end of file diff --git a/src/pages/components/_SocialLinks.js b/src/pages/components/_SocialLinks.js deleted file mode 100644 index dabe649..0000000 --- a/src/pages/components/_SocialLinks.js +++ /dev/null @@ -1,27 +0,0 @@ -import { FontAwesomeIcon } from "@fortawesome/react-fontawesome"; -import React from "react"; -import styles from "./styles.module.css"; - -import socialLinks from "../../data/_SocialLinks"; - -function SocialLink({ faIcon, alt, url }) { - return ( -
- - - -
- ); -} - -function SocialLinks() { - return ( -
- {socialLinks.map((props, idx) => ( - - ))} -
- ); -} - -export default SocialLinks; diff --git a/src/pages/components/styles.module.css b/src/pages/components/styles.module.css deleted file mode 100644 index 97ee1ae..0000000 --- a/src/pages/components/styles.module.css +++ /dev/null @@ -1,30 +0,0 @@ -/* stylelint-disable docusaurus/copyright-header */ - -.socialLinks { - display: flex; - flex-direction: row; - justify-content: center; - margin: 1.2rem 0; - padding-left: 0.3rem; -} - -.socialLinks div { - margin-right: 1rem; -} - -.socialLinks a { - font-size: 1rem; - width: 100%; -} - -@media screen and (max-width: 966px) { - .socialLinks { - justify-content: space-between; - padding: 0 1.6rem; - padding-bottom: 0.4rem; - } - - .socialLinks div { - margin-right: 0; - } -} diff --git a/new-site/src/pages/contact.astro b/src/pages/contact.astro similarity index 100% rename from new-site/src/pages/contact.astro rename to src/pages/contact.astro diff --git a/new-site/src/pages/index.astro b/src/pages/index.astro similarity index 100% rename from new-site/src/pages/index.astro rename to src/pages/index.astro diff --git a/src/pages/index.js b/src/pages/index.js deleted file mode 100644 index 18c247a..0000000 --- a/src/pages/index.js +++ /dev/null @@ -1,123 +0,0 @@ -import React, { useEffect, useRef, useState } from "react"; - -import Link from "@docusaurus/Link"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import useDocusaurusContext from "@docusaurus/useDocusaurusContext"; -import Layout from "@theme/Layout"; - -import SocialLinks from "./components/_SocialLinks"; -import styles from "./styles.module.css"; - -function Home() { - const context = useDocusaurusContext(); - const { siteConfig = {} } = context; - - const mainRef = useRef(null); - const [isLoading, setIsLoading] = useState(true); - const [headerHeight, setHeaderHeight] = useState(1536); - const [bannerHeight, setBannerHeight] = useState(256); - - useEffect(() => { - const tempHeaderHeight = Math.max(384, window.innerHeight); - setHeaderHeight(tempHeaderHeight); - setBannerHeight(Math.max(256, tempHeaderHeight / 2)); - setIsLoading(false); - mainRef.current.hidden = false; - }, []); - - return ( - -
-
-

Hi, I am Behitek!

-

A drop in the AI ocean!

- -

- - - -

-
-
-
-
-

- About me -

-
-
-
- -
-
-

Hello there 👋

-

- 🇻🇳 I am Hieu Nguyen, an AI Engineer from Vietnam. -

-

- 🚀 I love bringing AI into production to solve real problems, with a focus on natural language processing, retrieval augmented generation (RAG), LLMs, and ìnformation retrieval. -

-

- 🎓 I hold a Master of Information Science degree from the Japan Advanced Institute of Science and Technology (JAIST). -

-

- 💻 Besides coding, I enjoy exploring self-hosted solutions on Reddit's r/selfhosted and playing Clash of Clans. -

-
-
-
-
-

Continue exploring?

- - -
-
-
- - ); -} - -export default Home; diff --git a/src/pages/minesweeper/index.js b/src/pages/minesweeper/index.js deleted file mode 100644 index c4cdae4..0000000 --- a/src/pages/minesweeper/index.js +++ /dev/null @@ -1,254 +0,0 @@ -import Layout from '@theme/Layout'; -import React, { useEffect, useState } from 'react'; -import styles from './styles.module.css'; - -const DIFFICULTIES = { - easy: { rows: 8, cols: 8, mines: 10 }, - medium: { rows: 16, cols: 16, mines: 40 }, - hard: { rows: 16, cols: 30, mines: 99 } -}; - -const createBoard = (rows, cols, mines) => { - const board = Array(rows).fill().map(() => - Array(cols).fill().map(() => ({ - isMine: false, - isRevealed: false, - isFlagged: false, - neighborMines: 0 - })) - ); - - // Place mines randomly - let minesPlaced = 0; - while (minesPlaced < mines) { - const row = Math.floor(Math.random() * rows); - const col = Math.floor(Math.random() * cols); - if (!board[row][col].isMine) { - board[row][col].isMine = true; - minesPlaced++; - } - } - - // Calculate neighbor mines - for (let row = 0; row < rows; row++) { - for (let col = 0; col < cols; col++) { - if (!board[row][col].isMine) { - let count = 0; - for (let i = -1; i <= 1; i++) { - for (let j = -1; j <= 1; j++) { - if (row + i >= 0 && row + i < rows && col + j >= 0 && col + j < cols) { - if (board[row + i][col + j].isMine) count++; - } - } - } - board[row][col].neighborMines = count; - } - } - } - - return board; -}; - -const MinesweeperGame = () => { - const [difficulty, setDifficulty] = useState('medium'); - const [board, setBoard] = useState([]); - const [gameOver, setGameOver] = useState(false); - const [gameWon, setGameWon] = useState(false); - const [flagsPlaced, setFlagsPlaced] = useState(0); - const [time, setTime] = useState(0); - const [timerActive, setTimerActive] = useState(false); - - useEffect(() => { - resetGame(); - }, [difficulty]); - - useEffect(() => { - let interval; - if (timerActive && !gameOver && !gameWon) { - interval = setInterval(() => { - setTime(prevTime => prevTime + 1); - }, 1000); - } - return () => clearInterval(interval); - }, [timerActive, gameOver, gameWon]); - - const resetGame = () => { - const { rows, cols, mines } = DIFFICULTIES[difficulty]; - setBoard(createBoard(rows, cols, mines)); - setGameOver(false); - setGameWon(false); - setFlagsPlaced(0); - setTime(0); - setTimerActive(false); - }; - - const revealCell = (row, col) => { - if (gameOver || gameWon || board[row][col].isRevealed || board[row][col].isFlagged) return; - - if (!timerActive) { - setTimerActive(true); - // Ensure first click is safe - if (board[row][col].isMine) { - const newBoard = [...board]; - // Remove mine from current cell - newBoard[row][col].isMine = false; - - // Find a new spot for the mine - let placed = false; - while (!placed) { - const newRow = Math.floor(Math.random() * board.length); - const newCol = Math.floor(Math.random() * board[0].length); - if (!newBoard[newRow][newCol].isMine && (newRow !== row || newCol !== col)) { - newBoard[newRow][newCol].isMine = true; - placed = true; - } - } - - // Recalculate neighbor counts - for (let r = 0; r < board.length; r++) { - for (let c = 0; c < board[0].length; c++) { - if (!newBoard[r][c].isMine) { - let count = 0; - for (let i = -1; i <= 1; i++) { - for (let j = -1; j <= 1; j++) { - if (r + i >= 0 && r + i < board.length && c + j >= 0 && c + j < board[0].length) { - if (newBoard[r + i][c + j].isMine) count++; - } - } - } - newBoard[r][c].neighborMines = count; - } - } - } - setBoard(newBoard); - } - } - - const newBoard = [...board]; - - if (newBoard[row][col].isMine) { - // Game Over - reveal all mines - newBoard.forEach(row => row.forEach(cell => { - if (cell.isMine) cell.isRevealed = true; - })); - setGameOver(true); - } else { - // Reveal clicked cell and neighbors if empty - const revealNeighbors = (r, c) => { - if (r < 0 || r >= board.length || c < 0 || c >= board[0].length) return; - if (newBoard[r][c].isRevealed || newBoard[r][c].isFlagged) return; - - newBoard[r][c].isRevealed = true; - - if (newBoard[r][c].neighborMines === 0) { - for (let i = -1; i <= 1; i++) { - for (let j = -1; j <= 1; j++) { - revealNeighbors(r + i, c + j); - } - } - } - }; - - revealNeighbors(row, col); - } - - setBoard(newBoard); - - // Check for win - const unrevealedCount = newBoard.flat().filter(cell => !cell.isRevealed).length; - if (unrevealedCount === DIFFICULTIES[difficulty].mines) { - setGameWon(true); - } - }; - - const toggleFlag = (row, col, e) => { - e.preventDefault(); - if (gameOver || gameWon || board[row][col].isRevealed) return; - - const newBoard = [...board]; - const cell = newBoard[row][col]; - - if (!cell.isFlagged && flagsPlaced >= DIFFICULTIES[difficulty].mines) return; - - cell.isFlagged = !cell.isFlagged; - setFlagsPlaced(prev => cell.isFlagged ? prev + 1 : prev - 1); - setBoard(newBoard); - }; - - const renderCell = (cell, row, col) => { - let content = ''; - let className = styles.cell; - - if (cell.isRevealed) { - className += ` ${styles.revealed}`; - if (cell.isMine) { - content = '💣'; - className += ` ${styles.mine}`; - } else if (cell.neighborMines > 0) { - content = cell.neighborMines; - className += ` ${styles[`number-${cell.neighborMines}`]}`; - } - } else if (cell.isFlagged) { - content = '🚩'; - className += ` ${styles.flag}`; - } - - return ( - - ); - }; - - return ( - -
-
-

Minesweeper

-
- -
-
- {Object.keys(DIFFICULTIES).map(mode => ( - - ))} - -
- -
- 💣 {DIFFICULTIES[difficulty].mines - flagsPlaced} - ⏱️ {String(Math.floor(time / 60)).padStart(2, '0')}:{String(time % 60).padStart(2, '0')} - - {gameOver ? '💥 Game Over!' : gameWon ? '🎉 You Win!' : 'Playing...'} - -
- -
- {board.map((row, rowIndex) => - row.map((cell, colIndex) => renderCell(cell, rowIndex, colIndex)) - )} -
-
-
- - ); -}; - -export default MinesweeperGame; \ No newline at end of file diff --git a/src/pages/minesweeper/styles.module.css b/src/pages/minesweeper/styles.module.css deleted file mode 100644 index 0f3245f..0000000 --- a/src/pages/minesweeper/styles.module.css +++ /dev/null @@ -1,259 +0,0 @@ -.container { - display: flex; - flex-direction: column; - align-items: center; - min-height: 100vh; - padding: 2rem; - background: linear-gradient(45deg, #2c3e50, #3498db); -} - -.gameContainer { - background: rgba(255, 255, 255, 0.95); - backdrop-filter: blur(10px); - border-radius: 12px; - box-shadow: 0 8px 32px rgba(0, 0, 0, 0.1); - padding: 2rem; - margin-top: 2rem; - position: relative; -} - -.controls { - display: flex; - gap: 1rem; - margin-bottom: 2rem; - justify-content: center; - position: sticky; - left: 0; - right: 0; - z-index: 1; -} - -.gameStatus { - display: flex; - justify-content: space-between; - align-items: center; - background: rgba(52, 73, 94, 0.8); - backdrop-filter: blur(10px); - padding: 0.5rem 1rem; - border-radius: 12px; - color: #fff; - margin-bottom: 1rem; - font-family: 'Courier New', monospace; - font-size: 0.9rem; - box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); - border: 1px solid rgba(255, 255, 255, 0.1); - position: sticky; - left: 0; - right: 0; - z-index: 1; -} - -.header { - text-align: center; - margin-bottom: 2rem; -} - -.title { - font-size: 2.5rem; - color: #fff; - margin-bottom: 1rem; - text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2); -} - -.controls { - display: flex; - gap: 1rem; - margin-bottom: 2rem; - justify-content: center; -} - -.button { - padding: 0.5rem 1rem; - border: none; - border-radius: 6px; - font-size: 1rem; - cursor: pointer; - transition: all 0.3s ease; - background: #fff; - color: #2c3e50; -} - -@media (max-width: 480px) { - .button { - padding: 0.4rem 0.6rem; - font-size: 0.9rem; - } - - .controls { - gap: 0.5rem; - } -} - -.button:hover { - transform: translateY(-2px); - box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); -} - -.button.active { - background: #3498db; - color: #fff; -} - -.button:last-child { - background: linear-gradient(45deg, #ff6b6b, #ff8e53); - color: white; - font-weight: bold; - text-transform: uppercase; - letter-spacing: 1px; - padding: 0.6rem 1.2rem; - border-radius: 8px; - box-shadow: 0 4px 15px rgba(255, 107, 107, 0.3); - border: 1px solid rgba(255, 255, 255, 0.1); -} - -.button:last-child:hover { - transform: translateY(-3px); - box-shadow: 0 6px 20px rgba(255, 107, 107, 0.4); - background: linear-gradient(45deg, #ff8e53, #ff6b6b); -} - -.button:last-child:active { - transform: translateY(-1px); - box-shadow: 0 2px 10px rgba(255, 107, 107, 0.3); -} - -.gameStatus { - display: flex; - justify-content: space-between; - align-items: center; - background: rgba(52, 73, 94, 0.8); - backdrop-filter: blur(10px); - padding: 0.5rem 1rem; - border-radius: 12px; - color: #fff; - margin-bottom: 1rem; - font-family: 'Courier New', monospace; - font-size: 0.9rem; - box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); - border: 1px solid rgba(255, 255, 255, 0.1); - position: relative; -} - -.gameStatus > * { - display: flex; - align-items: center; - gap: 0.4rem; - padding: 0.2rem 0.6rem; - font-size: 0.9rem; -} - -.gameStatus > * { - display: flex; - align-items: center; - gap: 0.5rem; -} - -.grid { - display: grid; - gap: 1px; - background: #34495e; - padding: 1px; - border-radius: 4px; - width: fit-content; - margin: 0 auto; - grid-template-columns: repeat(auto-fill, 30px); - overflow-x: auto; - max-width: none; - -webkit-overflow-scrolling: touch; - scrollbar-width: thin; - scrollbar-color: rgba(52, 73, 94, 0.8) transparent; -} - -.grid::-webkit-scrollbar { - height: 8px; -} - -.grid::-webkit-scrollbar-track { - background: transparent; -} - -.grid::-webkit-scrollbar-thumb { - background-color: rgba(52, 73, 94, 0.8); - border-radius: 4px; -} - -.cell { - width: 30px; - height: 30px; - display: flex; - justify-content: center; - align-items: center; - background: #ffffff; - border: none; - font-weight: bold; - font-size: 1rem; - cursor: pointer; - transition: all 0.2s ease; - flex-shrink: 0; -} - -.cell:hover { - background: #e2e8f0; -} - -.cell.revealed { - background: #e2e2e2; -} - -.cell.mine { - background: #ff4444; - animation: explode 0.5s ease-out; -} - -@keyframes explode { - 0% { - transform: scale(0.8); - opacity: 0; - background: #ffeb3b; - } - 50% { - transform: scale(1.2); - opacity: 1; - background: #ff9800; - } - 100% { - transform: scale(1); - background: #ff4444; - } -} - -.cell.flag { - background: #ffd700; -} - -.number-1 { color: #3498db; } -.number-2 { color: #2ecc71; } -.number-3 { color: #e74c3c; } -.number-4 { color: #8e44ad; } -.number-5 { color: #c0392b; } -.number-6 { color: #16a085; } -.number-7 { color: #2c3e50; } -.number-8 { color: #7f8c8d; } - -@media (max-width: 768px) { - .container { - padding: 1rem; - } - - .gameContainer { - padding: 1rem; - width: 100%; - overflow-x: auto; - } - - .grid { - margin: 0 auto; - padding: 1px; - border-radius: 4px; - } -} \ No newline at end of file diff --git a/new-site/src/pages/projects.astro b/src/pages/projects.astro similarity index 100% rename from new-site/src/pages/projects.astro rename to src/pages/projects.astro diff --git a/src/pages/projects/index.js b/src/pages/projects/index.js deleted file mode 100644 index 38323eb..0000000 --- a/src/pages/projects/index.js +++ /dev/null @@ -1,231 +0,0 @@ -import React, { useEffect, useRef, useState } from "react"; -import { useLocation } from "react-router-dom"; -import clsx from "clsx"; - -import Link from "@docusaurus/Link"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import useDocusaurusContext from "@docusaurus/useDocusaurusContext"; -import Layout from "@theme/Layout"; - -import { FontAwesomeIcon } from "@fortawesome/react-fontawesome"; -import { - faCalendar, - faCode, - faFile, - faGamepad, - faLink, - faTools, - faUsers, -} from "@fortawesome/free-solid-svg-icons"; -import { faChrome } from "@fortawesome/free-brands-svg-icons"; - -import styles from "./styles.module.css"; -import projects from "../../data/_Projects"; - -function CategoryIcon({ category, size = "1x" }) { - let faIcon; - switch (category) { - case "Project": - faIcon = faFile; - break; - case "Open Source Tool": - faIcon = faTools; - break; - case "Website": - faIcon = faChrome; - break; - case "Game": - faIcon = faGamepad; - break; - default: - faIcon = faFile; - } - - return ; -} - -export function ProjectListings() { - return ( -
- {projects.map((project) => ( -
- - {project.imageUrl ? ( -
- {project.title} -
- ) : ( -
-

{project.title}

-
- )} -
-

{project.title}

-

{project.period}

-

{project.subtitle}

-

Read more

-
- -
- ))} -
- ); -} - -export function ProjectListing(props) { - const projectItem = props.projectItem; - return ( -
- - - -

{projectItem.title}

-

{projectItem.subtitle}

- {projectItem.imageUrl && ( - {projectItem.title} - )} -
-
    -
  • - {" "} - {projectItem.category} -
    • -
  • - {" "} - {projectItem.period} -
    • -
  • - {projectItem.tech} -
    • - {projectItem.team && ( -
  • - {" "} - {projectItem.team.map((member, i) => ( - - {member.link && {member.name}} - {!member.link && member.name} - {i < projectItem.team.length - 1 ? ", " : ""} - - ))} -
    • - )} -
- Description -
{projectItem.description}
- {projectItem.links && ( - <> - Links - - - )} -
- - - -
- ); -} - -function FollowUp() { - return ( -
-
-

Continue exploring?

- -
-
- ); -} - -export default function Projects() { - const context = useDocusaurusContext(); - const { siteConfig = {} } = context; - - const [loaded, setLoaded] = useState(false); - const [showProjectItem, setShowProjectItem] = useState(false); - const [projectItem, setProjectItem] = useState(projects[0]); - const slug = useLocation(); - - useEffect(() => { - function handleTransition() { - let foundProject; - if (slug.hash) { - foundProject = projects.find((project) => project.slug == slug.hash); - } - - if (foundProject) { - setProjectItem(foundProject); - setShowProjectItem(true); - window.scrollTo(0, 0); - } else { - setShowProjectItem(false); - } - - setLoaded(true); - } - - handleTransition(); - }); - - return ( - -
-

- My projects -

-
- {loaded && ( -
-
-
- {!showProjectItem && } - {showProjectItem && } -
-
- -
- )} - - ); -} diff --git a/src/pages/projects/styles.module.css b/src/pages/projects/styles.module.css deleted file mode 100644 index 10656b8..0000000 --- a/src/pages/projects/styles.module.css +++ /dev/null @@ -1,66 +0,0 @@ -/* stylelint-disable docusaurus/copyright-header */ -.projectPageHeader { - background-color: var(--ifm-color-secondary-darker); - color: var(--ifm-color-secondary-lighter); - display: block; - overflow: hidden; - padding: 1rem 0; - position: relative; - text-align: center; -} - -.projectPageHeader h2 { - display: inline-block; - padding-bottom: 6px; -} - -.projectItem { - margin: 0 auto; - max-width: 768px; - text-align: left; -} - -.projectItem h1 { - font-size: 2.4rem; -} - -.projectItem h2 { - font-size: 1rem; - font-weight: normal; -} - -.projectItem ul { - list-style: none outside; - margin: 0; - padding: 8px; - text-align: left; -} - -.projectItem li { - margin-bottom: 8px; -} - -.projectItem svg { - margin-right: 4px; - max-width: 12px; -} - -.projectItemButton { - display: flex; - margin: 0; - justify-content: center; -} - - -.directory { - background-color: var(--ifm-color-secondary-darker); - color: var(--ifm-color-secondary-lightest); - padding: 3.6rem 0.4rem; - width: 100%; -} - -@media screen and (max-width: 966px) { - .projectItemBackButton { - font-size: 1rem; - } -} diff --git a/new-site/src/pages/rss.xml.ts b/src/pages/rss.xml.ts similarity index 100% rename from new-site/src/pages/rss.xml.ts rename to src/pages/rss.xml.ts diff --git a/src/pages/styles.module.css b/src/pages/styles.module.css deleted file mode 100644 index 117d806..0000000 --- a/src/pages/styles.module.css +++ /dev/null @@ -1,150 +0,0 @@ -/* stylelint-disable docusaurus/copyright-header */ - -.heroBanner { - background: linear-gradient(135deg, #F9FAFB 0%, #ECFDF5 100%); - color: var(--ifm-color-secondary-darkest); - overflow: hidden; - display: flex; - justify-content: center; - align-items: center; - padding: 3.6rem 1.2rem; - position: relative; -} - -.heroBanner::before { - content: ''; - position: absolute; - top: 0; - left: 0; - right: 0; - bottom: 0; - background: url('data:image/svg+xml,') center/cover; - pointer-events: none; -} - -[data-theme='dark'] .heroBanner { - background: linear-gradient(135deg, #0F1419 0%, #1C2128 100%); - color: var(--ifm-color-secondary-lightest); -} - -.heroBannerWrapper { - min-height: 1024px; - text-align: center; - position: relative; - z-index: 1; -} - -.heroBanner h2 { - font-size: 80px !important; - line-height: 5.4rem; - font-weight: 600; -} - -@keyframes gradient { - 0% { - background-position: 0% 50%; - } - 50% { - background-position: 100% 50%; - } - 100% { - background-position: 0% 50%; - } -} - -.behitek { - background: linear-gradient(90deg, #f12711, #f5af19, #f5af19, #f12711); - background-size: 400% 400%; - animation: gradient 5s ease infinite; - background-clip: text; - -webkit-background-clip: text; - font-weight: 500; - color: transparent; -} - -.heroBanner p { - font-size: 20px; - letter-spacing: 0.5rem; - text-transform: uppercase; - font-weight: 300; - margin: 0 4px; - - /* max-width: 512px; */ -} - -.aboutHeader { - background-color: var(--ifm-background-color); - color: var(--ifm-color-secondary-darkest); - padding: 3.6rem 0 1rem; - text-align: center; - position: relative; - overflow: hidden; - display: block; - border-bottom: 1px solid var(--ifm-color-secondary-lightest); -} - -[data-theme='dark'] .aboutHeader { - background-color: var(--ifm-color-secondary-darker); - color: var(--ifm-color-secondary-lighter); -} - -.aboutHeader h2 { - display: inline-block; - padding-bottom: 6px; -} - -.about { - background-color: var(--ifm-background-color); - color: var(--ifm-color-secondary-darkest); - display: flex; - flex-direction: row; - justify-content: center; - align-items: center; - gap: 2.4rem; - padding: 3.6rem 1.2rem; -} - -[data-theme='dark'] .about { - background-color: var(--ifm-color-secondary-lightest); - color: var(--ifm-color-secondary-darker); -} - -.aboutProfilePic { - -webkit-mask-image: url('/img/mark-avatar.svg'); - mask-image: url('/img/mark-avatar.svg'); - -webkit-mask-size: auto; - mask-size: auto; - -webkit-mask-repeat: no-repeat; - mask-repeat: no-repeat; - -webkit-mask-position: center; - mask-position: center; - max-height: 450px; - max-width: 450px; -} - -.aboutText { - max-width: 512px; -} - -.aboutText a { - font-weight: bold; -} - -.directory { - background-color: var(--ifm-footer-background-color); - color: var(--ifm-color-secondary-darkest); - padding: 3.6rem 0.4rem; - width: 100%; - border-top: 1px solid var(--ifm-color-secondary-lightest); -} - -[data-theme='dark'] .directory { - background-color: var(--ifm-color-secondary-darker); - color: var(--ifm-color-secondary-lightest); -} - -@media screen and (max-width: 966px) { - .about { - flex-direction: column; - } -} diff --git a/new-site/src/pages/vi/blog/index.astro b/src/pages/vi/blog/index.astro similarity index 100% rename from new-site/src/pages/vi/blog/index.astro rename to src/pages/vi/blog/index.astro diff --git a/new-site/src/pages/vi/contact.astro b/src/pages/vi/contact.astro similarity index 100% rename from new-site/src/pages/vi/contact.astro rename to src/pages/vi/contact.astro diff --git a/new-site/src/pages/vi/index.astro b/src/pages/vi/index.astro similarity index 100% rename from new-site/src/pages/vi/index.astro rename to src/pages/vi/index.astro diff --git a/new-site/src/pages/vi/projects.astro b/src/pages/vi/projects.astro similarity index 100% rename from new-site/src/pages/vi/projects.astro rename to src/pages/vi/projects.astro diff --git a/new-site/src/styles/global.css b/src/styles/global.css similarity index 100% rename from new-site/src/styles/global.css rename to src/styles/global.css diff --git a/src/theme/BlogPostItem/index.js b/src/theme/BlogPostItem/index.js deleted file mode 100644 index 28c362d..0000000 --- a/src/theme/BlogPostItem/index.js +++ /dev/null @@ -1,27 +0,0 @@ -import { useBlogPost } from '@docusaurus/plugin-content-blog/client'; -import BlogPostItem from '@theme-original/BlogPostItem'; -import { DiscussionEmbed } from 'disqus-react'; -import React from 'react'; - -export default function BlogPostItemWrapper(props) { - const { metadata: { frontMatter }, isBlogPostPage } = useBlogPost(); - const { comments = true, slug, title } = frontMatter; - - return ( - <> - - {comments && isBlogPostPage && ( - - )} - - ); -} \ No newline at end of file diff --git a/new-site/src/utils/constants.ts b/src/utils/constants.ts similarity index 100% rename from new-site/src/utils/constants.ts rename to src/utils/constants.ts diff --git a/new-site/src/utils/helpers.ts b/src/utils/helpers.ts similarity index 100% rename from new-site/src/utils/helpers.ts rename to src/utils/helpers.ts diff --git a/static/.nojekyll b/static/.nojekyll deleted file mode 100644 index e69de29..0000000 diff --git a/static/CNAME b/static/CNAME deleted file mode 100644 index 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