English tutorial instructions

💾 AI Summary (ChatGPT)

This project enhances MkDocs documentation with AI-driven features like automatic summary generation and intelligent reading statistics. It supports multiple AI services, multi-language summaries, content cleaning, and fallback mechanisms. The tool provides flexible configuration options and intelligent caching. Reading statistics include character counting, code detection, and reading time estimation. Smart features adapt to different environments, recognize languages, and detect content types. Installation involves direct download or Git clone methods. Configuration includes setting up hooks, local configurations, and choosing AI services.

中文 | English

🚀 Supercharge Your MkDocs Documentation with AI!
This project provides powerful MkDocs hooks that add AI-driven summary generation and intelligent reading statistics to your technical documentation and blogs.

Preview 2

🌐 Live Demo: https://wcowin.work/Mkdocs-AI-Summary/

✨ Features¶

🤖 AI Smart Summary¶

Multi-AI Service Integration: Support for DeepSeek, OpenAI, Claude, Gemini and other mainstream AI services
Automatic Summary Generation: Generate high-quality 80-120 word intelligent summaries
Multi-Language Support: Support for Chinese, English, and bilingual summary generation
Intelligent Content Cleaning: Automatically filter YAML frontmatter, HTML tags, code blocks and other formatting elements
Fallback Summary Mechanism: Provides keyword-based local summaries when AI services are unavailable
Intelligent Caching System: 7-day intelligent expiration, avoiding duplicate API calls
Flexible Configuration: Support precise control at both folder and page levels

📊 Intelligent Reading Statistics (Optional)¶

Accurate Character Counting: Specially optimized for Chinese and English content recognition
Smart Code Detection: Recognizes 30+ programming languages and command-line code
Reading Time Estimation: Intelligent calculation based on language characteristics (Chinese: 400 chars/min, English: 200 words/min)
Beautiful Information Display: Uses MkDocs Material theme styled info boxes

🚀 Smart Features¶

Environment Adaptive: Automatically recognizes CI/local environment, locally or deployment both configurable enable/disable
Automatic Language Recognition: Supports 30+ programming and markup languages
Content Type Detection: Distinguishes between code, configuration, command-line and other content types
LRU Cache Optimization: Improves processing performance (Todo)
Comprehensive Error Handling: Exception handling and logging (Todo)

📦 Quick Installation¶

Method 1: Direct Download (Recommended)¶

Step 1: Download Files - Download the latest version from Releases page - Or directly download ai_summary.py file

Step 2: Create Directory and Place Files

# Execute in your MkDocs project root directory
mkdir -p docs/overrides/hooks/
mv ai_summary.py docs/overrides/hooks/

Step 3: Configure MkDocs Theme and Override Path

# Add to mkdocs.yml
theme:
  name: material
  custom_dir: docs/overrides  # Required configuration!!!
  features:
    - content.code.copy
    - content.code.select

Method 2: Git Clone¶

git clone https://github.com/Wcowin/mkdocs-ai-hooks.git
cd mkdocs-ai-hooks 
pip install -r requirements.txt

Dependencies Installation¶

pip install -r requirements.txt

🚀 Quick Start¶

1. Basic Configuration¶

Step 1: Configure Hooks Make sure ai_summary.py is placed in the docs/overrides/hooks directory, then:

# Add to mkdocs.yml
hooks:
  - docs/overrides/hooks/ai_summary.py      # AI summary hook

Step 2: Local Configuration Create a .env file in the root directory to store API keys (remember to add to .gitignore):

# .env file content
DEEPSEEK_API_KEY=your_deepseek_api_key_here
OPENAI_API_KEY=your_openai_api_key_here

# .gitignore file content
# Environment variable files (sensitive information)
.env
.env.local
.env.*.local
*.key

# MkDocs build output directory
site/

# AI summary cache directory (project root) - needs to be committed
!.ai_cache/

Check the directory tree structure here:

$ tree -a
Project Name
├── .github
│   ├── .DS_Store
│   └── workflows
│       └── ci.yml
├── docs
│   └── index.md
|   └── overrides
│       └── hooks
│           └── ai_summary.py
├── .env
├── .gitignore
├── README.md
└── mkdocs.yml

2. Configure AI Service¶

Choose AI Service Provider:

🌟 DeepSeek (Recommended): High cost-performance ratio, excellent Chinese performance
🔥 OpenAI: Powerful features, wide support
⚡ Claude: Clear logic, excellent text understanding
🧠 Gemini: Google's product, multi-language support

Get API Keys:

DeepSeek - Register to get API key
ChatAnywhere - Free OpenAI quota

Store the obtained API keys in the .env file created in the previous step!!!

3. Set Parameters¶

Configure directories that need AI summaries in ai_summary.py:

# 📂 Folders to enable AI summaries
self.enabled_folders = [
    'blog/',      # Blog articles
    # Add more folders...
]

4. Local Run and Test¶

mkdocs serve  # Local preview

5. Deployment Configuration¶

# ci.yml
name: ci 
on:
  push:
    branches:
      - master 
      - main
  # Prevent access to secrets from fork repositories
  pull_request:
    types: [closed]
    branches: [main, master]
permissions:
  contents: write
jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
          sparse-checkout: |
            docs
            includes
            requirements.txt
            .ai_cache
      - uses: actions/setup-python@v4
        with:
          python-version: 3.x
      - name: Set cache ID
        run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV 
      - uses: actions/cache@v3
        with:
          key: mkdocs-material-${{ github.run_number }}
          path: .cache
          restore-keys: |
            mkdocs-material-
      - run: pip install mkdocs-git-revision-date-localized-plugin
      - run: pip install mkdocs-git-authors-plugin
      - run: pip install mkdocs-git-committers-plugin-2
      - run: pip install markdown-callouts
      - run: pip install mkdocs-rss-plugin
      - run: pip install requests>=2.25.0
      - run: pip install python-dateutil>=2.8.0
      - run: pip install cachetools>=4.2.0
      - run: pip install python-dotenv>=0.19.0
      - run: pip install pymdown-extensions
      - run: pip install mkdocs-material 
      - run: pip install --upgrade --force-reinstall mkdocs-material
      - name: Deploy with AI Summary
        env:
          # AI summary switch control
          AI_SUMMARY_CI_ENABLED: 'true'           # Enable AI summary in CI deployment environment (true=generate AI summaries for articles in CI)
          AI_SUMMARY_CI_ONLY_CACHE: 'true'       # CI deployment does not generate new summaries (true=use local deployment summary cache, no repeated API calls)
          AI_SUMMARY_CI_FALLBACK: 'true'          # Enable fallback summary in CI deployment (true=generate offline basic summary when API fails)
          # AI_SUMMARY_LOCAL_ENABLED: 'false'       # Disable AI summary in local deployment environment (true=generate summaries during local development) (no need to manage this line)
          # AI_SUMMARY_CACHE_ENABLED: 'true'        # Enable cache function locally (true=cache summaries to avoid repeated generation) (no need to manage this line)
          # API key configuration
          DEEPSEEK_API_KEY: ${{ secrets.DEEPSEEK_API_KEY }}
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: mkdocs gh-deploy --force

      # Auto-commit newly generated AI cache files
      - name: Auto-commit AI cache (if any new files)
        run: |
          if [ -d ".ai_cache" ] && [ "$(ls -A .ai_cache 2>/dev/null)" ]; then
            git config --local user.email "action@github.com"
            git config --local user.name "GitHub Action"
            git add .ai_cache/
            if ! git diff --cached --quiet; then
              git commit -m "🤖 Auto-update AI summary cache [skip ci]"
              git push
              echo "✅ Auto-committed new AI cache files"
            else
              echo "ℹ️ No new cache files to commit"
            fi
          else
            echo "ℹ️ No cache directory found or cache is empty"
          fi

# Configuration in ai_summary.py
# AI summary local environment configuration
self.ci_config = {
    # CI deployment environment switch (no need to manage, only effective when set in ci.yml)
    'enabled_in_ci': os.getenv('AI_SUMMARY_CI_ENABLED', 'true').lower() == 'true',

    # Local deployment environment switch (true=enable AI summary during local development)
    'enabled_in_local': os.getenv('AI_SUMMARY_LOCAL_ENABLED', 'true').lower() == 'true',

    # CI deployment cache-only mode (no need to manage, only effective when set in ci.yml)
    'ci_only_cache': os.getenv('AI_SUMMARY_CI_ONLY_CACHE', 'false').lower() == 'true',

    # Local deployment cache function switch (true=enable cache to avoid repeated generation, false=always generate new summaries)
    'cache_enabled': os.getenv('AI_SUMMARY_CACHE_ENABLED', 'true').lower() == 'true',

    # CI deployment fallback summary switch (no need to manage, only effective when set in ci.yml)
    'ci_fallback_enabled': os.getenv('AI_SUMMARY_CI_FALLBACK', 'true').lower() == 'true',
}

Several Operation Modes:
1. Completely Disabled: No summary generation in both local and CI deployment
2. CI Deployment Only: Disabled locally, generate new summaries in CI deployment
3. Cache Mode: Summaries already generated locally, CI deployment uses cache (Recommended. The above configuration has default CI deployment cache mode, you can choose combinations yourself)
4. Fully Enabled: Both local and CI deployment run (more API consumption)

6. GitHub Secrets Configuration¶

Step 1: Set Repository Secrets
1. Go to GitHub repository → Settings → Secrets and variables → Actions
2. Click New repository secret to add:

1 2	`DEEPSEEK_API_KEY=your_deepseek_api_key_here OPENAI_API_KEY=your_openai_api_key_here`

Then deploy to GitHub Pages or other platforms.

If there are errors, you can ask ChatGPT or ask questions in Issues.

📖 Usage Guide¶

AI Summary Control¶

Method 1: Page-Level Control (Recommended)¶

In the YAML meta at the top of Markdown files:

Enable AI Summary:

---
title: Article Title
ai_summary: true   # Enable AI summary
---

Disable AI Summary:

---
title: Article Title
ai_summary: false  # Disable AI summary
description: Custom summary content  # Optional manual summary
---

Method 2: Folder-Level Control¶

# Configure in ai_summary.py
# 📂 Customizable folder configuration
self.enabled_folders = [
    'blog/',      # blog folder
    'index.md',     
    # 'develop/',   # develop folder
    # 'posts/',     # posts folder
    # 'trip/',     # trip folder
    # 'about/',     # about folder
]

# 📋 Excluded files and folders
self.exclude_patterns = [
    '404.md', 'tag.md', 'tags.md',
]

# 📋 Excluded specific files
self.exclude_files = [
    'blog/index.md',
]

🎨 Display Examples¶

AI Summary Display¶

Live Preview: AI Summary Display

💰 Cost Information¶

Single Request: Approximately $0.005-0.008 USD (medium to large documents)
Monthly Estimate: About $1-5 USD for regular blogs
Free Quota: Most AI service providers offer free credits for new users

⚙️ Advanced Configuration¶

Custom AI Service¶

# Add new AI service
self.ai_services = {
    'your_service': {
        'url': 'https://api.yourservice.com/v1/chat/completions',
        'model': 'your-model',
        'api_key': os.getenv('YOUR_API_KEY'),
        'max_tokens': 150,
        'temperature': 0.3
    }
}

# Default AI service to use
self.default_service = 'your_service'

# Service priority (try in order)
self.service_fallback_order = ['openai', 'deepseek', 'claude', 'gemini'] # Try in order

Custom Prompts¶

def generate_ai_summary(self, content, page_title=""):
    prompt = f"""Please generate a concise English summary (80-120 words) for the following technical document:

    Article title: {page_title}
    Article content: {content[:2500]}

    Requirements:
    1. Highlight core technical points
    2. Use concise professional language
    3. Keep length between 80-120 words
    """

Cache Configuration¶

# Modify cache expiration time
cache_time = datetime.fromisoformat(cache_data.get('timestamp', '1970-01-01'))
if (datetime.now() - cache_time).days < 30:  # Change to 30 days
    return cache_data

🌍 Multi-Language Support¶

Language Configuration¶

# Set in ai_summary.py
self.summary_language = 'en'    # English summaries
# self.summary_language = 'zh'  # Chinese summaries
# self.summary_language = 'both' # Bilingual summaries

Supported Languages¶

Fully Supported: Chinese, English
Partially Supported: Japanese, Korean, French, German

📊 Performance Optimization¶

Implemented Optimizations¶

LRU Caching: Function-level caching improves performance
Precompiled Regex: Faster text processing
Smart Filtering: Reduces unnecessary API calls
Content Hashing: Intelligent caching based on content changes

Performance Recommendations¶

Use ci_only_cache: true to only use cache in CI environment
Set enabled_folders appropriately to avoid processing unnecessary files
Regularly clean expired cache files

🤝 Contributing Guide¶

How to Contribute¶

Fork this repository
Create feature branch
Commit changes
Push branch
Create Pull Request

Development Environment¶

git clone https://github.com/Wcowin/mkdocs-ai-hooks.git
cd mkdocs-ai-hooks
pip install -r requirements.txt

📝 Changelog¶

[v1.3.0] (2025-06-04) - Latest Version¶

Core Improvements¶

Unified Cache Architecture
Cache path unified to project root directory .ai_cache
Local and CI environments use the same cache strategy
Enhanced CI/CD support, Support CI cache-only mode, significantly reducing deployment time
Smart recognition of 15+ deployment platforms (GitHub Actions, GitLab CI, etc.)
Configurable fallback summary mechanism

[v1.2.0] (2025-06-03)¶

✨ Major New Features¶

Multi-AI Service Support: Integration of DeepSeek, OpenAI, Gemini, Claude
Environment Adaptive: Automatic recognition of CI/local environment
Intelligent Caching System: Content hash caching, 7-day automatic expiration
Security Configuration: GitHub Secrets integration, secure API key management

🔧 Technical Improvements¶

Unified API Interface: Automatic adaptation to different AI service formats
Enhanced Error Handling: Comprehensive exception handling mechanisms
Performance Optimization: LRU caching and regex precompilation

[v1.0.0] (2025-06-01) - Initial Version¶

🤖 AI smart summary functionality
📖 Reading time statistics functionality
💾 Basic caching system
🎯 Basic configuration options

🐛 Issue Reporting¶

Encountered a problem? Please report it in Issues.

When reporting, please include: - MkDocs version - Python version - Complete error messages - Reproduction steps - Configuration files (remove sensitive information)