# ai.log

AI-powered static blog generator with ATProto integration, part of the ai.ai ecosystem.

## 🎯 Gitea Action Usage

Use ailog in your Gitea Actions workflow:

```yaml
name: Deploy Blog
on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4
    - uses: ai/log@v1
      with:
        content-dir: 'content'
        output-dir: 'public'
        ai-integration: true
        atproto-integration: true
    - uses: cloudflare/pages-action@v1
      with:
        apiToken: ${{ secrets.CLOUDFLARE_API_TOKEN }}
        accountId: ${{ secrets.CLOUDFLARE_ACCOUNT_ID }}
        projectName: my-blog
        directory: public
```

## 🚀 Quick Start

### Development Setup

```bash
# 1. Clone and setup
git clone https://git.syui.ai/ai/log
cd log

# 2. Start development services
./run.zsh serve    # Blog development server
./run.zsh c        # Cloudflare tunnel (example.com)
./run.zsh o        # OAuth web server  
./run.zsh co       # Comment system monitor

# 3. Start Ollama (for Ask AI)
brew install ollama
ollama pull gemma2:2b
OLLAMA_ORIGINS="https://example.com" ollama serve
```

### Production Deployment

```bash
# 1. Build static site
hugo

# 2. Deploy to GitHub Pages
git add .
git commit -m "Update blog"
git push origin main

# 3. Automatic deployment via GitHub Actions
# Site available at: https://yourusername.github.io/repo-name
```

### ATProto Integration

```bash
# 1. OAuth Client Setup (oauth/client-metadata.json)
{
  "client_id": "https://example.com/client-metadata.json",
  "client_name": "ai.log Blog System",
  "redirect_uris": ["https://example.com/oauth/callback"],
  "scope": "atproto",
  "grant_types": ["authorization_code", "refresh_token"],
  "response_types": ["code"],
  "application_type": "web",
  "dpop_bound_access_tokens": true
}

# 2. Comment System Configuration
# Collection: ai.syui.log (comments)
# User Management: ai.syui.log.user (registered users)

# 3. Services
./run.zsh o     # OAuth authentication server
./run.zsh co    # ATProto Jetstream comment monitor
```

### Development with run.zsh

```bash
# Development
./run.zsh serve

# Production (with Cloudflare Tunnel)  
./run.zsh tunnel

# OAuth app development
./run.zsh o

# Comment system monitoring
./run.zsh co
```

## 📋 Commands

| Command | Description |
|---------|-------------|
| `./run.zsh c` | Enable Cloudflare tunnel (example.com) for OAuth |
| `./run.zsh o` | Start OAuth web server (port:4173 = example.com) |
| `./run.zsh co` | Start comment system (ATProto stream monitor) |

## 🏗️ Architecture (Pure Rust + HTML + JS)

```
ai.log/
├── oauth/                  # 🎯 OAuth files (protected)
│   ├── oauth-widget-simple.js  # Self-contained OAuth widget
│   ├── oauth-simple.html       # OAuth authentication page
│   ├── client-metadata.json    # ATProto configuration
│   └── README.md               # Usage guide
├── my-blog/                # Blog content and templates
│   ├── content/posts/      # Markdown blog posts
│   ├── templates/          # Tera templates
│   ├── static/             # Static assets (OAuth copied here)
│   └── public/             # Generated site (build output)
├── src/                    # Rust blog generator
├── scripts/                # Build and deployment scripts
└── run.zsh                 # 🎯 Main build script
```

### ✅ Node.js Dependencies Eliminated
- ❌ `package.json` - Removed
- ❌ `node_modules/` - Removed  
- ❌ `npm run build` - Not needed
- ✅ Pure JavaScript OAuth implementation
- ✅ CDN-free, self-contained code
- ✅ Rust-only build process

---

## 📖 Original Features

[![Rust](https://img.shields.io/badge/Rust-000000?style=for-the-badge&logo=rust&logoColor=white)](https://www.rust-lang.org/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## 概要

ai.logは、[Anthropic Docs](https://docs.anthropic.com/)にインスパイアされたモダンなインターフェースを持つ、次世代静的ブログジェネレーターです。ai.gptとの深い統合、ローカルAI機能、atproto OAuth連携により、従来のブログシステムを超えた体験を提供します。

## 主な特徴

### 🎨 モダンインターフェース
- **Anthropic Docs風デザイン**: プロフェッショナルで読みやすい
- **Timeline形式**: BlueskyライクなタイムラインUI  
- **自動TOC**: 右サイドバーに目次を自動生成
- **レスポンシブ**: モバイル・デスクトップ対応

### 🤖 Ask AI機能 ✅
- **ローカルAI**: Ollama(gemma2:2b)による質問応答
- **認証必須**: ATProto OAuth認証でアクセス制御
- **トップページ限定**: ブログコンテンツに特化した回答
- **CORS解決済み**: OLLAMA_ORIGINS設定でクロスオリジン問題解消
- **プロフィール連携**: AIアバターとしてATProtoプロフィール画像表示
- **レスポンス最適化**: 80文字制限+高いtemperatureで多様な回答
- **ローディング表示**: Font Awesomeアイコンによる一行ローディング

### 🔧 Ask AI設定方法
```bash
# 1. Ollama設定
brew install ollama
ollama pull gemma2:2b

# 2. CORS設定で起動
OLLAMA_ORIGINS="https://example.com" ollama serve

# 3. AI DID設定 (my-blog/templates/base.html)
const aiConfig = {
    systemPrompt: 'You are a helpful AI assistant.',
    aiDid: 'did:plc:your-ai-bot-did'
};
```

### 🌐 分散SNS連携
- **atproto OAuth**: Blueskyアカウントでログイン
- **コメントシステム**: 分散SNSコメント
- **データ主権**: ユーザーがデータを所有

### 🔗 エコシステム統合
- **ai.gpt**: ドキュメント同期・AI機能連携
- **MCP Server**: ai.gptからの操作をサポート
- **ai.wiki**: 自動ドキュメント同期

## Architecture

### Dual MCP Integration

**ai.log MCP Server (API Layer)**
- **Role**: Independent blog API
- **Port**: 8002
- **Location**: `./src/mcp/`
- **Function**: Core blog generation and management

**ai.gpt Integration (Server Layer)**
- **Role**: AI integration gateway
- **Port**: 8001 (within ai.gpt)
- **Location**: `../src/aigpt/mcp_server.py`
- **Function**: AI memory system + HTTP proxy to ai.log

### Data Flow
```
Claude Code → ai.gpt (Server/AI) → ai.log (API/Blog) → Static Site
              ↑                      ↑
              Memory System          File Operations
              Relationship AI        Markdown Processing
              Context Analysis       Template Rendering
```

## Features

- **Static Blog Generation**: Inspired by Zola, built with Rust
- **AI-Powered Content**: Memory-driven article generation via ai.gpt
- **🌍 Ollama Translation**: Multi-language markdown translation with structure preservation
- **atproto Integration**: OAuth authentication and comment system (planned)
- **MCP Integration**: Seamless Claude Code workflow

## Installation

```bash
cargo install ailog
```

## Usage

### Standalone Mode

```bash
# Initialize a new blog
ailog init myblog

# Create a new post
ailog new "My First Post"

# Build the blog
ailog build

# Serve locally
ailog serve

# Start MCP server
ailog mcp --port 8002

# Generate documentation
ailog doc readme --with-ai
ailog doc api --output ./docs
ailog doc structure --include-deps

# Translate documents (requires Ollama)
ailog doc translate --input README.md --target-lang en
ailog doc translate --input docs/api.md --target-lang ja --model qwen2.5:latest

# Clean build files
ailog clean
```

### AI Ecosystem Integration

When integrated with ai.gpt, use natural language:
- "ブログ記事を書いて" → Triggers `log_ai_content`
- "記事一覧を見せて" → Triggers `log_list_posts`
- "ブログをビルドして" → Triggers `log_build_blog`

### Documentation & Translation

Generate comprehensive documentation and translate content:
- "READMEを生成して" → Triggers `log_generate_docs`
- "APIドキュメントを作成して" → Generates API documentation
- "プロジェクト構造を解析して" → Creates structure documentation
- "このファイルを英語に翻訳して" → Triggers `log_translate_document`
- "マークダウンを日本語に変換して" → Uses Ollama for translation

## MCP Tools

### ai.log Server (Port 8002)
- `create_blog_post` - Create new blog post
- `list_blog_posts` - List existing posts
- `build_blog` - Build static site
- `get_post_content` - Get post by slug
- `translate_document` ⭐ - Ollama-powered markdown translation
- `generate_documentation` ⭐ - Code analysis and documentation generation

### ai.gpt Integration (Port 8001)
- `log_create_post` - Proxy to ai.log + error handling
- `log_list_posts` - Proxy to ai.log + formatting
- `log_build_blog` - Proxy to ai.log + AI features
- `log_get_post` - Proxy to ai.log + context
- `log_system_status` - Health check for ai.log
- `log_ai_content` ⭐ - AI memory → blog content generation
- `log_translate_document` 🌍 - Document translation via Ollama
- `log_generate_docs` 📚 - Documentation generation

### Documentation Generation Tools
- `doc readme` - Generate README.md from project analysis
- `doc api` - Generate API documentation
- `doc structure` - Analyze and document project structure
- `doc changelog` - Generate changelog from git history
- `doc translate` 🌍 - Multi-language document translation

### Translation Features
- **Language Support**: English, Japanese, Chinese, Korean, Spanish
- **Markdown Preservation**: Code blocks, links, images, tables maintained
- **Auto-Detection**: Automatically detects Japanese content
- **Ollama Integration**: Uses local AI models for privacy and cost-efficiency
- **Smart Processing**: Section-by-section translation with structure awareness

## Configuration

### ai.log Configuration
- Location: `~/.config/syui/ai/log/`
- Format: TOML configuration

### ai.gpt Integration
- Configuration: `../config.json`
- Auto-detection: ai.log tools enabled when `./log/` directory exists
- System prompt: Automatically triggers blog tools for related queries

## AI Integration Features

### Memory-Driven Content Generation
- **Source**: ai.gpt memory system
- **Process**: Contextual memories → AI analysis → Blog content
- **Output**: Structured markdown with personal insights

### Automatic Workflows
- Daily blog posts from accumulated memories
- Content enhancement and suggestions
- Related article recommendations
- Multi-language content generation

## atproto Integration (Planned)

### OAuth 2.0 Authentication
- Client metadata: `public/client-metadata.json`
- Comment system integration
- Data sovereignty: Users own their comments
- Collection storage in atproto

### Comment System
- **ATProto Stream Monitoring**: Real-time Jetstream connection monitoring
- **Collection Tracking**: Monitors `ai.syui.log` collection for new comments
- **User Management**: Automatically adds commenting users to `ai.syui.log.user` collection
- **Comment Display**: Fetches and displays comments from registered users
- **OAuth Integration**: atproto account login via Cloudflare tunnel
- **Distributed Storage**: Comments stored in user-owned atproto collections

## Build & Deploy

### GitHub Actions
```yaml
# .github/workflows/gh-pages.yml
- name: Build ai.log
  run: |
    cd log
    cargo build --release
    ./target/release/ailog build
```

### Cloudflare Pages
- Static output: `./public/`
- Automatic deployment on main branch push
- AI content generation during build process

## Development Status

### ✅ Completed Features
- Project structure and Cargo.toml setup
- CLI interface (init, new, build, serve, clean, mcp, doc)
- Configuration system with TOML support
- Markdown parsing with frontmatter support
- Template system with Handlebars
- Static site generation with posts and pages
- Development server with hot reload
- **MCP server integration (both layers)**
- **ai.gpt integration with 6 tools**
- **AI memory system connection**
- **📚 Documentation generation from code**
- **🔍 Rust project analysis and API extraction**
- **📝 README, API docs, and structure analysis**
- **🌍 Ollama-powered translation system**
- **🚀 Complete MCP integration with ai.gpt**
- **📄 Markdown-aware translation preserving structure**
- **💬 ATProto comment system with Jetstream monitoring**
- **🔄 Real-time comment collection and user management**
- **🔐 OAuth 2.1 integration with Cloudflare tunnel**
- **🤖 Ask AI feature with Ollama integration**
- **⚡ CORS resolution via OLLAMA_ORIGINS**
- **🔒 Authentication-gated AI chat**
- **📱 Top-page-only AI access pattern**
- Test blog with sample content and styling

### 🚧 In Progress
- AI-powered content enhancement pipeline
- Advanced comment moderation system

### 📋 Planned Features
- Advanced template customization
- Plugin system for extensibility
- Real-time comment system
- Multi-blog management
- VTuber integration (ai.verse connection)

## Integration with ai Ecosystem

### System Dependencies
- **ai.gpt**: Memory system, relationship tracking, AI provider
- **ai.card**: Future cross-system content sharing
- **ai.bot**: atproto posting and mention handling
- **ai.verse**: 3D world blog representation (future)

### yui System Compliance
- **Uniqueness**: Each blog post tied to individual identity
- **Reality Reflection**: Personal memories → digital content
- **Irreversibility**: Published content maintains historical integrity

## Getting Started

### 1. Standalone Usage
```bash
git clone [repository]
cd log
cargo run -- init my-blog
cargo run -- new "First Post"
cargo run -- build
cargo run -- serve
```

### 2. AI Ecosystem Integration
```bash
# Start ai.log MCP server
cargo run -- mcp --port 8002

# In another terminal, start ai.gpt
cd ../
# ai.gpt startup commands

# Use Claude Code with natural language blog commands
```

## Documentation Generation Features

### 📚 Automatic README Generation
```bash
# Generate README from project analysis
ailog doc readme --source ./src --with-ai

# Output: Enhanced README.md with:
# - Project overview and metrics
# - Dependency analysis
# - Module structure
# - AI-generated insights
```

### 📖 API Documentation
```bash
# Generate comprehensive API docs
ailog doc api --source ./src --format markdown --output ./docs

# Creates:
# - docs/api.md (main API overview)
# - docs/module_name.md (per-module documentation)
# - Function signatures and documentation
# - Struct/enum definitions
```

### 🏗️ Project Structure Analysis
```bash
# Analyze and document project structure
ailog doc structure --source . --include-deps

# Generates:
# - Directory tree visualization
# - File distribution by language
# - Dependency graph analysis
# - Code metrics and statistics
```

### 📝 Git Changelog Generation
```bash
# Generate changelog from git history
ailog doc changelog --from v1.0.0 --explain-changes

# Creates:
# - Structured changelog
# - Commit categorization
# - AI-enhanced change explanations
```

### 🤖 AI-Enhanced Documentation
When `--with-ai` is enabled:
- **Content Enhancement**: AI improves readability and adds insights
- **Context Awareness**: Leverages ai.gpt memory system
- **Smart Categorization**: Automatic organization of content
- **Technical Writing**: Professional documentation style

## 🌍 Translation System

### Ollama-Powered Translation

ai.log includes a comprehensive translation system powered by Ollama AI models:

```bash
# Basic translation
ailog doc translate --input README.md --target-lang en

# Advanced translation with custom settings
ailog doc translate \
  --input docs/technical-guide.ja.md \
  --target-lang en \
  --source-lang ja \
  --output docs/technical-guide.en.md \
  --model qwen2.5:latest \
  --ollama-endpoint http://localhost:11434
```

### Translation Features

#### 📄 Markdown-Aware Processing
- **Code Block Preservation**: All code snippets remain untranslated
- **Link Maintenance**: URLs and link structures preserved
- **Image Handling**: Alt text can be translated while preserving image paths
- **Table Translation**: Table content translated while maintaining structure
- **Header Preservation**: Markdown headers translated with level maintenance

#### 🎯 Smart Language Detection
- **Auto-Detection**: Automatically detects Japanese content using Unicode ranges
- **Manual Override**: Specify source language for precise control
- **Mixed Content**: Handles documents with multiple languages

#### 🔧 Flexible Configuration
- **Model Selection**: Choose from available Ollama models
- **Custom Endpoints**: Use different Ollama instances
- **Output Control**: Auto-generate or specify output paths
- **Batch Processing**: Process multiple files efficiently

### Supported Languages

| Language | Code | Direction | Model Optimized |
|----------|------|-----------|-----------------|
| English  | `en` | ↔️        | ✅ qwen2.5      |
| Japanese | `ja` | ↔️        | ✅ qwen2.5      |
| Chinese  | `zh` | ↔️        | ✅ qwen2.5      |
| Korean   | `ko` | ↔️        | ⚠️ Basic       |
| Spanish  | `es` | ↔️        | ⚠️ Basic       |

### Translation Workflow

1. **Parse Document**: Analyze markdown structure and identify sections
2. **Preserve Code**: Isolate code blocks and technical content
3. **Translate Content**: Process text sections with Ollama AI
4. **Reconstruct**: Rebuild document maintaining original formatting
5. **Validate**: Ensure structural integrity and completeness

### Integration with ai.gpt

```python
# Via ai.gpt MCP tools
await log_translate_document(
    input_file="README.ja.md",
    target_lang="en",
    model="qwen2.5:latest"
)
```

### Requirements

- **Ollama**: Install and run Ollama locally
- **Models**: Download supported models (qwen2.5:latest recommended)
- **Memory**: Sufficient RAM for model inference
- **Network**: For initial model download only

## Configuration Examples

### Basic Blog Config
```toml
[blog]
title = "My AI Blog"
description = "Personal thoughts and AI insights"
base_url = "https://myblog.example.com"

[ai]
provider = "openai"
model = "gpt-4"
translation = true
```

### Advanced Integration
```json
// ../config.json (ai.gpt)
{
  "mcp": {
    "servers": {
      "ai_gpt": {
        "endpoints": {
          "log_ai_content": "/log_ai_content",
          "log_create_post": "/log_create_post"
        }
      }
    }
  }
}
```

## Troubleshooting

### MCP Connection Issues
- Ensure ai.log server is running: `cargo run -- mcp --port 8002`
- Check ai.gpt config includes log endpoints
- Verify `./log/` directory exists relative to ai.gpt

### Build Failures
- Check Rust version: `rustc --version`
- Update dependencies: `cargo update`
- Clear cache: `cargo clean`

### AI Integration Problems
- Verify ai.gpt memory system is initialized
- Check AI provider configuration
- Ensure sufficient context in memory system

## systemd

```sh
$ sudo vim /usr/lib/systemd/system/ollama.service
[Service]
Environment="OLLAMA_ORIGINS=https://example.com"
```

```sh
# ファイルをsystemdディレクトリにコピー
sudo cp ./systemd/system/ailog-stream.service /etc/systemd/system/
sudo cp ./systemd/system/cloudflared-log.service /etc/systemd/system/

# 権限設定
sudo chmod 644 /etc/systemd/system/ailog-stream.service
sudo chmod 644 /etc/systemd/system/cloudflared-log.service

# systemd設定reload
sudo systemctl daemon-reload

# サービス有効化・開始
sudo systemctl enable ailog-stream.service
sudo systemctl enable cloudflared-log.service

sudo systemctl start ailog-stream.service
sudo systemctl start cloudflared-log.service

# 状態確認
sudo systemctl status ailog-stream.service
sudo systemctl status cloudflared-log.service

# ログ確認
journalctl -u ailog-stream.service -f
journalctl -u cloudflared-log.service -f

設定のポイント：
- User=syui でユーザー権限で実行
- Restart=always で異常終了時自動再起動
- After=network.target でネットワーク起動後に実行
- StandardOutput=journal でログをjournalctlで確認可能
```

## License

© syui

---

**Part of the ai ecosystem**: ai.gpt, ai.card, ai.log, ai.bot, ai.verse, ai.shell
