655 lines
19 KiB
Markdown
655 lines
19 KiB
Markdown
# ai.log
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AI-powered static blog generator with ATProto integration, part of the ai.ai ecosystem.
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## 🚀 Quick Start
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### Development Setup
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```bash
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# 1. Clone and setup
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git clone https://git.syui.ai/ai/log
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cd log
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# 2. Start development services
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./run.zsh serve # Blog development server
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./run.zsh c # Cloudflare tunnel (example.com)
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./run.zsh o # OAuth web server
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./run.zsh co # Comment system monitor
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# 3. Start Ollama (for Ask AI)
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brew install ollama
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ollama pull gemma2:2b
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OLLAMA_ORIGINS="https://example.com" ollama serve
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```
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### Production Deployment
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```bash
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# 1. Build static site
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hugo
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# 2. Deploy to GitHub Pages
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git add .
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git commit -m "Update blog"
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git push origin main
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# 3. Automatic deployment via GitHub Actions
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# Site available at: https://yourusername.github.io/repo-name
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```
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### ATProto Integration
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```bash
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# 1. OAuth Client Setup (oauth/client-metadata.json)
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{
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"client_id": "https://example.com/client-metadata.json",
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"client_name": "ai.log Blog System",
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"redirect_uris": ["https://example.com/oauth/callback"],
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"scope": "atproto",
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"grant_types": ["authorization_code", "refresh_token"],
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"response_types": ["code"],
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"application_type": "web",
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"dpop_bound_access_tokens": true
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}
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# 2. Comment System Configuration
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# Collection: ai.syui.log (comments)
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# User Management: ai.syui.log.user (registered users)
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# 3. Services
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./run.zsh o # OAuth authentication server
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./run.zsh co # ATProto Jetstream comment monitor
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```
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### Development with run.zsh
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```bash
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# Development
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./run.zsh serve
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# Production (with Cloudflare Tunnel)
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./run.zsh tunnel
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# OAuth app development
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./run.zsh o
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# Comment system monitoring
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./run.zsh co
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```
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## 📋 Commands
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| Command | Description |
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|---------|-------------|
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| `./run.zsh c` | Enable Cloudflare tunnel (example.com) for OAuth |
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| `./run.zsh o` | Start OAuth web server (port:4173 = example.com) |
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| `./run.zsh co` | Start comment system (ATProto stream monitor) |
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## 🏗️ Architecture (Pure Rust + HTML + JS)
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```
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ai.log/
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├── oauth/ # 🎯 OAuth files (protected)
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│ ├── oauth-widget-simple.js # Self-contained OAuth widget
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│ ├── oauth-simple.html # OAuth authentication page
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│ ├── client-metadata.json # ATProto configuration
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│ └── README.md # Usage guide
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├── my-blog/ # Blog content and templates
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│ ├── content/posts/ # Markdown blog posts
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│ ├── templates/ # Tera templates
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│ ├── static/ # Static assets (OAuth copied here)
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│ └── public/ # Generated site (build output)
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├── src/ # Rust blog generator
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├── scripts/ # Build and deployment scripts
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└── run.zsh # 🎯 Main build script
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```
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### ✅ Node.js Dependencies Eliminated
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- ❌ `package.json` - Removed
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- ❌ `node_modules/` - Removed
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- ❌ `npm run build` - Not needed
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- ✅ Pure JavaScript OAuth implementation
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- ✅ CDN-free, self-contained code
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- ✅ Rust-only build process
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---
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## 📖 Original Features
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[](https://www.rust-lang.org/)
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[](https://opensource.org/licenses/MIT)
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## 概要
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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。
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## 主な特徴
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### 🎨 モダンインターフェース
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- **Anthropic Docs風デザイン**: プロフェッショナルで読みやすい
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- **Timeline形式**: BlueskyライクなタイムラインUI
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- **自動TOC**: 右サイドバーに目次を自動生成
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- **レスポンシブ**: モバイル・デスクトップ対応
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### 🤖 Ask AI機能 ✅
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- **ローカルAI**: Ollama(gemma2:2b)による質問応答
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- **認証必須**: ATProto OAuth認証でアクセス制御
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- **トップページ限定**: ブログコンテンツに特化した回答
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- **CORS解決済み**: OLLAMA_ORIGINS設定でクロスオリジン問題解消
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- **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
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- **レスポンス最適化**: 80文字制限+高いtemperatureで多様な回答
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- **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
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### 🔧 Ask AI設定方法
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```bash
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# 1. Ollama設定
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brew install ollama
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ollama pull gemma2:2b
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# 2. CORS設定で起動
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OLLAMA_ORIGINS="https://example.com" ollama serve
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# 3. AI DID設定 (my-blog/templates/base.html)
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const aiConfig = {
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systemPrompt: 'You are a helpful AI assistant.',
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aiDid: 'did:plc:your-ai-bot-did'
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};
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```
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### 🌐 分散SNS連携
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- **atproto OAuth**: Blueskyアカウントでログイン
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- **コメントシステム**: 分散SNSコメント
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- **データ主権**: ユーザーがデータを所有
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### 🔗 エコシステム統合
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- **ai.gpt**: ドキュメント同期・AI機能連携
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- **MCP Server**: ai.gptからの操作をサポート
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- **ai.wiki**: 自動ドキュメント同期
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## Architecture
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### Dual MCP Integration
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**ai.log MCP Server (API Layer)**
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- **Role**: Independent blog API
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- **Port**: 8002
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- **Location**: `./src/mcp/`
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- **Function**: Core blog generation and management
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**ai.gpt Integration (Server Layer)**
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- **Role**: AI integration gateway
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- **Port**: 8001 (within ai.gpt)
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- **Location**: `../src/aigpt/mcp_server.py`
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- **Function**: AI memory system + HTTP proxy to ai.log
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### Data Flow
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```
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Claude Code → ai.gpt (Server/AI) → ai.log (API/Blog) → Static Site
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↑ ↑
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Memory System File Operations
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Relationship AI Markdown Processing
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Context Analysis Template Rendering
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```
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## Features
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- **Static Blog Generation**: Inspired by Zola, built with Rust
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- **AI-Powered Content**: Memory-driven article generation via ai.gpt
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- **🌍 Ollama Translation**: Multi-language markdown translation with structure preservation
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- **atproto Integration**: OAuth authentication and comment system (planned)
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- **MCP Integration**: Seamless Claude Code workflow
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## Installation
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```bash
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cargo install ailog
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```
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## Usage
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### Standalone Mode
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```bash
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# Initialize a new blog
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ailog init myblog
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# Create a new post
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ailog new "My First Post"
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# Build the blog
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ailog build
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# Serve locally
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ailog serve
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# Start MCP server
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ailog mcp --port 8002
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# Generate documentation
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ailog doc readme --with-ai
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ailog doc api --output ./docs
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ailog doc structure --include-deps
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# Translate documents (requires Ollama)
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ailog doc translate --input README.md --target-lang en
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ailog doc translate --input docs/api.md --target-lang ja --model qwen2.5:latest
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# Clean build files
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ailog clean
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```
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### AI Ecosystem Integration
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When integrated with ai.gpt, use natural language:
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- "ブログ記事を書いて" → Triggers `log_ai_content`
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- "記事一覧を見せて" → Triggers `log_list_posts`
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- "ブログをビルドして" → Triggers `log_build_blog`
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### Documentation & Translation
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Generate comprehensive documentation and translate content:
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- "READMEを生成して" → Triggers `log_generate_docs`
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- "APIドキュメントを作成して" → Generates API documentation
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- "プロジェクト構造を解析して" → Creates structure documentation
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- "このファイルを英語に翻訳して" → Triggers `log_translate_document`
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- "マークダウンを日本語に変換して" → Uses Ollama for translation
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## MCP Tools
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### ai.log Server (Port 8002)
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- `create_blog_post` - Create new blog post
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- `list_blog_posts` - List existing posts
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- `build_blog` - Build static site
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- `get_post_content` - Get post by slug
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- `translate_document` ⭐ - Ollama-powered markdown translation
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- `generate_documentation` ⭐ - Code analysis and documentation generation
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### ai.gpt Integration (Port 8001)
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- `log_create_post` - Proxy to ai.log + error handling
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- `log_list_posts` - Proxy to ai.log + formatting
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- `log_build_blog` - Proxy to ai.log + AI features
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- `log_get_post` - Proxy to ai.log + context
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- `log_system_status` - Health check for ai.log
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- `log_ai_content` ⭐ - AI memory → blog content generation
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- `log_translate_document` 🌍 - Document translation via Ollama
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- `log_generate_docs` 📚 - Documentation generation
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### Documentation Generation Tools
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- `doc readme` - Generate README.md from project analysis
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- `doc api` - Generate API documentation
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- `doc structure` - Analyze and document project structure
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- `doc changelog` - Generate changelog from git history
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- `doc translate` 🌍 - Multi-language document translation
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### Translation Features
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- **Language Support**: English, Japanese, Chinese, Korean, Spanish
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- **Markdown Preservation**: Code blocks, links, images, tables maintained
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- **Auto-Detection**: Automatically detects Japanese content
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- **Ollama Integration**: Uses local AI models for privacy and cost-efficiency
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- **Smart Processing**: Section-by-section translation with structure awareness
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## Configuration
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### ai.log Configuration
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- Location: `~/.config/syui/ai/log/`
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- Format: TOML configuration
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### ai.gpt Integration
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- Configuration: `../config.json`
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- Auto-detection: ai.log tools enabled when `./log/` directory exists
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- System prompt: Automatically triggers blog tools for related queries
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## AI Integration Features
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### Memory-Driven Content Generation
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- **Source**: ai.gpt memory system
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- **Process**: Contextual memories → AI analysis → Blog content
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- **Output**: Structured markdown with personal insights
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### Automatic Workflows
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- Daily blog posts from accumulated memories
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- Content enhancement and suggestions
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- Related article recommendations
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- Multi-language content generation
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## atproto Integration (Planned)
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### OAuth 2.0 Authentication
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- Client metadata: `public/client-metadata.json`
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- Comment system integration
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- Data sovereignty: Users own their comments
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- Collection storage in atproto
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### Comment System
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- **ATProto Stream Monitoring**: Real-time Jetstream connection monitoring
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- **Collection Tracking**: Monitors `ai.syui.log` collection for new comments
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- **User Management**: Automatically adds commenting users to `ai.syui.log.user` collection
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- **Comment Display**: Fetches and displays comments from registered users
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- **OAuth Integration**: atproto account login via Cloudflare tunnel
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- **Distributed Storage**: Comments stored in user-owned atproto collections
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## Build & Deploy
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### GitHub Actions
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```yaml
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# .github/workflows/gh-pages.yml
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- name: Build ai.log
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run: |
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cd log
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cargo build --release
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./target/release/ailog build
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```
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### Cloudflare Pages
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- Static output: `./public/`
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- Automatic deployment on main branch push
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- AI content generation during build process
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## Development Status
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### ✅ Completed Features
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- Project structure and Cargo.toml setup
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- CLI interface (init, new, build, serve, clean, mcp, doc)
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- Configuration system with TOML support
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- Markdown parsing with frontmatter support
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- Template system with Handlebars
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- Static site generation with posts and pages
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- Development server with hot reload
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- **MCP server integration (both layers)**
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- **ai.gpt integration with 6 tools**
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- **AI memory system connection**
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- **📚 Documentation generation from code**
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- **🔍 Rust project analysis and API extraction**
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- **📝 README, API docs, and structure analysis**
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- **🌍 Ollama-powered translation system**
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- **🚀 Complete MCP integration with ai.gpt**
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- **📄 Markdown-aware translation preserving structure**
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- **💬 ATProto comment system with Jetstream monitoring**
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- **🔄 Real-time comment collection and user management**
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- **🔐 OAuth 2.1 integration with Cloudflare tunnel**
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- **🤖 Ask AI feature with Ollama integration**
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- **⚡ CORS resolution via OLLAMA_ORIGINS**
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- **🔒 Authentication-gated AI chat**
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- **📱 Top-page-only AI access pattern**
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- Test blog with sample content and styling
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### 🚧 In Progress
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- AI-powered content enhancement pipeline
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- Advanced comment moderation system
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### 📋 Planned Features
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- Advanced template customization
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- Plugin system for extensibility
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- Real-time comment system
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- Multi-blog management
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- VTuber integration (ai.verse connection)
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## Integration with ai Ecosystem
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### System Dependencies
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- **ai.gpt**: Memory system, relationship tracking, AI provider
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- **ai.card**: Future cross-system content sharing
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- **ai.bot**: atproto posting and mention handling
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- **ai.verse**: 3D world blog representation (future)
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### yui System Compliance
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- **Uniqueness**: Each blog post tied to individual identity
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- **Reality Reflection**: Personal memories → digital content
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- **Irreversibility**: Published content maintains historical integrity
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## Getting Started
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### 1. Standalone Usage
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```bash
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git clone [repository]
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cd log
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cargo run -- init my-blog
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cargo run -- new "First Post"
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cargo run -- build
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cargo run -- serve
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```
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### 2. AI Ecosystem Integration
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```bash
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# Start ai.log MCP server
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cargo run -- mcp --port 8002
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# In another terminal, start ai.gpt
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cd ../
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# ai.gpt startup commands
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# Use Claude Code with natural language blog commands
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```
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## Documentation Generation Features
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### 📚 Automatic README Generation
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```bash
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# Generate README from project analysis
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ailog doc readme --source ./src --with-ai
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# Output: Enhanced README.md with:
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# - Project overview and metrics
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# - Dependency analysis
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# - Module structure
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# - AI-generated insights
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```
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### 📖 API Documentation
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```bash
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# Generate comprehensive API docs
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ailog doc api --source ./src --format markdown --output ./docs
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# Creates:
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# - docs/api.md (main API overview)
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# - docs/module_name.md (per-module documentation)
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# - Function signatures and documentation
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# - Struct/enum definitions
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```
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### 🏗️ Project Structure Analysis
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```bash
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# Analyze and document project structure
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ailog doc structure --source . --include-deps
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# Generates:
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# - Directory tree visualization
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# - File distribution by language
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# - Dependency graph analysis
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# - Code metrics and statistics
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```
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### 📝 Git Changelog Generation
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```bash
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# Generate changelog from git history
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ailog doc changelog --from v1.0.0 --explain-changes
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# Creates:
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# - Structured changelog
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# - Commit categorization
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# - AI-enhanced change explanations
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```
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### 🤖 AI-Enhanced Documentation
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When `--with-ai` is enabled:
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- **Content Enhancement**: AI improves readability and adds insights
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- **Context Awareness**: Leverages ai.gpt memory system
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- **Smart Categorization**: Automatic organization of content
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- **Technical Writing**: Professional documentation style
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## 🌍 Translation System
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### Ollama-Powered Translation
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ai.log includes a comprehensive translation system powered by Ollama AI models:
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```bash
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# Basic translation
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ailog doc translate --input README.md --target-lang en
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# Advanced translation with custom settings
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ailog doc translate \
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--input docs/technical-guide.ja.md \
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--target-lang en \
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--source-lang ja \
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--output docs/technical-guide.en.md \
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--model qwen2.5:latest \
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--ollama-endpoint http://localhost:11434
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```
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### Translation Features
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#### 📄 Markdown-Aware Processing
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- **Code Block Preservation**: All code snippets remain untranslated
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- **Link Maintenance**: URLs and link structures preserved
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- **Image Handling**: Alt text can be translated while preserving image paths
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- **Table Translation**: Table content translated while maintaining structure
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- **Header Preservation**: Markdown headers translated with level maintenance
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#### 🎯 Smart Language Detection
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- **Auto-Detection**: Automatically detects Japanese content using Unicode ranges
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- **Manual Override**: Specify source language for precise control
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- **Mixed Content**: Handles documents with multiple languages
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#### 🔧 Flexible Configuration
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- **Model Selection**: Choose from available Ollama models
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- **Custom Endpoints**: Use different Ollama instances
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- **Output Control**: Auto-generate or specify output paths
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- **Batch Processing**: Process multiple files efficiently
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### Supported Languages
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| Language | Code | Direction | Model Optimized |
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|----------|------|-----------|-----------------|
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| English | `en` | ↔️ | ✅ qwen2.5 |
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| Japanese | `ja` | ↔️ | ✅ qwen2.5 |
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| Chinese | `zh` | ↔️ | ✅ qwen2.5 |
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| Korean | `ko` | ↔️ | ⚠️ Basic |
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| Spanish | `es` | ↔️ | ⚠️ Basic |
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### Translation Workflow
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1. **Parse Document**: Analyze markdown structure and identify sections
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2. **Preserve Code**: Isolate code blocks and technical content
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3. **Translate Content**: Process text sections with Ollama AI
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4. **Reconstruct**: Rebuild document maintaining original formatting
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5. **Validate**: Ensure structural integrity and completeness
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### Integration with ai.gpt
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```python
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# Via ai.gpt MCP tools
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await log_translate_document(
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input_file="README.ja.md",
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target_lang="en",
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model="qwen2.5:latest"
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)
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```
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### Requirements
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- **Ollama**: Install and run Ollama locally
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- **Models**: Download supported models (qwen2.5:latest recommended)
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- **Memory**: Sufficient RAM for model inference
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- **Network**: For initial model download only
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## Configuration Examples
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### Basic Blog Config
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```toml
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[blog]
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title = "My AI Blog"
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description = "Personal thoughts and AI insights"
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base_url = "https://myblog.example.com"
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[ai]
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provider = "openai"
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model = "gpt-4"
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translation = true
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```
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### Advanced Integration
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```json
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// ../config.json (ai.gpt)
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{
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"mcp": {
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"servers": {
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"ai_gpt": {
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"endpoints": {
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"log_ai_content": "/log_ai_content",
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"log_create_post": "/log_create_post"
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}
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}
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}
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}
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}
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```
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## Troubleshooting
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### MCP Connection Issues
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- Ensure ai.log server is running: `cargo run -- mcp --port 8002`
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- Check ai.gpt config includes log endpoints
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- Verify `./log/` directory exists relative to ai.gpt
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### Build Failures
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- Check Rust version: `rustc --version`
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- Update dependencies: `cargo update`
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- Clear cache: `cargo clean`
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### AI Integration Problems
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- Verify ai.gpt memory system is initialized
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- Check AI provider configuration
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- Ensure sufficient context in memory system
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## systemd
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```sh
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$ sudo vim /usr/lib/systemd/system/ollama.service
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[Service]
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Environment="OLLAMA_ORIGINS=https://example.com"
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```
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```sh
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# ファイルをsystemdディレクトリにコピー
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sudo cp ./systemd/system/ailog-stream.service /etc/systemd/system/
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sudo cp ./systemd/system/cloudflared-log.service /etc/systemd/system/
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# 権限設定
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sudo chmod 644 /etc/systemd/system/ailog-stream.service
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sudo chmod 644 /etc/systemd/system/cloudflared-log.service
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# systemd設定reload
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sudo systemctl daemon-reload
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# サービス有効化・開始
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sudo systemctl enable ailog-stream.service
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sudo systemctl enable cloudflared-log.service
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sudo systemctl start ailog-stream.service
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sudo systemctl start cloudflared-log.service
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# 状態確認
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sudo systemctl status ailog-stream.service
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sudo systemctl status cloudflared-log.service
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# ログ確認
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journalctl -u ailog-stream.service -f
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journalctl -u cloudflared-log.service -f
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設定のポイント：
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- User=syui でユーザー権限で実行
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- Restart=always で異常終了時自動再起動
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- After=network.target でネットワーク起動後に実行
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- StandardOutput=journal でログをjournalctlで確認可能
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```
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## License
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© syui
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---
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**Part of the ai ecosystem**: ai.gpt, ai.card, ai.log, ai.bot, ai.verse, ai.shell
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