AutoAiTag 🔖

Intelligent AI-powered metadata generation for Obsidian vaults

AutoAiTag automatically enhances your Obsidian notes with AI-generated summaries, tags, and intelligent date detection. It's designed to work with local language models (LM Studio) for maximum privacy and cost-effectiveness.

✨ Key Features

🤖 AI-Powered Metadata Generation

• Smart Summaries: Generate concise, contextual summaries (max 50 words) for long notes (Configurable; Default=1000 chars)
• Intelligent Tagging: Extract up to 3 relevant keyword tags from note content
• Date Detection: Three-tier system for accurate date extraction:
  1. AI Detection: LLM extracts dates from note content with confidence scoring
  2. Filename Parsing: Extract dates from filenames like (2024-01-15)
  3. File System Fallback: Use file creation date as last resort

🛡️ Vault-Safe Processing

• Non-destructive: Preserves all existing frontmatter and metadata
• Smart Merging: Combines new AI tags with existing ones
• Smart Update: Uses autoAiTag tag to prevent re-processing files that AutoAiTag has already processed. Can be 'forced' to reprocess all files if need be.
• Non Destructive Dry Run: Use the Dry run option first to see a JSON file containing all the proposed changes before comitting them to your Markdown files. Includes a confidence level for Date parsing.

⚡ Performance & Scalability

• Multithreaded Processing: Configurable worker threads for large vaults
• Concurrency Control: Limit simultaneous LLM requests to prevent server overload
• Progress Tracking: Real-time feedback and detailed error logging
• Batch Processing: Handle thousands of notes efficiently

🔧 Flexible Configuration

• Local LLM Support: Works with LM Studio, Ollama, or any OpenAI-compatible API
• Customizable Thresholds: Adjust character limits and processing parameters
• Environment Variables: Easy deployment across different systems
• Debug Mode: Detailed logging for troubleshooting

🚀 Installation

Prerequisites

• Python 3.7+ (Python 3.8+ recommended)
• LM Studio or compatible local LLM server
• Obsidian vault with markdown files

Quick Install

# Clone the repository
git clone https://github.com/tonyknight/AutoAiTag.git
cd AutoAiTag

# Install dependencies
pip install -r requirements-minimal.txt

# Run the script
python AutoAiTag.py

Alternative Installation

# Install dependencies directly
pip install requests PyYAML

# Download and run
wget https://raw.githubusercontent.com/tonyknight/AutoAiTag/main/AutoAiTag.py
python AutoAiTag.py

⚙️ Configuration

LM Studio Setup

1. Install LM Studio from lmstudio.ai
2. Download a model (recommended: Llama2 7B or larger)
3. Start the server with OpenAI-compatible API enabled
4. Update the script with your server details:

# In AutoAiTag.py, update these settings:
API_URL = "http://localhost:1234/v1/chat/completions"  # Your LM Studio endpoint
MODEL_NAME = "llama2:7b"  # Your preferred model

Key Configuration Options

# Processing Settings
DEFAULT_CHAR_LIMIT = 1000      # Minimum characters for AI summary processing
DEFAULT_WORKERS = 4            # Number of concurrent file processors
DEFAULT_MAX_CONCURRENT_LLM = 2 # Max simultaneous LLM requests
REQUEST_TIMEOUT = 60           # LLM response timeout (seconds)

📖 Usage

Basic Workflow

1. Run the script: python AutoAiTag.py
2. Choose mode:
   • Dry Run: Test without modifying files
   • Write Mode: Actually update your notes
3. Configure settings:
   • Character threshold for AI processing
   • Worker thread count
   • Force processing options
4. Process your vault: Let AutoAiTag enhance your notes!

Example Output

---
title: Meeting Notes
Date: 2024-01-15
summary: Team discussed Q1 goals, budget allocation, and timeline for new product launch
tags: [meeting, planning, budget, product-launch]
wordCount: 1250
autoAiTag: true
---

Processing Modes

🔍 Dry Run Mode

• Purpose: Test configuration and see what would happen
• Output: metadata_dryrun.json with planned changes
• Safety: No files are modified
• Use case: First-time setup, testing, auditing

✏️ Write Mode

• Purpose: Actually enhance your notes with metadata
• Output: Modified markdown files with new frontmatter
• Safety: Creates backups, preserves existing content
• Use case: Production use, bulk processing

📊 Error Handling & Logging

Error Logs

AutoAiTag generates detailed error logs for troubleshooting:

• Filename: (YYYY-MM-DD HH-MM-SS) AutoAiTag Log.json
• Contents: File paths, error messages, timestamps
• Location: Same directory as your vault

Common Issues & Solutions

• LLM Connection Failed: Check LM Studio server status and API URL
• Permission Errors: Ensure write access to vault directory
• Memory Issues: Reduce worker thread count
• Slow Processing: Increase LLM concurrency limit (if server can handle it)

🔧 Advanced Usage

Command Line Options

# Process specific subdirectory
python AutoAiTag.py
# Then enter: ./DailyNotes

# Force reprocessing of all files
python AutoAiTag.py
# Choose Write Mode, then: Force processing = y

Custom Date Formats

AutoAiTag recognizes these filename patterns:

• Meeting Notes (2024-01-15).md ✅
• Journal 2024-01-15.md ❌ (no parentheses)
• Notes (2024-1-5).md ✅ (auto-formats to 2024-01-05)

Performance Tuning

# For large vaults (1000+ files)
DEFAULT_WORKERS = 8
DEFAULT_MAX_CONCURRENT_LLM = 4

# For slower LLM servers
DEFAULT_MAX_CONCURRENT_LLM = 1
REQUEST_TIMEOUT = 120

Development Setup

# Install development dependencies
pip install -r requirements.txt

# Run tests
python -m pytest

# Code formatting
black AutoAiTag.py
flake8 AutoAiTag.py

Areas for Improvement

• Additional local LLM providers (Ollama, etc.)
• More metadata fields (reading time, complexity score)
• GUI interface for command line haters

📝 License

This project is licensed under the MIT License.

📞 Support

• Issues: GitHub Issues
• Wiki: Documentation
• Discussions: GitHub Discussions

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Made with ❤️ for the Obsidian community

Transform your vault from a collection of notes into an intelligent, searchable knowledge base with AutoAiTag.