💬 zk-chat: AI-Powered Chat for Your Zettelkasten

Transform your Zettelkasten into an intelligent knowledge assistant. zk-chat lets you have natural conversations with your notes using AI—whether you run models locally with Ollama or use OpenAI's cloud API.

🎯 Why zk-chat?

• 🔒 Privacy First: Run everything locally with Ollama—no data leaves your machine
• 🤖 AI-Powered RAG: Semantic search with vector embeddings finds exactly what you need
• 📊 Visual Analysis: Analyze diagrams, charts, and images in your notes
• 🔗 Graph Traversal: Discover connections through wikilinks, backlinks, and forward links
• 🧠 Smart Memory: AI remembers context across conversations
• 🔌 Extensible: MCP server support and plugin architecture
• ⚡ Fast: Efficient local indexing and incremental updates
• 💻 CLI & GUI: Choose your interface—powerful command-line or experimental GUI

📚 Documentation

Full documentation: https://vetzal.com/zk-chat/

• Installation Guide - Get started in minutes
• Quick Start Tutorial - Your first conversation
• Model Selection Guide - Choose the right model for your hardware
• Command Reference - Complete CLI documentation
• Plugin Development - Extend zk-chat with custom tools

🚀 Quick Start

Installation

# Install with pipx (recommended)
pipx install zk-chat

# Install Ollama and a model
brew install ollama
ollama pull qwen3:8b

# For visual analysis (optional)
ollama pull gemma3:27b

Your First Chat

# Start interactive session
zk-chat interactive --vault /path/to/your/notes

# Ask a question
zk-chat query "What are my main ideas about productivity?"

# Single query with visual model
zk-chat query "Analyze diagram.png" --visual-model gemma3:27b

✨ Key Features

🔍 Intelligent Search & Retrieval

• Semantic Search: Vector embeddings find conceptually similar content
• Excerpt Retrieval: Extract relevant passages from your documents
• Full Document Access: AI can read complete notes when needed
• Graph Traversal: Explore connections via wikilinks, backlinks, and forward links
• Link Path Finding: Discover how concepts connect through your knowledge graph

🤖 AI Capabilities

• RAG (Retrieval Augmented Generation): AI answers using your actual notes
• Smart Memory: Persistent context across conversations
• Visual Analysis: Understand diagrams, charts, and images
• Multi-Model Support: Choose from dozens of Ollama models or use OpenAI
• Autonomous Agent Mode: AI can plan and execute complex multi-step tasks

🛠️ Content Management

• Read-Only by Default: Safe exploration of your knowledge base
• Optional Writing: Enable --unsafe mode for AI-assisted content creation
• Git Integration: Track all AI changes with automatic commits
• Wikilink Support: Full understanding of [[wikilink]] syntax
• Markdown Native: Works seamlessly with Obsidian and other markdown tools

🔌 Extensibility

• MCP Server Support: Connect external tools via Model Context Protocol
• Plugin Architecture: Extend with custom Python plugins
• Available Plugins:
  • Wikipedia Integration - Research and create notes from Wikipedia
  • Image Generation - Generate images with Stable Diffusion 3.5

📦 What's New in v3.5.0

🔖 Bookmark Management

# List all vault bookmarks
zk-chat bookmarks list

# Remove a bookmark
zk-chat bookmarks remove /path/to/vault

🔍 Index Diagnostics

# Check index health
zk-chat diagnose index

# Run test query to verify search
zk-chat diagnose index --query "test"

🔌 MCP Server Integration

# Register external tools
zk-chat mcp register figma stdio "figma-mcp"

# List and verify servers
zk-chat mcp list
zk-chat mcp verify

🤖 Updated Model Recommendations (2025)

Choose based on your RAM:

See the Model Selection Guide for detailed recommendations by task and hardware.

💡 Usage Examples

Interactive Chat

# Start a conversation
zk-chat interactive

# With specific model
zk-chat interactive --model qwen3:14b

# Allow AI to edit files (with git tracking)
zk-chat interactive --unsafe --git

# With visual analysis
zk-chat interactive --visual-model gemma3:27b

Single Queries

# Direct question
zk-chat query "What are my thoughts on AI?"

# From file or stdin
cat question.txt | zk-chat query

# Complex multi-step query
zk-chat query "Find all notes about productivity and create a summary"

Bookmark Management

# Save current vault
zk-chat interactive --vault ~/notes --save

# List bookmarks
zk-chat bookmarks list

# Remove bookmark
zk-chat bookmarks remove ~/old-notes

Index Management

# Quick incremental update
zk-chat index update

# Full rebuild
zk-chat index update --full

# Check status
zk-chat index status

Visual Analysis

Ask natural questions about images in your vault:

• "What's in the diagram at architecture.png?"
• "Describe the chart in sales-data.jpg"
• "Extract text from screenshot.png"

🎛️ Command-Line Options

Common Options (All Commands)

• --vault PATH or -v - Path to your Zettelkasten
• --gateway {ollama,openai} or -g - Choose AI provider
• --model NAME or -m - Specify chat model
• --visual-model NAME - Specify visual analysis model
• --save - Bookmark the current vault
• --no-index - Skip indexing on startup (faster start)

Agent Mode Options

• --unsafe - Allow AI to create/edit files ⚠️
• --git - Enable git integration (recommended with --unsafe)
• --store-prompt / --no-store-prompt - Control system prompt storage
• --reset-memory - Clear smart memory

⚠️ Safety: The --unsafe flag allows AI to modify your notes. Always use with --git for tracking changes.

🔧 Requirements

For Local AI (Ollama)

• Ollama installed and running
• Sufficient RAM for your chosen model (8GB minimum, 16GB+ recommended)
• Compatible with macOS (M1/M2/M3 recommended), Linux, and Windows

For Cloud AI (OpenAI)

• OpenAI API key set in OPENAI_API_KEY environment variable
• Internet connection
• API credits

Your Zettelkasten

• Markdown notes (.md files)
• Works with Obsidian, Logseq, or any markdown-based system
• Supports wikilinks ([[Note Name]])

🏗️ Architecture

zk-chat uses a modern RAG architecture:

1. Indexing: Creates vector embeddings of your notes using ChromaDB
2. Retrieval: Semantic search finds relevant content for queries
3. Generation: LLM generates responses using retrieved context
4. Tools: AI can use 15+ tools to explore and interact with your vault

The AI agent has access to:

• Document search and retrieval
• Wikilink graph traversal
• Visual analysis (with vision models)
• Smart memory storage
• Git version control
• External tools via MCP servers

🔌 Plugin Development

Create custom tools for your specific needs:

from zk_chat.services import ZkChatPlugin, ServiceProvider

class MyPlugin(ZkChatPlugin):
    def __init__(self, service_provider: ServiceProvider):
        super().__init__(service_provider)

    def run(self, input_text: str) -> str:
        # Access zk-chat services
        fs = self.filesystem_gateway
        zk = self.zettelkasten
        llm = self.llm_broker

        # Your plugin logic
        return result

See the Plugin Development Guide for complete documentation.

📊 Performance Tips

For Faster Responses

• Use smaller models (qwen3:1.5b, qwen2.5:3b)
• Increase concurrency: export OLLAMA_NUM_PARALLEL=4
• Skip indexing on startup: --no-index

For Better Quality

• Use larger models (gpt-oss:20b, qwen3:14b)
• Increase context window: export OLLAMA_NUM_CTX=16384
• Enable visual analysis for image-heavy vaults

Resource Management

# Check running models
ollama ps

# Monitor system resources
htop  # or Activity Monitor on Mac

# Remove unused models
ollama list
ollama rm unused-model

🗂️ Storage

zk-chat stores its data in your vault:

• .zk_chat - Configuration file
• .zk_chat_db/ - Vector database (ChromaDB)
• ZkSystemPrompt.md - System prompt (customizable)

Add these to .gitignore:

.zk_chat_db/
.zk_chat

🆘 Troubleshooting

Ollama Connection Issues

# Start Ollama
ollama serve

# Verify model installed
ollama list

Index Problems

# Diagnose index health
zk-chat diagnose index

# Force full rebuild
zk-chat index update --full

Performance Issues

• Try a smaller model
• Close other applications
• Increase system resources
• Use OpenAI instead of local models

🤝 Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

📜 License

MIT License - Copyright (c) 2024-2025 Stacey Vetzal

🔗 Links

• Documentation: https://vetzal.com/zk-chat/
• PyPI: https://pypi.org/project/zk-chat/
• GitHub: https://github.com/svetzal/zk-chat
• Issues: https://github.com/svetzal/zk-chat/issues
• Discussions: https://github.com/svetzal/zk-chat/discussions

⭐ Star History

If you find zk-chat useful, please consider starring the repository on GitHub!

Made with ❤️ for the Zettelkasten community

🧹 Linting and Auto-fixing

This project uses Ruff for linting and auto-fixes (including removing unused imports/variables and sorting imports).

Install dev tools

python -m pip install -e ".[dev]"

Check for issues (no changes written)

# Lint with Ruff (fails on errors; uses pyproject.toml configuration)
ruff check zk_chat

Auto-fix common issues (writes changes)

# Apply safe fixes (remove unused imports/variables, sort imports, etc.)
ruff check zk_chat --fix

Optional formatting (if desired):

# Ruff can also format code
ruff format zk_chat

Notes:

• Line length is enforced at 120 characters via Ruff (pyproject.toml).
• CI runs Ruff in the validation workflow; Flake8 and Autoflake are no longer used.