A Go-based system for syncing your notes to a vector database and querying them with AI assistance. The system consists of two main components:

• vector-sync: Monitors your notes directory and syncs changes to Pinecone vector database
• note-gpt: AI-powered query interface for your vectorized notes using Gemini

• 🔄 Real-time sync: Automatically detects file changes and updates the vector database
• 🔍 Semantic search: Query your notes using natural language
• 🤖 AI assistance: Get contextual answers from your notes using Google's Gemini
• 📁 File watching: Monitors .md files in your notes directory
• 🌲 Tree structure: Maintains directory structure for efficient syncing
• 💾 Incremental updates: Only processes changed files

• Go 1.21 or higher
• Pinecone account and API key
• Google AI Studio API key for Gemini
• Local embedding server (Ollama recommended)

git clone https://github.com/ashmaster/vector-notes.git

# Install vector-sync dependencies
cd vector-sync
go mod tidy
# Install note-gpt dependencies
cd ../note-gpt
go mod tidy

1. Create a Pinecone account
2. Create a new index with the following specifications:
   • Dimension: 768 (for nomic-embed-text model)
   • Metric: Cosine
   • Index Name: joyful-elm (or update the code to match your index name)

Install and run Ollama with the nomic-embed-text model:

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh
# Pull the embedding model
ollama pull nomic-embed-text
# Start Ollama server (runs on localhost:11434 by default)
ollama serve

Create .env files in both vector-sync/ and note-gpt/ directories:

vector-sync/.env:

PINECONE_API_KEY=your_pinecone_api_key
PINECONE_HOST=https://your-index-host.pinecone.io
NOTES_DIR=/path/to/your/notes/directory
EMBEDDING_URL=http://localhost:11434/api/embed

note-gpt/.env:

PINECONE_API_KEY=your_pinecone_api_key
PINECONE_HOST=https://your-index-host.pinecone.io
NOTES_DIR=/path/to/your/notes/directory
EMBEDDING_URL=http://localhost:11434/api/embed
GEMINI_API_KEY=your_gemini_api_key

Replace the placeholder values:

• your_pinecone_api_key: Your Pinecone API key
• https://your-index-host.pinecone.io: Your Pinecone index host URL
• /path/to/your/notes/directory: Absolute path to your notes directory
• your_gemini_api_key: Your Google AI Studio API key

The vector-sync service monitors your notes directory and keeps the vector database synchronized:

cd vector-sync
go run main.go

This will:

• Build an initial tree structure of your notes
• Start watching for file changes
• Sync changes to Pinecone every 5 seconds
• Log all sync operations

The note-gpt service provides an interactive query interface:

cd note-gpt
go run cmd/main.go

This starts an interactive session where you can:

• Ask questions about your notes
• Get AI-generated answers with file citations
• Maintain conversation context across queries

Example interaction:

> What are the cake ingredients?
Based on your notes from "Cake ingredients.md", you'll need flour, sugar, eggs, butter, and baking powder.
> How much flour?
According to the same file, you need 2 cups of all-purpose flour.

The repository includes VS Code launch configurations. You can:

1. Open the workspace in VS Code
2. Go to Run and Debug (Ctrl+Shift+D)
3. Select either "Launch Vector Sync" or "Launch note-gpt"
4. Press F5 to start debugging

1. File Watcher: Monitors .md files using fsnotify
2. Tree Structure: Maintains file hierarchy in Tree
3. Diff Detection: Compares client and server trees to find changes
4. Vector Upsert: Embeds content and stores in Pinecone via Vector

1. Query Processing: Takes user input and vectorizes it
2. Semantic Search: Finds top 2 relevant notes from Pinecone
3. Context Building: Reads file contents and builds LLM context
4. AI Response: Generates response using Gemini with conversation history

├── vector-sync/ # Sync service
│ ├── internal/
│ │ ├── config.go # Configuration management
│ │ ├── sync.go # Main synchronization logic
│ │ ├── tree.go # File tree operations
│ │ ├── node.go # Tree node structure
│ │ ├── watcher.go # File system watcher
│ │ └── utils.go # Utility functions
│ ├── pkg/
│ │ ├── vector.go # Pinecone integration
│ │ └── embedding.go # Embedding API client
│ └── main.go # Entry point
├── note-gpt/ # Query service
│ ├── cmd/
│ │ └── main.go # CLI interface
│ ├── internal/
│ │ ├── app.go # Main application logic
│ │ └── config.go # Configuration management
│ └── pkg/
│ ├── vector.go # Pinecone query operations
│ ├── embedding.go # Embedding API client
│ └── gemini.go # Gemini AI client

Both services provide detailed logging:

• File operations and sync status
• Vector database operations
• API calls and responses
• Error details with context

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

This project is licensed under the MIT License.