vault-graph-viewer

To visualize markdown notes as an interactive graph, you can use a tool that maps wikilinks and semantic similarity into a D3.js graph. This requires a local Flask server and a Python environment with ChromaDB to process your vault.

Semantic similarity for linking markdown notes works by calculating content relationships beyond direct wikilinks. ChromaDB processes the text to generate edges, allowing you to explore conceptually related notes and identify clusters within the vault.

Yes, you can use Flask and ChromaDB to explore a local vault of markdown files. The setup requires running a local Flask server with ChromaDB to index the markdown content and render the interactive D3.js visualization.

The best way to find orphaned notes in a markdown vault is by using a graph visualization tool that highlights disconnected nodes. This approach quickly identifies gaps in your knowledge base by filtering for notes lacking wikilinks or semantic similarity edges.

To start the vault graph viewer locally, run the vault_viewer.py script from the memory-index/viewer directory. This launches the Flask server to render your markdown notes as an interactive D3.js graph.