ⓘ Previously published as vault-search (plugin id and repository renamed). A different plugin authored by a separate developer now occupies the vault-search id — see the Upgrading from vault-search section below before installing if you used earlier versions.

Why Vault Curate?

Obsidian's built-in search is literal: think "prayer" but your note says "devotional" and you'll miss it. Most semantic-search plugins use generic multilingual models, which tend to under-perform on Chinese content.

Andrej Karpathy shared his vision of LLM-maintained knowledge bases — letting AI "compile" your notes into structured wikis. Compelling, but it asks you to hand over full editorial control. Vault Curate takes a different stance: AI should help you see, not think for you.

Three differentiators

Quick Start

1. In Obsidian, go to Settings → Community plugins and search for Vault Curate
2. After enabling, the Welcome to Vault Curate modal opens. Under Embedding provider, pick Built-in (on-device, WebGPU) and click Index my vault now
3. After the ~110 MB model download and WebGPU indexing finish, click the sidebar compass icon and start searching

⚠️ Vault Curate is currently going through Obsidian's community review. If you can't find it in Community plugins yet, use the Manual install path below.

Installation

Requirements

• Obsidian desktop (v1.0.0+)
• Advanced paths only: a local Ollama instance or any OpenAI-compatible server

From Community plugins (recommended)

1. Open Settings → Community plugins in Obsidian
2. Make sure Restricted mode is off, click Community plugins → Browse
3. Search Vault Curate → Install → Enable
4. The Welcome to Vault Curate modal opens automatically on first launch

Manual install

1. Download main.js, manifest.json, styles.css, worker.js, ort-wasm-simd-threaded.wasm from Releases
2. Copy them into .obsidian/plugins/vault-curate/ in your vault
3. Enable in Settings → Community plugins

Tip: If your vault is Git-tracked, add .obsidian/plugins/*/data.json and .obsidian/plugins/*/index.sqlite to .gitignore.

Upgrading from vault-search

If you used the earlier vault-search plugin, follow this path:

1. Open your vault folder and locate .obsidian/plugins/vault-search/
2. Delete that folder directly from the filesystem. ⚠️ Do not use Community plugins → Uninstall — a different plugin now occupies the vault-search id and may insert itself when you uninstall.
3. Install Vault Curate via the Installation steps above
4. Enable. The Welcome to Vault Curate modal will guide you through rebuilding the index.

Embeddings are not reused across versions — a from-scratch rebuild takes ~1–2 minutes on WebGPU for a few hundred notes. Frontmatter descriptions and tags already in your notes are preserved (they live in the .md files, not in the index).

If you had keybindings set on vault-search:* commands, redo them under vault-curate:* in Settings → Hotkeys (9 commands total — see Commands).

Features

Search (Hybrid Fusion)

Three signals combined via Reciprocal Rank Fusion (k=60):

Two entry points:

• Cmd/Ctrl+P → Vault Curate: Semantic search (modal) for quick jump
• Sidebar → Search tab for persistent results

Discover

Discover works on notes, not query strings — it surfaces semantically related Cold notes you haven't touched recently:

• Current note: when you open a file, related notes appear automatically, with Cold notes visually highlighted ("you haven't read this one")
• Global: Cold notes most related to your entire Hot pool — intentional blind-spot mining
• Results can be exported to a topic-grouped Map of Content via Generate MOC (falls back to a flat MOC if results are too few or too similar)

Hot / Cold auto-tiering

Notes are auto-classified by internal links + recency:

• Hot: linked to / recently touched
• Cold: orphan / untouched for a while

The "recent" cutoff is tunable in Settings → Advanced → Hot window (days). Cold notes don't get buried in Discover — they're exactly the content you should be re-seeing.

Find similar notes

Right-click any .md → VC: Find similar notes → results show up in the sidebar; you can drag them straight to Canvas.

AI curation (off by default)

Turn it on under Settings → AI Curation → Enable AI curation to unlock three actions:

• Generate a description + tags into a single note's frontmatter
• Run description generation across the sidebar's search / discover results
• Generate a topic-grouped MOC via HDBSCAN clustering + LLM naming

The LLM provider is configured separately under Settings → AI Curation (local Ollama or any OpenAI-compatible endpoint).

Commands

From Command Palette (Cmd/Ctrl+P), type Vault Curate: to see them all.

Right-click menus expose two of these directly on a .md:

• VC: Find similar notes
• VC: Generate description (AI curation on)

Settings

The settings panel is split into three sections:

Quick setup

Changing the embedding provider or model triggers a confirmation modal — the index is wiped and rebuilt.

AI Curation

Advanced

Collapsible <details> block: top results / min score / Hot window (days) / default search scope (Hot / Cold / All) / chunk size + overlap / synonym list / auto-index toggle / rebuild + update buttons / index stats.

Privacy

Three embedding modes, picked from Quick setup → Embedding provider:

The same applies to AI curation (description / MOC naming), which uses an independently-configured LLM endpoint.

No telemetry. No usage tracking. Nothing is sent to any server unless you configure a remote endpoint.

Audit disclosures

The Obsidian Developer Dashboard's automated audit may flag the following items on this plugin. They are intentional and disclosed here for transparency:

• Vault enumeration (vault.getMarkdownFiles()): The indexer needs to walk the full list of markdown files in your vault to build the semantic embedding index. The excludePatterns setting (Settings → Advanced) lets you scope this — e.g. excluding _templates/, .trash/, or any folder you don't want indexed. No file is read until it's in the included set.
• Dynamic code execution (new Function in bundled @huggingface/transformers): The Hugging Face Transformers library uses new Function internally to create type-safe method dispatchers during model loading. Vault Curate's own source code contains zero eval() or new Function(). We bundle the upstream library as-is to avoid divergence; the dynamic dispatch happens only inside the embedding model's tokenizer/inference setup, not on any vault content.
• Direct filesystem access: The bundled sql.js ships an Emscripten output with a Node.js fallback path that imports node:fs / node:crypto. These branches are dead code in Obsidian's renderer process (gated by process.type !== "renderer"). As of v1.0.3, the esbuild config strips those require() strings from the released bundle so the audit no longer sees them.

🔒 About API key storage

Vault Curate, like every Obsidian plugin, stores its settings (including any OpenAI API key) as plain text in <vault>/.obsidian/plugins/vault-curate/data.json. This is Obsidian's plugin storage mechanism, not a vault-curate-specific design choice.

If your vault syncs to a cloud service (iCloud / Dropbox / Google Drive) or pushes to a public Git repository, you should:

1. Add .obsidian/plugins/vault-curate/data.json to your sync exclusion list or .gitignore
2. Or use the Built-in model / Ollama path — neither requires an API key

Tech Stack

• TypeScript + esbuild (two-stage bundle for worker + main)
• sql.js (SQLite via WASM) for the storage layer — replaces v0.x's data.json / index.json
• Pure-TS BM25+ (src/storage/bm25.ts) for CJK-aware full-text search (no native FTS5 dependency)
• @huggingface/transformers + bge-small-zh-v1.5 q8 (~110 MB, WebGPU/WASM) for on-device embeddings
• hdbscan-ts for topic clustering (MOC)
• Reciprocal Rank Fusion (k=60) combining BM25 + semantic + fuzzy
• Optional: Ollama / any OpenAI-compatible endpoint for higher-end embedding or LLM models

Development

git clone https://github.com/notoriouslab/vault-curate.git
cd vault-curate
npm install
npm run dev    # watch mode
npm run build  # production build
npm test       # vitest unit tests (59 tests)

License

MIT