Vault Curate
Jacob Mei1k downloadsHybrid semantic search for your vault notes. BM25 + WebGPU embedding + fuzzy retrieval, Multilingual, with particularly strong Chinese/CJK support, with optional LLM
- Overview
- Scorecard
- Updates6
Vault Curate
High-quality Chinese-friendly semantic search for Obsidian, with optional AI curation.
ⓘ Previously published as
vault-search(plugin id and repository renamed). A different plugin authored by a separate developer now occupies thevault-searchid — 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
| Feature | How it works |
|---|---|
| Chinese semantic quality beats generic multilingual models | Ships with bge-small-zh-v1.5 (Chinese-only training). In head-to-head testing on Chinese names, religious terms, and colloquial phrases, generic MiniLM-style multilingual models miss most of the matches; Vault Curate consistently recalls the right notes. |
| Zero-config to run, WebGPU accelerated | ~110 MB model downloads once. WebGPU indexing: 342 notes / 5,004 chunks in about 1m23s (WASM fallback still works, around 27 minutes). |
| AI curation is opt-in, never silent | Description generation, MOC clustering, and frontmatter rewrites all require explicit opt-in. Nothing runs LLMs in the background and nothing rewrites your notes without you asking. |
Quick Start
- In Obsidian, go to Settings → Community plugins and search for Vault Curate
- After enabling, the Welcome to Vault Curate modal opens. Under Embedding provider, pick Built-in (on-device, WebGPU) and click Index my vault now
- 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)
- Open Settings → Community plugins in Obsidian
- Make sure Restricted mode is off, click Community plugins → Browse
- Search Vault Curate → Install → Enable
- The Welcome to Vault Curate modal opens automatically on first launch
Manual install
- Download
main.js,manifest.json,styles.css,worker.js,ort-wasm-simd-threaded.wasmfrom Releases - Copy them into
.obsidian/plugins/vault-curate/in your vault - Enable in Settings → Community plugins
Tip: If your vault is Git-tracked, add
.obsidian/plugins/*/data.jsonand.obsidian/plugins/*/index.sqliteto.gitignore.
Upgrading from vault-search
If you used the earlier vault-search plugin, follow this path:
- Open your vault folder and locate
.obsidian/plugins/vault-search/ - Delete that folder directly from the filesystem. ⚠️ Do not use Community plugins → Uninstall — a different plugin now occupies the
vault-searchid and may insert itself when you uninstall. - Install Vault Curate via the Installation steps above
- 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):
| Path | Catches |
|---|---|
| BM25 (pure TS, CJK trigram) | Exact phrases, keyword combinations |
| Semantic embedding | Different wording, same meaning |
| Fuzzy title (Jaro–Winkler) | Typos, spelling variants |
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.
| Command | What it does | Requires |
|---|---|---|
Semantic search (modal) |
Modal-style semantic search with quick jump | always available |
Open search panel |
Open the sidebar panel | always available |
Find similar notes |
Find semantically related notes to the active .md |
always available |
Rebuild index |
Wipe the existing index and re-index everything | always available |
Update index |
Incremental update (re-index files with newer mtime) | always available |
Discover related Cold notes |
Global discover: Cold notes most related to your Hot pool | always available |
Generate description for active note |
LLM-write description + tags to the active file's frontmatter | AI curation on |
Generate descriptions for current results |
Batch description for the current sidebar results | AI curation on |
Generate MOC (topic-grouped) |
HDBSCAN cluster + LLM-name each group | AI curation on |
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
| Setting | Default | Note |
|---|---|---|
| Embedding provider | Built-in (on-device, WebGPU) | One of three: Built-in / Ollama / OpenAI-compatible |
| Excluded folders | (empty) | Folder globs that won't be indexed |
Changing the embedding provider or model triggers a confirmation modal — the index is wiped and rebuilt.
AI Curation
| Setting | Default | Note |
|---|---|---|
| Enable AI curation | off | When off, description / MOC commands stay hidden |
| LLM provider | Ollama | The endpoint used for description + MOC naming |
| LLM model | qwen3:1.7b | Recommended default; any Ollama model works |
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:
| Mode | Where embeddings run | Where note text goes |
|---|---|---|
| Built-in | On-device WebGPU / WASM | Stays on your device |
| Ollama (local daemon) | Local Ollama daemon on 127.0.0.1 | Stays on your device |
| OpenAI-compatible API | Any endpoint you point it at — could be local (LM Studio, llama.cpp, …) or remote (OpenAI etc.) | Depends on the endpoint you choose; may leave your device |
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.
🔒 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:
- Add
.obsidian/plugins/vault-curate/data.jsonto your sync exclusion list or.gitignore - 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-v1.5q8 (~110 MB, WebGPU/WASM) for on-device embeddingshdbscan-tsfor 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)