Auto Linker

Auto Linker

Surfaces the links you would have made by hand. As you write, Auto Linker quietly underlines the words that match your other notes and offers a one‑click wiki‑link — ranked by a confidence score (not a blind text match), so you get to choose the useful links and not the noise.

This software was vibe-coded

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Why it's different

Auto Linker always suggests and never links on its own. It also remembers your decisions to improve its suggestions over time using a weighted model. Most "auto link" tools do a binary match: a word either equals a note title or it doesn't - Auto Linker instead computes a confidence score for every candidate and only shows the ones that clear a threshold you control. That score blends several signals:

• Lexical match — how closely the text matches a note's title (exact, stemmed, or a close typo).
• Significance — common words (the, and, from) score low; distinctive words score high. No hard stop‑word list; it's graded, and self‑tunes to your vault via IDF.
• Capitalization — AND reads differently from and; ALL‑CAPS / TitleCase get a boost, lowercase function words a penalty.
• Note importance — well‑linked "hub" notes rank higher (PageRank over your link graph).
• Semantic meaning (optional) — an on‑device embedding model re‑ranks candidates by meaning.
• Learned preference — it learns from the links you actually make and the suggestions you accept.

Features

• Scored, ranked suggestions with a single sensitivity dial controlling suggestion noise.
• Per‑signal weight sliders — tune the balance yourself; restore defaults anytime.
• Smart tokenizer — handles 802.1Q, Topic : Subtopic, client-server, and path‑like titles via configurable separator rules.
• Typo tolerance — databse still finds Database; bounded so it stays quiet.
• Multi‑word titles & aliases, including a distinctive word standing in for the whole (kruger → Dunning‑Kruger).
• Learned aliases — link [[Dinosaur|raptor]] once and raptor can suggest Dinosaur thereafter, given a confident score.
• Reject memory — dismiss a suggestion per‑note or vault‑wide; manage them in settings.
• Hover to preview the target note, approve (✓), or reject (✗) inline.
• Skips [[existing links]], #tags, inline `code` and ``` fenced blocks.
• Never links a note to itself — nudging you toward atomic, outward‑defined notes.

Privacy

Everything runs locally. The index, scores, reject list, learned aliases, and the optional embedding cache all stay on your device — no telemetry, no external API. The only network event in the whole plugin is the optional, one‑time download of the embedding model when you turn the semantic tier on (it carries no note data, and can be avoided entirely by pointing at a model you already have).

The semantic tier (optional, off by default)

Turning on Semantic meaning loads a small embedding model that re‑ranks suggestions by meaning. When you enable it you choose:

• Download the default multilingual model (Xenova/paraphrase-multilingual-MiniLM-L12-v2, ~50 MB, once), or
• Use local model — point at a transformers.js‑format model already on disk (air‑gapped / your own).

Use Settings → "Index vault for semantics" to pre‑compute every note's "meaning fingerprint" so meaning‑based ranking is ready across the whole vault immediately.

The semantic tier re‑ranks literal candidates — it refines what the text match already found. It does not discover links for text with no word in common with a note title (e.g. typing "star wars" won't surface a Sci-Fi note). Add an alias for that.

Semantic suggestions have a dotted underline.

Rule‑based stemming covers Latin/Cyrillic scripts; Semitic Hebrew/Arabic morphology is left to the multilingual embedding model rather than faked.

Install

Manually: copy main.js, manifest.json, and styles.css (if present) into <vault>/.obsidian/plugins/auto-linker/, then enable it in Settings → Community plugins.

Via BRAT: add this repository as a beta plugin, then enable Auto Linker in the Community Plugins list.

Usage

1. Start typing. Matching spans get a subtle underline (a dotted underline means the match was lifted by meaning rather than text alone).
2. Hover a suggestion to preview the target, then ✓ to insert [[Note|text]] or ✗ to dismiss.
3. Dismissed suggestions can be escalated to vault‑wide from the staging panel, and reviewed under Settings → Rejected suggestions.
4. Tune Sensitivity and the Signal weights in settings to match how you work.

Build from source

npm install
npm run dev      # watch mode — pairs with the Hot-Reload plugin
npm run build    # production build → main.js

Built with esbuild; the embedding backend (@xenova/transformers) is bundled and lazy‑loaded only when the semantic tier is enabled.

License

GPL-3.0-only