🧠 Karpathy LLM Wiki Plugin for Obsidian

An Obsidian plugin that turns your notes into a connected, queryable knowledge base — the Karpathy LLM Wiki idea, built into the editor where you already write.

Obsidian Review Perfect Score • Zero-embedding graph retrieval • 10-language native • Works with every provider Local-first • No backend • GDPR-Friendly

English | 简体中文 | 繁體中文 | 日本語 | 한국어 | Deutsch | Français | Español | Português | Italiano

Official Site | Obsidian Marketplace | Blog | Discussions

🤔 Why this plugin? | 🚀 Quick Start | ✨ Features | 🌐 Ecosystem | 🔍 How Retrieval Works | 🤖 Models | ❓ FAQ

← If this plugin has helped you, feel free to buy me a coffee♥️ or drop a star🌟↗

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📑 Contents

• Why this plugin?
• Is it for me?
• Quick Start
• Features
• Ecosystem
• Tools
• How retrieval works
• Models
• FAQ
• Privacy
• Support
• License & Credits

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🤔 Why this plugin?

You write notes. They sit in folders. Finding what relates to what means remembering threads you forgot months ago.

Other open-source reimplementations of Karpathy's LLM Wiki idea exist — but none of them ships as a one-click Obsidian plugin. Most are CLI tools, Claude Code skills, or separate desktop apps. We are the only one with native UI, in-vault storage, and Obsidian's own Graph View built in.

How we compare

	Karpathy LLM Wiki (this plugin)	nashsu / llm_wiki	SamurAIGPT / llm-wiki-agent	sdyckjq / llm-wiki-skill	atomicstrata / llm-wiki-compiler
Delivery & install	✅ 5 min — One-click Obsidian plugin: Community Plugins → Install → pick provider → Ingest	❌ 30 min+ — Compile/download Tauri binary, configure CLI	❌ 15 min — Claude Code subscription + skill install	❌ 10 min — Claude Code/Codex subscription + skill setup	❌ 30 min+ — pip install + Python SDK + local server
Architecture & dependencies	✅ Zero dependencies — no vector DB, no embedding model, no external processes (PPR over [[wiki-link]] graph, by design)	🟡 Embeds Python runtime + sigma.js + sqlite; optional embeddings off by default	🟡 Uses Claude Code's environment — not self-contained; no embeddings	🟡 Requires separate platform runtime; no embeddings	❌ Requires Python + embedding model + vector DB (mandatory)
i18n (UI + wiki output)	✅ 10 languages (independent UI / output)	🟡 2 (EN / 中文)	❌ English only	❌ English only	❌ English only
LLM providers	✅ 12+ (incl. Codex OAuth, Bedrock, LM Studio, Ollama, Anthropic-compatible, Kimi, GLM, MiniMax, DeepSeek)	🟡 OpenAI-compatible	🟡 Subscription via Claude Code	🟡 Subscription via Claude Code / Codex	🟡 OpenAI-compatible
Retrieval & query pipeline	✅ 5-stage cascade — Lex → LLM keyword → substring scan → LLM KB fallback → PPR expansion (truncates on first sufficient signal). Personalized PageRank (Haveliwala 2002) + Monte Carlo (Fogaras 2005)	🟡 2-hop decay only (4-signal heuristic: Adamic-Adar + 2-hop)	❌ Louvain community detection only	❌ k-hop previews only (no LLM augment)	❌ BM25 + semantic over chunks (no graph)
Graph visualization	✅ Obsidian's native Graph View (built in, zero extra size)	❌ Custom sigma.js + graphology in desktop app	🟡 vis.js graph.html (separate file)	❌ Custom sigma.js offline HTML	❌ Read-only browser viewer
Wiki honesty	✅ "Stage FALLBACK" banner when no wiki source matches your query	❌ No equivalent	❌ No equivalent	❌ No equivalent	❌ No equivalent
Published retrieval benchmark	✅ PPR @5 = 27.1% vs pure-kNN 24.1% (only published number in this space)	❌ 58% → 71% only with embeddings enabled, not in our apples-to-apples format	❌ Not published	❌ Not published	❌ Not published

Three things we chose on purpose, not by accident

• 🪟 Obsidian is the runtime. No terminal, no separate app, no Docker, no Python. Install from Community Plugins, click Ingest, the wiki lives in your vault from the first second. Obsidian's native Graph View renders your [[wiki-link]] graph — built in, zero extra bundle size.
• 🧭 Clean and self-contained. Zero dependencies. No embedding model, no vector database, no pip package — a single plugin that reads your notes, talks to an LLM, and writes wiki pages. Everything lives inside Obsidian.
• 🔌 Any model you already pay for. Anthropic, Bedrock, OpenAI, ChatGPT Plan (Codex OAuth), DeepSeek, Kimi, GLM, MiniMax, LM Studio, Ollama, OpenRouter, Anthropic-compatible, custom endpoint — twelve-plus providers, none of them required to have an embedding endpoint.

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🎯 Is it for me?

✅ Yes, if you:

• Want a 5-minute setup, not a 5-hour project. Install from Community Plugins → pick a provider → Ingest one note. No CLI, no Python, no separate runtime, no vector DB. You see wiki pages in wiki/ within seconds.
• Want something clean and self-contained. The plugin has exactly zero external dependencies: no embedding model, no vector database, no pip package, no Docker container. It's a single Obsidian plugin that reads your notes, talks to an LLM, and writes wiki pages into your vault. Everything lives inside Obsidian.
• Want a queryable chat that answers from your notes — not the internet — with every answer carrying [[wiki-links]] back into your knowledge graph.
• Care about data sovereignty — runs fully local with Ollama or LM Studio, never touching the internet.
• Write in or read from any of 10 supported languages — the UI and wiki output language are independent (your wiki can be in Chinese while the interface is in English).
• Maintain the graph by writing [[wiki-links]] — every link you write already enriches retrieval; no separate tagging/embedding/indexing step.
• Want one-click maintenance — Lint health scan + Smart Fix All keep duplicates, dead links, and orphan pages in check without you hand-curating.

❌ No, if you:

• Want a general-purpose ChatGPT replacement — this plugin answers from your knowledge only.
• Need a RAG pipeline over PDFs / web pages / external corpora — we focus on the in-vault path (PDFs are supported as of v1.25.0).
• Are looking for a hosted SaaS — there's no backend, no server, no account.

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🚀 Quick Start

1. Install. Obsidian → Settings → Community plugins → Browse → search "Karpathy LLM Wiki" → Install → Enable. Or visit the Community Plugin page and click Add to Obsidian.

2. Configure a provider. Open Settings → Karpathy LLM Wiki → pick a provider (OpenAI, Anthropic, Ollama, ChatGPT Plan (Codex OAuth), etc.) → enter API key (not needed for local) → click Test Connection → Save.

3. Ingest one note. Two ways:

   • ⌨️ Keyboard: Cmd+P/Ctrl+P → "Ingest single source" → pick any Markdown (or PDF, v1.25.0+) file.
   • 🖱️ Toolbar icon: Click the sticker icon in Obsidian's left ribbon to instantly ingest the currently-open note — no menu hunting.

   Your first wiki pages appear in wiki/sources/, wiki/entities/, wiki/concepts/ within seconds.

4. Query your wiki. Two ways:

   • ⌨️ Keyboard: Cmd+P/Ctrl+P → "Query wiki".
   • 🖱️ Toolbar icon: Click the message-circle icon in Obsidian's left ribbon.

   A right-docked side panel opens (Copilot-style) where you can chat with your wiki. Answers carry [[wiki-links]] back into your knowledge graph.

That's it. The plugin modifies nothing in your original notes — only creates new pages under wiki/. Both Ingest and Query wiki are pinned to the left ribbon for one-click access anytime. (Cmd on macOS, Ctrl on Windows/Linux.)

Core commands

Command	What it does
📥 Ingest single source	Cmd+P/Ctrl+P → "Ingest single source" — pick a Markdown or PDF (v1.25.0+) file, get entity/concept/wiki pages. Also: 🖱️ ribbon sticker icon on the active note.
📂 Ingest from folder	Cmd+P/Ctrl+P → "Ingest from folder" — batch-ingest every note in a folder, with smart batch skip
📑 Ingest multiple files	Cmd+P/Ctrl+P → "Ingest multiple files" — pick a subset via a two-pane file tree (with live queue + per-file cancel)
🔍 Query wiki	Cmd+P/Ctrl+P → "Query wiki" — chat with your wiki in a right-docked side panel; answers carry [[wiki-links]]. Also: 🖱️ ribbon message-circle icon.
🛠️ Lint wiki	Cmd+P/Ctrl+P → "Lint wiki" — full health scan: duplicates, dead links, empty pages, orphans, missing aliases, contradictions
⚡ Smart Fix All	inside Lint Modal — one-click causal-order repair with per-phase report
📋 Regenerate index	Cmd+P/Ctrl+P → "Regenerate index" — rebuild wiki/index.md with current pages and aliases
⏹ Cancel	Cmd+P/Ctrl+P → "Cancel current ingestion" or click the status bar — stops cleanly at the next batch boundary
📊 Ingestion history	Cmd+P/Ctrl+P → "View Ingestion History" — searchable UI for past ingestions, lint reports, maintenance runs

Before	After
notes/machine-learning.md (a flat file)	wiki/concepts/supervised-learning.md with [[bidirectional links]], aliases, source attribution, and an entry in wiki/index.md

💡 Stay updated. New features, fixes, and performance improvements ship frequently. Settings → Community plugins → Check for updates, or enable automatic plugin updates. 📖 Detailed walkthroughs (installation, PDF setup, multi-provider notes, upgrades) are maintained in GitHub Discussions → Guides.

🌟 If this saved you setup time, a star on GitHub helps others find it.

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✨ Features

📚 Knowledge quality

• 🔍 Entity & concept extraction — LLM extracts entities (people, orgs, products, events) and concepts (theories, methods, terms) into standalone pages. Granularity is configurable (Minimal → Fine, plus Custom) so you trade cost vs. depth.
• 🏷️ Mandatory aliases — every page ships with at least one alias (translation, abbreviation, variant) so cross-language duplicate detection works.
• 🔄 Tiered duplicate detection — Tier 1 (direct name match: cross-language, abbreviation, high-similarity titles) is always verified; Tier 2 (shared links, medium similarity) fills remaining token budget.
• 🧩 Smart merge & contradiction state — duplicates merge while preserving aliases; contradictions are flagged with source attribution; reviewed: true pages are protected from overwrite.
• 🎨 Custom tag vocabulary — define your own entity-type and concept-type tag lists in Settings → Wiki → Tag Vocabulary → Custom. The vocabulary is a schema injection hint for the LLM, not a write-time enforcement gate — small/local models may still drift, and the Lint diagnostic surfaces those pages so you can fix them. (Schema enforcement is being designed for v1.26.0+; see the design anchor.)

📄 PDF ingest (v1.25.0+)

• 🔌 Provider gate — Anthropic, OpenAI, and Bedrock handle PDF natively. For any other OpenAI/Anthropic-compatible endpoint, enable Force PDF Support in Settings → LLM Configuration → Advanced to let the plugin attempt the call. For local OCR on Apple Silicon, third-party extractors (MinerU, Docling, Mathpix, Adobe), and the full PDF ingest walkthrough, see PDF OCR Paths below and docs/PDF-OCR-GUIDE.md.
• 🗄️ Bounded cache — .obsidian/plugins/karpathywiki/pdf-cache/ stores converted Markdown keyed by content hash + model + converter version. Three-defense-layer housekeeping: 100 MB total / 1000 entries / 10 MB single-entry caps with LRU-by-mtime eviction.
• 📝 Optional vault sidecar — Settings → Wiki Configuration → Wiki Folder → Write PDF Markdown to Vault writes <basename>.pdf.md next to the source PDF (off by default — cache-only is the default).
• 🛡️ Verbatim transcriber prompt — OCR-style conversion with [illegible] / [figure: ...] anti-hallucination markers; markdown-fence-wrapping from small local models is auto-cleaned before cache write.

📄 PDF OCR Paths

Three paths, pick what fits your setup:

1. ☁️ Cloud provider with native PDF support — Anthropic, OpenAI, or AWS Bedrock read PDFs out of the box. Just ingest; no extra setup. For any other OpenAI/Anthropic-compatible endpoint, enable Force PDF Support in Settings → LLM Configuration → Advanced to let the plugin attempt the call.
2. 🖥️ Local OCR on Apple Silicon — oMLX integrates Microsoft Markitdown as a built-in PDF→Markdown backend. Enable Markitdown in oMLX, load Baidu Unlimited-OCR (3B / 570M-active, open-sourced 2026-06) as the vision model, point the plugin at oMLX as a Custom OpenAI-Compatible provider, turn on Force PDF Support, and pick the multimodal model oMLX is serving. The PDF never leaves your machine.
3. 🛠️ Third-party extractor (MinerU, Docling, Mathpix, Adobe) — run a separate extractor on your PDFs to produce .md files, then ingest them as regular Markdown notes via the plugin's standard pipeline. Most reliable for scientific papers, scanned documents, math-heavy PDFs.

📖 Full setup walkthroughs for all three paths (cloud providers, oMLX hardware tiers, MinerU installation, cache housekeeping) → docs/PDF-OCR-GUIDE.md

💬 Query & maintenance

• 🧭 5-stage PPR cascade — see How retrieval works. Personalized PageRank over [[wiki-link]] gives graph-aware multi-hop context.
• 🪟 Right-docked side panel — Query Wiki opens in a Copilot-style right sidebar leaf (v1.22.1+) instead of a centered modal.
• 🔍 Lint health scan — single command catches: duplicates, dead links, empty pages, orphans, missing aliases, contradictions.
• ⚡ Smart Fix All — one-click causal-order repair: fill aliases → merge duplicates → fix dead links → link orphans → expand empty pages, with per-phase report.
• 📊 Operation history panel — searchable, filterable UI for past ingestions, lint reports, and maintenance runs.
• 🛡️ Pre-ingest gate — empty / whitespace / frontmatter-only notes are rejected before any LLM call; content-hash dedup catches identical files across paths.

🔒 Privacy

• 🚫 No backend, no tracking, no analytics. Runs entirely inside Obsidian. Network is used only to communicate with the LLM provider you configure.
• 📁 Source files are read-only. The plugin never modifies your original vault notes — only creates new pages under wiki/.
• 🦙 Full local mode. Ollama, LM Studio, or any local OpenAI-compatible endpoint → your notes never leave your machine.
• 🔐 Minimal permissions. Vault file access for wiki management. Clipboard access only when you click the "Copy" button in the Query modal.

🦙 Local-first

• 🖥️ Ollama, LM Studio, OpenRouter, custom endpoint — out-of-the-box. Local models work for query (smaller context windows); ingest on a 2,000-page vault usually needs a long-context cloud model.
• 📄 PDF OCR path is fully local on Apple Silicon — see PDF OCR Paths below.
• 🔐 ChatGPT Plan (Codex OAuth) — desktop loopback callback on 127.0.0.1:1455; mobile via device-code. Credentials live only in Obsidian SecretStorage; sign-out clears them. Third-party Codex compatibility, not an OpenAI partnership.

🌐 Language

• 🌍 10 UI languages — English, 简体中文, 繁體中文, 日本語, 한국어, Deutsch, Français, Español, Português, Italiano. UI and wiki-output language are independent — your wiki can be Chinese while the interface is English.
• 📚 10 wiki-output languages — same set; pick in Settings → Wiki Configuration. Custom input option for ad-hoc prompts.
• 🈶 269+ translated UI strings — every label, modal, and notice. Adding an 11th language is contributor-driven (PR #159 pattern).

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🌐 Ecosystem

The plugin composes with the rest of your Obsidian stack — each tool below plugs into the [[wiki-link]] graph without code changes.

• 📄 MinerU online conversion — official free PDF/Word/PPT/Excel/HTML/image → Markdown converter from Shanghai AI Lab's OpenDataLab team. Upload a document, download the .md, drop it in your vault outside the wiki folder, run Ingest single source. Best path for scientific papers, scanned documents, and complex multi-modal PDFs that need accurate formula/table extraction. Privacy-sensitive users can self-host MinerU; future versions may integrate MinerU natively — see #376.
• 🕸️ Obsidian Graph View — open the native graph on any wiki page; every [[wiki-link]] becomes a node, every back-link an edge. Built in, zero extra bundle size.
• ✂️ Obsidian Web Clipper — official browser extension. Save web pages (articles, blog posts, Reddit threads, Hacker News, recipes, research papers, YouTube transcripts via Interpreter) into any folder of your vault, then run the plugin's Ingest from folder command to batch-extract entities and concepts.
• 📊 Dataview — query the wiki like a database with DQL (LIST FROM "wiki/entities" WHERE contains(tags, "person")) or JS API. The plugin writes standard frontmatter (tags:, type:, aliases:) on every page, so Dataview queries work out of the box.
• 🌿 Git — version your vault (any Git client). The plugin never rewrites your source files; only creates new pages under wiki/, so git diff cleanly separates your edits from LLM-generated content.
• 🎞️ Marp Slides — turn any Obsidian note into slide decks via Marp frontmatter (marp: true). Wiki pages are pure Markdown, so they render as slides without extra conversion.
• 🖼️ Canvas — Obsidian's native infinite canvas. Drag wiki cards onto a Canvas to assemble study guides, mind maps, or research overviews from [[wiki-links]] without leaving the vault.
• 🎤 Obsidian Nous — companion plugin for local voice memo and meeting capture (whisper.cpp on macOS; audio never leaves the machine). Generates speaker-labeled transcripts and its own wiki hub pages. Independent of this plugin — both can share the same vault without coupling.

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🛠️ Tools

The plugin ships a headless CLI in this repo so you can run the same ingest pipeline against a vault on disk — no Obsidian, no Electron, no display. The engine, the analyzer, the page factory, the schema manager and the LLM clients are imported straight from src/; only the host (obsidian, the live vault, the metadataCache) is replaced by a shim. Useful for CI, scripted runs, comparing sampling parameters across arms, and profiling the extract loop on a single source.

pnpm llm-wiki ingest --vault /path/to/vault --source "notes/foo.md" --dry-run

See the full flag reference, environment requirements, and shim caveats in tools/llm-wiki-cli/README.md.

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🔍 How retrieval works

Most "AI search" plugins fragment your notes into chunks and embed them in a vector DB. We don't. Karpathy's argument against RAG is that chunking breaks the LLM's ability to reason across your whole knowledge graph — and that argument holds up in practice. Instead, we walk the graph you already maintain by writing [[wiki-links]].

The 5-stage seed-selection cascade

When you ask "Who founded Microsoft?", Query Wiki runs five stages before any answer generation:

1. Lex fast path — straight token-overlap against every entity/concept title and aliases. Free, instant, and the gating step for everything that follows.
2. LLM keyword generation — the LLM proposes 8–12 cross-language keywords from your query (handles synonyms, abbreviations, and token-overlap-resistant terms in one LLM call).
3. Local substring scan — every generated keyword is re-matched locally against page titles, aliases, and body snippets. No extra LLM call; rounds out noise-tolerant recall.
4. LLM KB fallback — when lex + keyword scan returns weak signals, the LLM re-seeds the top-N candidates against the full wiki for one semantic pass.
5. PPR graph expansion — Personalized PageRank (Haveliwala 2002) over the [[wiki-link]] graph starting from the candidate seed set. This is what gives graph-aware multi-hop context: "Bill Gates" → "Microsoft" → "competitors", not just literal title overlap.

The cascade truncates at whichever step returned enough signal — no fixed 5-step cost, no LLM calls when lex is sufficient, no lost precision when LLM augmentation is needed.

Personalized PageRank at scale

We use Monte Carlo PPR (Fogaras 2005) — 3,000 random walks × 50 steps each — with the dead-end rule from Haveliwala 2002. Cost is O(K × L) independent of the number of pages, so a 2,000-page vault sees the same expansion latency as a 200-page one.

PPR @5 = 27.1% vs pure-kNN baseline 24.1% on the project's own benchmark corpus (the only published retrieval benchmark in this open-source LLM-Wiki space).

Why no embeddings

We deliberately rejected the embedding path in Issue #175. The graph signal is already there — every [[wiki-link]] is a hand-curated "these are related" edge, and most providers we support (Ollama, LM Studio, Anthropic, Bedrock, Kimi, GLM, MiniMax) don't ship a /v1/embeddings endpoint at all. Adding an embedding model would mean a per-page download, a per-provider adapter, and zero benefit on retrieval quality.

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🤖 Models

Supported providers (12+, all from models.dev cross-check 2026-07):

Provider	Series	Notes
Anthropic	Claude 5 series	Native PDF; /v1/messages protocol
OpenAI	GPT-5.6 series (Sol / Terra / Luna)	Native PDF; Platform API key
Google Gemini	Gemini 3.6 series	Native PDF (file parts since 1.5); OpenAI-compatible endpoint
DeepSeek	DeepSeek V4 series	OpenAI-compatible; lowest cost tier
Alibaba Qwen	Qwen3.7/3.8 series	OpenAI-compatible (DashScope)
xAI Grok	Grok 4 series	OpenAI-compatible; long context
Moonshot Kimi	Kimi K3 series	OpenAI-compatible; 2.8T MoE frontier
Zhipu GLM	GLM-5 series	OpenAI-compatible; strong bilingual
MiniMax	MiniMax M3 series	OpenAI-compatible; 1M context
Step (阶跃星辰)	Step 3 series (Flash)	OpenAI-compatible; fast inference
Tencent Hunyuan	Hy3 series	OpenAI-compatible; open-weight MoE
Xiaomi MiMo	MiMo V2.5 series	MIT open-source; flat pricing
Google Gemma	Gemma 4 series	Open-weight; 262K context
AWS Bedrock	Anthropic + OpenAI variants	VPC / compliance path
ChatGPT Plan (Codex OAuth)	Codex Responses API	Browser/device-code sign-in; SecretStorage
Local: Ollama, LM Studio, OpenRouter, Anthropic-Compatible	Any OpenAI-/Anthropic-protocol model	Custom OpenAI-Compatible + Anthropic-Compatible (Token Plan / Coding Plan)

This plugin feeds the LLM your full Wiki context per query — so long-context models win. The full tiered table (cloud + local) lives in docs/MODEL-GUIDE.md, cross-checked against models.dev so the picks stay current.

What matters

• 🧠 Context window ≥ 200K tokens for vaults over ~500 pages. Below 200K the cascade's assembled context starts getting truncated.
• ⚖️ Instruction-following quality matters more than raw IQ for the extraction task — pick a model that follows the schema template, not the biggest leaderboard number.
• 🔌 Embedding endpoint is irrelevant — we don't use embeddings. A provider that lacks /v1/embeddings is fine (most of our 12+ providers do).
• 🦙 Local works for query, cloud for ingest — ingest on a 2,000-page vault usually needs a long-context cloud model; a 262K local model covers most queries.

Anthropic vs OpenAI vs Codex OAuth — they are distinct providers

• Anthropic (and its Bedrock variant) — separately billed Anthropic Platform API key.
• OpenAI — separately billed OpenAI Platform API key.
• ChatGPT Plan (Codex OAuth) — experimental, distinct provider that uses eligible Codex allowance after browser or device-code sign-in; availability follows OpenAI Codex authentication and allowance policies, not plan name. Third-party Codex compatibility, not an OpenAI partnership or a general ChatGPT API.

📖 Full pick table (cloud + local + PDF OCR + Codex OAuth + quantization + hardware tiers) → docs/MODEL-GUIDE.md

❓ FAQ

What does the plugin actually do?

Pick any note, folder, or selection; the LLM extracts entities and concepts and generates an interlinked wiki with [[bidirectional links]]. Ask questions and get conversational answers grounded in your notes, not the internet. Your original vault notes are never modified.

How do I get started?

Install from Obsidian Community Plugins → pick a provider → Test Connection → run Ingest single source on any note. First wiki pages appear within seconds. See Quick Start.

Is my existing wiki safe?

✅ Backward compatible since v1.0.0. Set reviewed: true on any page to protect it from overwrite. Upgrading from v1.24.x doesn't rewrite your vault; v1.25.0's PDF ingest is cache-only by default.

Is my data sent anywhere?

🚫 No backend, no analytics — the plugin runs entirely inside Obsidian. Only text you explicitly send for ingest/query leaves your device, and only to the LLM provider you configure. For complete data locality, use Ollama or LM Studio.

Can I use the plugin in my language?

🌍 10 languages for both UI and wiki output. UI and wiki language are independent. Adding an 11th language is contributor-driven (PR #159 pattern).

How is this different from a RAG chatbot?

🚫 No chunking. 🚫 No embeddings. 🚫 No vector DB. ✅ Personalized PageRank over your existing [[wiki-link]] graph — graph-aware multi-hop context, zero embedding cost, full local-model support.

Which LLM should I use?

Long-context models (≥200K tokens) work best. The Models section covers the principles; the full tiered table is in docs/MODEL-GUIDE.md.

Is there a published benchmark?

Yes — PPR @5 = 27.1% vs pure-kNN baseline 24.1% on the project's own corpus. The full pipeline and benchmark script are described in How retrieval works.

How do I control API costs?

Use Coarse or Minimal extraction granularity for batch ingest. Smart Batch Skip auto-detects already-ingested files. Auto-Maintenance is OFF by default. Lint shows counts before running fixes — nothing is charged without your approval.

How do I cancel a running operation?

Click the status bar (shows "Ingesting… click to cancel") or Cmd+P/Ctrl+P → "Cancel current ingestion". Stops cleanly at the next batch boundary.

Where do I get help?

GitHub Issues for bug reports · GitHub Discussions for questions and feature requests · Developer Console (Ctrl+Shift+I / Cmd+Option+I) for plugin logs.

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🔒 Privacy

This plugin is listed on the Obsidian Community Plugin Market and undergoes automated review for security and permissions.

• 🚫 No backend, no server, no data collection. Pure local software running inside Obsidian. The plugin cannot and does not collect, store, or transmit your data to any server — because no such server exists.
• 🔐 Network access is opt-in. Used only to communicate with the LLM provider you configure. You choose the provider, you enter the API key, you decide where your data goes.
• 📁 Vault file access is used for wiki management (reading notes, generating pages, scanning dead links, detecting duplicates). The plugin never modifies your source files.
• 📋 Clipboard access is used exclusively by the "Copy" button in the Query modal — and only when you click it.

For complete data locality, use Ollama or LM Studio. With a local provider, your data never leaves your machine.

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💖 Support

If LLM-Wiki has become a meaningful part of your knowledge workflow:

• ☕ Buy me a Ko-fi — one-time or monthly
• 💳 Tip via PayPal — one-time tip

Sponsorship is entirely optional. The plugin stays Apache-2.0-licensed and feature-complete regardless.

Thanks to @jameses-cyber and @issaqua for supporting the project.

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📜 License & Credits

Apache License, Version 2.0 — see LICENSE and NOTICE.

Built on:

• 💡 Andrej Karpathy's LLM Wiki — the original concept
• 🛠️ Obsidian Plugin API
• 🔌 Vercel AI SDK v6 (@ai-sdk/openai, @ai-sdk/anthropic, @ai-sdk/openai-compatible) via Obsidian requestUrl
• 🧮 Personalized PageRank (Haveliwala 2002) and Monte Carlo PPR (Fogaras 2005) — retrieval algorithms

Maintainer: @green-dalii