# 🧠 Karpathy LLM Wiki Plugin for Obsidian > An Obsidian plugin that turns your notes into a connected, queryable knowledge base — the [Karpathy LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) idea, built into the editor where you already write. **Obsidian Review Perfect Score • Zero-embedding graph retrieval • 11-language native • Native PDF + images + Office ingest • Works with every provider • Local-first • No backend • GDPR-Friendly**     
  
  [](https://github.com/green-dalii/obsidian-llm-wiki/actions/workflows/release.yml) [](https://deepwiki.com/green-dalii/obsidian-llm-wiki) **English** | [简体中文](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/docs/README_CN.md) | [繁體中文](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/docs/README_ZH-Hant.md) | [日本語](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/docs/README_JA.md) | [한국어](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/docs/README_KO.md) | [Deutsch](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/docs/README_DE.md) | [Français](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/docs/README_FR.md) | [Español](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/docs/README_ES.md) | [Português](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/docs/README_PT.md) | [Italiano](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/docs/README_IT.md) | [Русский](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/docs/README_RU.md) [Official Site](https://llmwiki.greenerai.top/) | [Obsidian Marketplace](https://community.obsidian.md/plugins/karpathywiki) | [Blog](https://llmwiki.greenerai.top/blog/) | [Discussions](https://github.com/green-dalii/obsidian-llm-wiki/discussions) 🤔 [Why this plugin?](#-why-this-plugin) | 🚀 [Quick Start](#-quick-start) | ✨ [Features](#-features) | 🌐 [Ecosystem](#-ecosystem) | 🛠️ [Headless CLI](#-headless-cli) | 🔍 [How Retrieval Works](#-how-retrieval-works) | 🤖 [Models](#-models) | ❓ [FAQ](#-faq) [](https://ko-fi.com/H7V1228WMD) ← If this plugin has helped you, feel free to buy me a coffee♥️ or drop a star🌟↗ --- ## 🤔 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 ships as a one-click Obsidian plugin.** Most are CLI tools, Claude Code skills, or separate desktop apps; this one runs inside Obsidian — Graph View, ribbons, command palette included. ### How we compare | | **Karpathy LLM Wiki** (this plugin) | nashsu / llm_wiki | SamurAIGPT / llm-wiki-agent | atomicstrata / llm-wiki-compiler | |---|---|---|---|---| | **Delivery** | ✅ One-click Obsidian plugin | 🟡 Tauri desktop app | 🟡 Claude Code / Codex / OpenCode / Gemini CLI skill | 🟡 TypeScript CLI pipeline | | **Dependencies** | ✅ None — plugin only | 🟡 Python runtime + sqlite | 🟡 Claude Code / Codex / OpenCode runtime | ❌ Embedding model + vector DB (per their docs) | | **i18n (UI + wiki output)** | ✅ 11 languages | 🟡 EN / 中文 | ❌ EN only | ❌ EN only | | **LLM providers** | ✅ 16+ (Anthropic, OpenAI, Bedrock, Gemini, DeepSeek, Qwen, Grok, Kimi, GLM, MiniMax, Step, Hunyuan, MiMo, Gemma, Codex OAuth, Ollama, LM Studio, OpenRouter, Anthropic-Compatible, …) | 🟡 OpenAI-compatible | 🟡 Subscription via Claude Code / Codex | 🟡 OpenAI-compatible | | **Retrieval & query pipeline** | ✅ **PPR + Monte Carlo** over `[[wiki-link]]` graph | 🟡 2-hop decay (4-signal heuristic) | 🟡 Louvain community detection | 🟡 BM25 + semantic over chunks | | **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) | ❌ Read-only browser viewer | | **Ingest formats** | ✅ **Markdown + PDF + images + Office (DOCX/PPTX/XLSX)** — one switch flips between native PDF (Anthropic / OpenAI / Bedrock / Gemini) and the built-in MinerU multi-format backend | 🟡 Markdown + PDF | 🟡 Markdown / code files only | 🟡 Markdown only | Three we left off the table: **sdyckjq/llm-wiki-skill** was a Codex skill that 404s today (deleted by author); **atomicstrata** is included even though its retrieval is chunk-based because it's the most active TypeScript alternative; **nashsu** ships the largest user base of the three (10k+ stars, others 2k+) but is a Tauri desktop app, not an Obsidian plugin. ### 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), Gemini, DeepSeek, Qwen, Grok, Kimi, GLM, MiniMax, Step, Hunyuan, MiMo, Gemma, Ollama, LM Studio, OpenRouter, Anthropic-compatible, custom endpoint — sixteen-plus providers, none of them required to have an embedding endpoint. --- ## 🎯 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** — answers come from your vault only, not the internet. - **Need RAG over large external corpora** (Confluence, Notion, arXiv, scraped web pages) — the plugin ingests your vault plus standalone PDF/Office files; bulk external-corpus RAG is out of scope by design. - **Want a hosted SaaS with team collaboration** — there's no backend, no server, no shared state; everything runs locally inside your Obsidian. --- ## 🚀 Quick Start 1. **Install.** Obsidian → Settings → Community plugins → Browse → search "Karpathy LLM Wiki" → Install → Enable. Or visit the [Community Plugin page](https://community.obsidian.md/plugins/karpathywiki) 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` | > 📖 Walkthroughs in [GitHub Discussions → Guides](https://github.com/green-dalii/obsidian-llm-wiki/discussions/categories/guides). Found it useful? [Star the repo](https://github.com/green-dalii/obsidian-llm-wiki) to follow releases. --- ## ✨ 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 tags in Settings → Wiki → Tag Vocabulary → *Custom*. The vocabulary is a schema-injection hint, not a wire-level gate — small/local models may still drift (about one in ten returns the model's built-in taxonomy). Lint surfaces the rest. Design anchor for schema enforcement: [Issue #328](https://github.com/green-dalii/obsidian-llm-wiki/issues/328). ### 📄 Document / PDF / Image ingest Five on-ramps, switchable per ingest: 1. **🆕 Built-in MinerU backend (v1.27.0, #404)** — Settings → Wiki Configuration → Markdown Conversion Backend → *MinerU*. PDF + images (PNG/JPG/JPEG/JP2/WebP/GIF/BMP) + Office (DOC/DOCX/PPT/PPTX/XLS/XLSX) through [MinerU's Precise parser](https://mineru.net/apiManage/docs). Token in Obsidian SecretStorage. Best path for scientific papers, scanned documents, and Office files where layout preservation matters. Server caps: 200 MB / 200 pages per PDF, 256 MB / 10,000 files per archive. 2. **☁️ Cloud providers with native PDF** — Anthropic, OpenAI, Google Gemini, and AWS Bedrock (Anthropic + OpenAI variants) read PDFs as file parts out of the box. No setup beyond provider selection. 3. **🖥️ Local OCR on Apple Silicon** — [oMLX](https://github.com/jundot/omlx) bundles Microsoft Markitdown as a built-in PDF→Markdown backend. Enable Markitdown in oMLX, load [Baidu Unlimited-OCR](https://huggingface.co/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**, pick the multimodal model oMLX is serving. The PDF never leaves your machine. 4. **🛠️ Third-party extractor (MinerU online UI)** — use the [MinerU Extractor online service](https://mineru.net/OpenSourceTools/Extractor) for a quick manual UI when you don't want to wire up an API token. Download the converted `.md`, drop it in your vault outside the wiki folder, and ingest as a regular Markdown note. 5. **🔌 Force PDF Support** — for any other OpenAI/Anthropic-compatible endpoint that accepts file parts, the plugin attempts the call (Settings → LLM Configuration → Advanced). The endpoint decides; failures surface as a localized Notice. **Caveat for Office formats:** Obsidian does not natively render `.docx` / `.xlsx` / `.pptx` ([file-formats](https://obsidian.md/help/file-formats)), so the practical workflow for Office files is: MinerU converts to `.md`, the plugin ingests that `.md` into wiki pages, and the original Office file is kept around for reference. Use a community plugin like Pandoc Plugin / Docxer / Md Importer / Office Reader if you need to inline-preview Office files. **Plumbing shared across all paths:** - **🗄️ Bounded cache** — `.obsidian/plugins/karpathywiki/pdf-cache/` stores converted Markdown keyed by content hash + model + converter version; 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 `.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. - **🔁 Source-page verbatim quotes (v1.27.0, #496)** — every generated `sources/.md` page now carries a `Mentions in Source` section built from the same verbatim quotes the extraction captured per entity/concept (the prose the model already proved it could see), so the underlying document is the one wiki page with a real, grounded trail back to its source text. 📖 **Full setup walkthroughs** for all paths (cloud providers, oMLX hardware tiers, MinerU installation, cache housekeeping) → [docs/PDF-OCR-GUIDE.md](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/docs/PDF-OCR-GUIDE.md) ### 💬 Query & maintenance - **🧭 5-stage PPR cascade** — see [How retrieval works](#-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. - **🆕 Fix Dead Links leave-it outcome (v1.27.0, #485)** — Settings → Advanced → *Create Stubs for Unresolvable Links* (default ON) lets you opt out of empty placeholder pages: when off, the dead link stays visible in every lint report until a real source defines it, and ingest creates pages through normal channels. The never-LLM-expand gate from #197 is unchanged — the new control only governs whether the stub page is *written at all*. - **📊 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. - **🆕 Ingest candidate gate (v1.27.0, #514 / PR #521)** — opt-in toggle (`skipMentionOnlyCandidates`, default off, Settings → Advanced). For sources whose language has a measured profile (de measured; en/fr/es/pt/nl/ko estimated with pinned edge cases; zh/ja character-script thresholds unmeasured), candidates named only inside parentheses / enumerations / short list items are pruned before they cost a page plus dedup and generation calls. Cross-language notes are not gated; wiki languages without a profile report once per ingest and never silently skip. - **🆕 Per-step task policies (v1.27.0, #525 / #490)** — LLM Advanced → Task Policies field; override the per-step text-mode/thinking setting without code changes. Built-in baseline stays intact for steps you don't list. ### 🔒 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 [Document / PDF / Image ingest](#-document--pdf--image-ingest) above. - **🔐 ChatGPT Plan (Codex OAuth)** — desktop loopback or mobile device-code; credentials live only in Obsidian SecretStorage. (See [Anthropic vs OpenAI vs Codex OAuth](#-anthropic-vs-openai-vs-codex-oauth--they-are-distinct-providers) below for the full provider-boundary explanation.) ### 🌐 Language - **🌍 11 UI languages** — English, 简体中文, 繁體中文, 日本語, 한국어, Deutsch, Français, Español, Português, Italiano, Русский. UI and wiki-output language are independent — your wiki can be Chinese while the interface is English. - **📚 11 wiki-output languages** — same set; pick in Settings → Wiki Configuration. *Custom input* option for ad-hoc prompts. - **All UI strings translated per locale** — every label, modal, and notice. Adding a 12th language is contributor-driven (PR #159 pattern). --- ## 🌐 Ecosystem The plugin composes with the rest of your Obsidian stack — each tool below plugs into the `[[wiki-link]]` graph without code changes. - **📄 [MinerU multi-format backend](https://mineru.net/apiManage/docs) (built-in since v1.27.0)** — what used to be a separate CLI/UI step is now a plugin switch; see [Document / PDF / Image ingest](#-document--pdf--image-ingest) for the full path table. The [MinerU online service](https://mineru.net/OpenSourceTools/Extractor) remains available for users who prefer a quick UI over an API token; [self-host MinerU](https://github.com/opendatalab/mineru) is also an option. - **🕸️ 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](https://obsidian.md/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](https://github.com/blacksmithgu/obsidian-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](https://github.com/samuele-cozzi/obsidian-marp)** — 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](https://github.com/AndyMDH/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. --- ## 🧰 Headless CLI **Most users should ignore this section.** The plugin's user-facing CLI lives in the sibling repo [green-dalii/obsidian-llm-wiki-cli](https://github.com/green-dalii/obsidian-llm-wiki-cli) — install with `npm i -g karpathywiki-cli` and run `karpathywiki-cli ingest --sources --wiki --provider --key `. What ships in this repo at `tools/dev-instrument/` is the **dev-only headless measurement instrument** for engine contributors — it runs the real `WikiEngine.ingestSource` against a vault on disk with no Obsidian runtime, prints per-task token + wall-clock accounting — same numbers that drive the perf evidence in CLAUDE.md and release notes. See [`tools/dev-instrument/README.md`](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/tools/dev-instrument/README.md) for the entry command, env vars, measurement modes, and exit-code spec. --- ## 🔍 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](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) 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 stage returned enough signal — no fixed 5-stage cost; no LLM calls when lex is sufficient; semantic fallback only when lex + keyword scan alone isn't enough. ### 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)** (K = walks, L = steps per walk), 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](https://github.com/green-dalii/obsidian-llm-wiki/issues/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. --- ## 🤖 Models **Supported providers (16+, 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 | Native PDF; VPC / compliance path; **API key + SSO + IAM** (v1.27.0, #425) | | **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](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/docs/MODEL-GUIDE.md), cross-checked against [models.dev](https://models.dev/) so the picks stay current. ### What matters - **🧠 Context window ≥ 200K tokens** for vaults over ~500 pages. Below 200K the cascade may drop earlier stages to fit. - **⚖️ 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 16+ providers don't ship one). - **🦙 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. For PDF / image / Office ingest, see [Document / PDF / Image ingest](#-document--pdf--image-ingest) in Features — Anthropic, OpenAI, Bedrock, and Gemini read PDFs as file parts natively; the built-in MinerU backend (v1.27.0+) and **Force PDF Support** cover everything else. ### 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. ### AWS Bedrock — three auth modes (v1.27.0, #425) Settings → Provider → Bedrock (Anthropic / OpenAI) now picks one of three auth modes; the provider row then asks for the inputs that mode actually needs: - **API key** — the original Stage-1 bearer path; behavior is byte-for-byte identical to v1.26.4 and the recommended pick for users who already pay for a Bedrock API key. - **SSO** — IAM Identity Center device flow. Click *Sign in with AWS SSO*, paste the verification URL code in the browser, the plugin receives an SSO token via `karpathywiki-bedrock-sso` in SecretStorage, exchanges it for temporary role credentials, and signs every request with hand-rolled SigV4 (no AWS SDK added). Account ID and role name are auto-detected when the SSO identity exposes exactly one of each; otherwise enter them in the provider settings. - **IAM** — static access keys for environments without SSO (CI, scheduled batch jobs). Stored in `karpathywiki-bedrock-iam` in SecretStorage; the in-memory cache memoizes per access-key to keep SigV4 signing within expiry. All three modes share the same Obsidian SecretStorage discipline (no credentials in `data.json`, logs, or docs) and the same zero-AWS-SDK hand-rolled OIDC + SigV4 path. Bedrock region is independent of auth mode and is configured in the same provider row. > 📖 **Full pick table** (cloud + local + PDF OCR + Codex OAuth + quantization + hardware tiers) → [docs/MODEL-GUIDE.md](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/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](#-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, and v1.27.0 adds native PDF + images + Office ingest without changing the on-disk wiki layout. ### Can I ingest PDFs, images, and Office documents? ✅ Yes. Anthropic, OpenAI, Bedrock, and Gemini read PDFs natively; the built-in MinerU backend (v1.27.0) covers everything else (PDF + images + Office). Full walkthrough — cloud providers, Apple Silicon OCR, Force PDF Support, cache housekeeping — in [docs/PDF-OCR-GUIDE.md](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/docs/PDF-OCR-GUIDE.md). ### 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? 🌍 11 languages for both UI and wiki output. UI and wiki language are independent. Adding a 12th 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](#-models) covers the principles; the full tiered table is in [docs/MODEL-GUIDE.md](https://github.com/green-dalii/obsidian-llm-wiki/blob/main/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-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](https://github.com/green-dalii/obsidian-llm-wiki/issues) for bug reports · [GitHub Discussions](https://github.com/green-dalii/obsidian-llm-wiki/discussions) for questions and feature requests · Developer Console (`Ctrl+Shift+I` / `Cmd+Option+I`) for plugin logs. --- ## 🔒 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. --- ## 💖 Support If LLM-Wiki has become a meaningful part of your knowledge workflow: - ☕ **[Buy me a Ko-fi](https://ko-fi.com/greenerdalii)** — one-time or monthly - 💳 **[Tip via PayPal](https://paypal.me/greenerdalii)** — one-time tip Thanks to the following for supporting the project: [@jameses-cyber](https://github.com/jameses-cyber), [@issaqua](https://github.com/issaqua), Dikson Choi --- ## 🔭 Other projects - **[obsidian-llm-wiki-cli](https://github.com/green-dalii/obsidian-llm-wiki-cli)** — the headless ingest CLI, sibling repo shipped as the `karpathywiki-cli` npm package. Runs the same `WikiEngine` against a vault on disk, no renderer. Install with `npm i -g karpathywiki-cli`. The in-tree `tools/dev-instrument/` is the dev-only measurement instrument that drives the per-task cost numbers in this plugin's release notes. - **[pi-shift-router](https://github.com/green-dalii/pi-shift-router)** — a task-level router for [pi-coding-agent](https://github.com/earendil-works/pi). Before each turn a small LLM judge marks your message routine or consequential, and the tier it picks drives the whole turn. Complex tasks go further: the Smart tier runs as a CTO that plans the work, delegates implementation to Fast subagents, reviews each result and iterates. Upgrades are instant, downgrades wait for a sustained trend; per-tier fallback chains ride out 429s and 5xx. Zero runtime deps, MIT. → [shiftrouter.greenerai.top](https://shiftrouter.greenerai.top) - **[dsh-shift-router](https://github.com/green-dalii/dsh-shift-router)** — DSH fork of pi-shift-router, same task-level routing design but targeted at the [dsh-coding-agent](https://github.com/earendil-works/dsh) runtime. Same judge-driven tier picks, same per-tier fallback chains, MIT. - **[dsh-plugin-dev-skill](https://github.com/green-dalii/dsh-plugin-dev-skill)** — DSH companion to the Claude-side `obsidian-plugin-dev` workflow: scaffolds an Obsidian plugin workspace, drives the Red→Green TDD loop, runs the Six-Gate quality closure (lint/tsc/test/build/css-lint), and prepares a release-ready branch on `feat/*` or `fix/*`. Built so DSH-using contributors get the same scaffolding + gate experience without copy-pasting from CLAUDE.md. --- ## 📜 License & Credits Apache License, Version 2.0 — see [LICENSE](LICENSE) and [NOTICE](NOTICE). **Built on:** - 💡 [Andrej Karpathy's LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) — the original concept - 🛠️ [Obsidian Plugin API](https://docs.obsidian.md/Plugins/Getting+started/Build+a+plugin) - 🔌 [Vercel AI SDK v6](https://ai-sdk.dev/) (`@ai-sdk/openai`, `@ai-sdk/anthropic`, `@ai-sdk/openai-compatible`) via Obsidian `requestUrl` - 🧮 [Personalized PageRank (Haveliwala 2002)](https://www-cs.stanford.edu/~taherh/papers/topic-sensitive-pagerank-tkde.pdf) and [Monte Carlo PPR (Fogaras 2005)](https://www.cs.cmu.edu/~dpelleg/download/pagerank.pdf) — retrieval algorithms **Maintainer:** [@green-dalii](https://github.com/green-dalii) [](https://www.star-history.com/?repos=green-dalii%2Fobsidian-llm-wiki&type=timeline&legend=bottom-right)