A personal knowledge base that builds itself.
LLM reads your documents, builds a structured wiki, and keeps it current.
What is this? • Features • Tech Stack • Installation • Credits • License
English | 中文
- Two-Step Chain-of-Thought Ingest — LLM analyzes first, then generates wiki pages with source traceability and incremental cache
- 4-Signal Knowledge Graph — relevance model with direct links, source overlap, Adamic-Adar, and type affinity
- Louvain Community Detection — automatic knowledge cluster discovery with cohesion scoring
- Graph Insights — surprising connections and knowledge gaps with one-click Deep Research
- Vector Semantic Search — optional embedding-based retrieval via LanceDB, supports any OpenAI-compatible endpoint
- Persistent Ingest Queue — serial processing with crash recovery, cancel, retry, and progress visualization
- Folder Import — recursive folder import preserving directory structure, folder context as LLM classification hint
- Deep Research — LLM-optimized search topics, multi-query web search, auto-ingest results into wiki
- Async Review System — LLM flags items for human judgment, predefined actions, pre-generated search queries
- Chrome Web Clipper — one-click web page capture with auto-ingest into knowledge base
LLM Wiki is a cross-platform desktop application that turns your documents into an organized, interlinked knowledge base — automatically. Instead of traditional RAG (retrieve-and-answer from scratch every time), the LLM incrementally builds and maintains a persistent wiki from your sources. Knowledge is compiled once and kept current, not re-derived on every query.
This project is based on Karpathy's LLM Wiki pattern — a methodology for building personal knowledge bases using LLMs. We implemented the core ideas as a full desktop application with significant enhancements.
The foundational methodology comes from Andrej Karpathy's llm-wiki.md, which describes the pattern of using LLMs to incrementally build and maintain a personal wiki. The original document is an abstract design pattern; this project is a concrete implementation with substantial extensions.
The core architecture follows Karpathy's design faithfully:
- Three-layer architecture: Raw Sources (immutable) → Wiki (LLM-generated) → Schema (rules & config)
- Three core operations: Ingest, Query, Lint
- index.md as the content catalog and LLM navigation entry point
- log.md as the chronological operation record with parseable format
- [[wikilink]] syntax for cross-references
- YAML frontmatter on every wiki page
- Obsidian compatibility — the wiki directory works as an Obsidian vault
- Human curates, LLM maintains — the fundamental role division
The original is an abstract pattern document designed to be copy-pasted to an LLM agent. We built it into a full cross-platform desktop application with:
- Three-column layout: Knowledge Tree / File Tree (left) + Chat (center) + Preview (right)
- Icon sidebar for switching between Wiki, Sources, Search, Graph, Lint, Review, Deep Research, Settings
- Custom resizable panels — drag-to-resize left and right panels with min/max constraints
- Activity panel — real-time processing status showing file-by-file ingest progress
- All state persisted — conversations, settings, review items, project config survive restarts
- Scenario templates — Research, Reading, Personal Growth, Business, General — each pre-configures purpose.md and schema.md
The original has Schema (how the wiki works) but no formal place for why the wiki exists. We added purpose.md:
- Defines goals, key questions, research scope, evolving thesis
- LLM reads it during every ingest and query for context
- LLM can suggest updates based on usage patterns
- Different from schema — schema is structural rules, purpose is directional intent
The original describes a single-step ingest where the LLM reads and writes simultaneously. We split it into two sequential LLM calls for significantly better quality:
Step 1 (Analysis): LLM reads source → structured analysis
- Key entities, concepts, arguments
- Connections to existing wiki content
- Contradictions & tensions with existing knowledge
- Recommendations for wiki structure
Step 2 (Generation): LLM takes analysis → generates wiki files
- Source summary with frontmatter (type, title, sources[])
- Entity pages, concept pages with cross-references
- Updated index.md, log.md, overview.md
- Review items for human judgment
- Search queries for Deep Research
Additional ingest enhancements beyond the original:
- SHA256 incremental cache — source file content is hashed before ingest; unchanged files are skipped automatically, saving LLM tokens and time
- Persistent ingest queue — serial processing prevents concurrent LLM calls; queue persisted to disk, survives app restart; failed tasks auto-retry up to 3 times
- Folder import — recursive folder import preserving directory structure; folder path passed to LLM as classification context (e.g., "papers > energy" helps categorize content)
- Queue visualization — Activity Panel shows progress bar, pending/processing/failed tasks with cancel and retry buttons
- Auto-embedding — when vector search is enabled, new pages are automatically embedded after ingest
- Source traceability — every generated wiki page includes a
sources: []field in YAML frontmatter, linking back to the raw source files that contributed to it - overview.md auto-update — global summary page regenerated on every ingest to reflect the latest state of the wiki
- Guaranteed source summary — fallback ensures a source summary page is always created, even if the LLM omits it
- Language-aware generation — LLM responds in the user's configured language (English or Chinese)
The original mentions [[wikilinks]] for cross-references but has no graph analysis. We built a full knowledge graph visualization and relevance engine:
4-Signal Relevance Model:
| Signal | Weight | Description |
|---|---|---|
| Direct link | ×3.0 | Pages linked via [[wikilinks]] |
| Source overlap | ×4.0 | Pages sharing the same raw source (via frontmatter sources[]) |
| Adamic-Adar | ×1.5 | Pages sharing common neighbors (weighted by neighbor degree) |
| Type affinity | ×1.0 | Bonus for same page type (entity↔entity, concept↔concept) |
Graph Visualization (sigma.js + graphology + ForceAtlas2):
- Node colors by page type or community, sizes scaled by link count (√ scaling)
- Edge thickness and color by relevance weight (green=strong, gray=weak)
- Hover interaction: neighbors stay visible, non-neighbors dim, edges highlight with relevance score label
- Zoom controls (ZoomIn, ZoomOut, Fit-to-screen)
- Position caching prevents layout jumps when data updates
- Legend switches between type counts and community info based on coloring mode
Not in the original. Automatic discovery of knowledge clusters using the Louvain algorithm (graphology-communities-louvain):
- Auto-clustering — discovers which pages naturally group together based on link topology, independent of predefined page types
- Type / Community toggle — switch between coloring nodes by page type (entity, concept, source...) or by discovered knowledge cluster
- Cohesion scoring — each community scored by intra-edge density (actual edges / possible edges); low-cohesion clusters (< 0.15) flagged with warning
- 12-color palette — distinct visual separation between clusters
- Community legend — shows top node label, member count, and cohesion per cluster
Not in the original. The system automatically analyzes graph structure to surface actionable insights:
Surprising Connections:
- Detects unexpected relationships: cross-community edges, cross-type links, peripheral↔hub couplings
- Composite surprise score ranks the most noteworthy connections
- Dismissable — mark connections as reviewed so they don't reappear
Knowledge Gaps:
- Isolated pages (degree ≤ 1) — pages with few or no connections to the rest of the wiki
- Sparse communities (cohesion < 0.15, ≥ 3 pages) — knowledge areas with weak internal cross-references
- Bridge nodes (connecting 3+ clusters) — critical junction pages that hold multiple knowledge areas together
Interactive:
- Click any insight card to highlight corresponding nodes and edges in the graph; click again to deselect
- Knowledge gaps and bridge nodes have a Deep Research button — triggers LLM-optimized research with domain-aware topics (reads overview.md + purpose.md for context)
- Research topic shown in editable confirmation dialog before starting — user can refine topic and search queries
The original describes a simple query where the LLM reads relevant pages. We built a multi-phase retrieval pipeline with optional vector search and budget control:
Phase 1: Tokenized Search
- English: word splitting + stop word removal
- Chinese: CJK bigram tokenization (每个 → [每个, 个…])
- Title match bonus (+10 score)
- Searches both wiki/ and raw/sources/
Phase 1.5: Vector Semantic Search (optional)
- Embedding via any OpenAI-compatible /v1/embeddings endpoint
- Stored in LanceDB (Rust backend) for fast ANN retrieval
- Cosine similarity finds semantically related pages even without keyword overlap
- Results merged into search: boosts existing matches + adds new discoveries
Phase 2: Graph Expansion
- Top search results used as seed nodes
- 4-signal relevance model finds related pages
- 2-hop traversal with decay for deeper connections
Phase 3: Budget Control
- Configurable context window: 4K → 1M tokens
- Proportional allocation: 60% wiki pages, 20% chat history, 5% index, 15% system
- Pages prioritized by combined search + graph relevance score
Phase 4: Context Assembly
- Numbered pages with full content (not just summaries)
- System prompt includes: purpose.md, language rules, citation format, index.md
- LLM instructed to cite pages by number: [1], [2], etc.
Vector Search is fully optional — disabled by default, enabled in Settings with independent endpoint, API key, and model configuration. When disabled, the pipeline falls back to tokenized search + graph expansion. Benchmark: overall recall improved from 58.2% to 71.4% with vector search enabled.
The original has a single query interface. We built full multi-conversation support:
- Independent chat sessions — create, rename, delete conversations
- Conversation sidebar — quick switching between topics
- Per-conversation persistence — each conversation saved to
.llm-wiki/chats/{id}.json - Configurable history depth — limit how many messages are sent as context (default: 10)
- Cited references panel — collapsible section on each response showing which wiki pages were used, grouped by type with icons
- Reference persistence — cited pages stored directly in message data, stable across restarts
- Regenerate — re-generate the last response with one click (removes last assistant + user message pair, re-sends)
- Save to Wiki — archive valuable answers to
wiki/queries/, then auto-ingest to extract entities/concepts into the knowledge network
Not in the original. For LLMs that emit <think> blocks (DeepSeek, QwQ, etc.):
- Streaming thinking — rolling 5-line display with opacity fade during generation
- Collapsed by default — thinking blocks hidden after completion, click to expand
- Visual separation — thinking content shown in distinct style, separate from the main response
Not in the original. Full LaTeX math support across all views:
- KaTeX rendering — inline
$...$and block$$...$$formulas rendered via remark-math + rehype-katex - Milkdown math plugin — preview editor renders math natively via @milkdown/plugin-math
- Auto-detection — bare
\begin{aligned}and other LaTeX environments automatically wrapped with$$delimiters - Unicode fallback — 100+ symbol mappings (α, ∑, →, ≤, etc.) for simple inline notation outside math blocks
The original suggests staying involved during ingest. We added an asynchronous review queue:
- LLM flags items needing human judgment during ingest
- Predefined action types: Create Page, Deep Research, Skip — constrained to prevent LLM hallucination of arbitrary actions
- Search queries generated at ingest time — LLM pre-generates optimized web search queries for each review item
- User handles reviews at their convenience — doesn't block ingest
Not in the original. When the LLM identifies knowledge gaps:
- Web search (Tavily API) finds relevant sources with full content extraction (no truncation)
- Multiple search queries per topic — LLM-generated at ingest time, optimized for search engines
- LLM-optimized research topics — when triggered from Graph Insights, LLM reads overview.md + purpose.md to generate domain-specific topics and queries (not generic keywords)
- User confirmation dialog — editable topic and search queries shown for review before research starts
- LLM synthesizes findings into a wiki research page with cross-references to existing wiki
- Thinking display —
<think>blocks shown as collapsible sections during synthesis, auto-scroll to latest content - Auto-ingest — research results automatically processed to extract entities/concepts into the wiki
- Task queue with 3 concurrent tasks
- Research Panel — dedicated sidebar panel with dynamic height, real-time streaming progress
The original mentions Obsidian Web Clipper. We built a dedicated Chrome Extension (Manifest V3):
- Mozilla Readability.js for accurate article extraction (strips ads, nav, sidebars)
- Turndown.js for HTML → Markdown conversion with table support
- Project picker — choose which wiki to clip into (supports multi-project)
- Local HTTP API (port 19827, tiny_http) — Extension ↔ App communication
- Auto-ingest — clipped content automatically triggers the two-step ingest pipeline
- Clip watcher — polls every 3 seconds for new clips, processes automatically
- Offline preview — shows extracted content even when app is not running
The original focuses on text/markdown. We support structured extraction preserving document semantics:
| Format | Method |
|---|---|
| pdf-extract (Rust) with file caching | |
| DOCX | docx-rs — headings, bold/italic, lists, tables → structured Markdown |
| PPTX | ZIP + XML — slide-by-slide extraction with heading/list structure |
| XLSX/XLS/ODS | calamine — proper cell types, multi-sheet support, Markdown tables |
| Images | Native preview (png, jpg, gif, webp, svg, etc.) |
| Video/Audio | Built-in player |
| Web clips | Readability.js + Turndown.js → clean Markdown |
The original has no deletion mechanism. We added intelligent cascade deletion:
- Deleting a source file removes its wiki summary page
- 3-method matching finds related wiki pages: frontmatter
sources[]field, source summary page name, frontmatter section references - Shared entity preservation — entity/concept pages linked to multiple sources only have the deleted source removed from their
sources[]array, not deleted entirely - Index cleanup — removed pages are purged from index.md
- Wikilink cleanup — dead
[[wikilinks]]to deleted pages are removed from remaining wiki pages
Not in the original. Users can configure how much context the LLM receives:
- Slider from 4K to 1M tokens — adapts to different LLM capabilities
- Proportional budget allocation — larger windows get proportionally more wiki content
- 60/20/5/15 split — wiki pages / chat history / index / system prompt
The original is platform-agnostic (abstract pattern). We handle concrete cross-platform concerns:
- Path normalization — unified
normalizePath()used across 22+ files, backslash → forward slash - Unicode-safe string handling — char-based slicing instead of byte-based (prevents crashes on CJK filenames)
- macOS close-to-hide — close button hides window (app stays running in background), click dock icon to restore, Cmd+Q to quit
- Windows/Linux close confirmation — confirmation dialog before quitting to prevent accidental data loss
- Tauri v2 — native desktop on macOS, Windows, Linux
- GitHub Actions CI/CD — automated builds for macOS (ARM + Intel), Windows (.msi), Linux (.deb / .AppImage)
- i18n — English + Chinese interface (react-i18next)
- Settings persistence — LLM provider, API key, model, context size, language saved via Tauri Store
- Obsidian config — auto-generated
.obsidian/directory with recommended settings - Markdown rendering — GFM tables with borders, proper code blocks, wikilink processing in chat and preview
- Multi-provider LLM support — OpenAI, Anthropic, Google, Ollama, Custom — each with provider-specific streaming and headers
- 15-minute timeout — long ingest operations won't fail prematurely
- dataVersion signaling — graph and UI automatically refresh when wiki content changes
| Layer | Technology |
|---|---|
| Desktop | Tauri v2 (Rust backend) |
| Frontend | React 19 + TypeScript + Vite |
| UI | shadcn/ui + Tailwind CSS v4 |
| Editor | Milkdown (ProseMirror-based WYSIWYG) |
| Graph | sigma.js + graphology + ForceAtlas2 |
| Search | Tokenized search + graph relevance + optional vector (LanceDB) |
| Vector DB | LanceDB (Rust, embedded, optional) |
| pdf-extract | |
| Office | docx-rs + calamine |
| i18n | react-i18next |
| State | Zustand |
| LLM | Streaming fetch (OpenAI, Anthropic, Google, Ollama, Custom) |
| Web Search | Tavily API |
Download from Releases:
- macOS:
.dmg(Apple Silicon + Intel) - Windows:
.msi - Linux:
.deb/.AppImage
# Prerequisites: Node.js 20+, Rust 1.70+
git clone https://github.com/nashsu/llm_wiki.git
cd llm_wiki
npm install
npm run tauri dev # Development
npm run tauri build # Production build- Open
chrome://extensions - Enable "Developer mode"
- Click "Load unpacked"
- Select the
extension/directory
- Launch the app → Create a new project (choose a template)
- Go to Settings → Configure your LLM provider (API key + model)
- Go to Sources → Import documents (PDF, DOCX, MD, etc.)
- Watch the Activity Panel — LLM automatically builds wiki pages
- Use Chat to query your knowledge base
- Browse the Knowledge Graph to see connections
- Check Review for items needing your attention
- Run Lint periodically to maintain wiki health
my-wiki/
├── purpose.md # Goals, key questions, research scope
├── schema.md # Wiki structure rules, page types
├── raw/
│ ├── sources/ # Uploaded documents (immutable)
│ └── assets/ # Local images
├── wiki/
│ ├── index.md # Content catalog
│ ├── log.md # Operation history
│ ├── overview.md # Global summary (auto-updated)
│ ├── entities/ # People, organizations, products
│ ├── concepts/ # Theories, methods, techniques
│ ├── sources/ # Source summaries
│ ├── queries/ # Saved chat answers + research
│ ├── synthesis/ # Cross-source analysis
│ └── comparisons/ # Side-by-side comparisons
├── .obsidian/ # Obsidian vault config (auto-generated)
└── .llm-wiki/ # App config, chat history, review items
This project is licensed under the GNU General Public License v3.0 — see LICENSE for details.
