交易评论维基 · Trading Review Wiki

An LLM-powered knowledge base designed for traders.
Automatically compound your trading understanding over time.

📬 Author WeChat Public Account: ymj0418 • Welcome to submit bugs and feedback

Features • Quick Start • Tech Stack • Installation • Credits • License

English | 中文

---

This project is based on Andrej Karpathy's LLM Wiki pattern, with heavy enhancements for stock trading review workflows, including Chinese templates, trade order import, FIFO P&L calculation, quick review templates, and image support.

Features

Trading-Specific

• Quick Review Template — One-click daily trading review creation, auto-saved to raw/日复盘/
• Trade Order Import — CSV / Excel broker statement import with automatic header detection
• FIFO P&L Calculation — Realized profit/loss via FIFO, auto-appended to daily reviews
• Stock Archives — LLM routes stock insights to wiki/股票/ instead of generic entities/
• Image Support — Send screenshots in chat, preview PNG / JPG / GIF in the preview panel
• Theme Switching — 5 preset color themes (Midnight Blue, Forest Green, Deep Plum, Warm Amber) for different trading moods
• Wiki Reorganize Script — Bulk-move misplaced root-level pages into typed directories. Handy when LLM puts files in the wrong place. Script · When to use & How to use

Knowledge Management

• Two-Step Chain-of-Thought Ingest — LLM analyzes first, then writes wiki pages
• 4-Signal Knowledge Graph — Direct links, source overlap, Adamic-Adar, type affinity
• Louvain Community Detection — Auto-discover knowledge clusters
• Vector Semantic Search — Optional embedding-based retrieval via LanceDB
• Chrome Web Clipper — One-click web capture with auto-ingest

Quick Start

1. Launch the app → Create a new project (choose "交易复盘" template)
2. Go to Settings → Configure LLM provider (API Key + model)
3. Click 🖊️ Quick Review in the sidebar → generate and fill today's review
4. Go to Sources → Import trade statements (CSV / XLSX)
5. Ask the LLM questions in Chat based on your trading records
6. Click Save to Wiki on valuable replies to archive them

Downloads

• Windows: Trading Review Wiki_0.6.8_x64-setup.exe
• macOS: Download .dmg from GitHub Releases (Apple Silicon)
  • ⚠️ 注意：当前版本未进行 Apple 代码签名，首次打开可能提示"已损坏"。绕过方法：终端执行 xattr -c /Applications/Trading\ Review\ Wiki.app，或右键 → 打开。

Tech Stack

• Desktop: Tauri v2 (Rust backend)
• Frontend: React 19 + TypeScript + Vite
• UI: shadcn/ui + Tailwind CSS v4
• Editor: Milkdown
• Graph: sigma.js + graphology + ForceAtlas2
• Vector DB: LanceDB

Credits

The foundational methodology comes from Andrej Karpathy's llm-wiki.md.

License

This project is licensed under the GNU General Public License v3.0 — see LICENSE.

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

---

Features

• 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
• Quick Review Template — one-click daily trading review creation, auto-saved to raw/daily-reviews/
• Trade Order Import — CSV/XLSX trade order import with automatic FIFO P&L calculation
• Wiki Reorganize Script — batch-fix pages that LLM dropped in the wiki root instead of the correct typed folder (script · docs)
• Manual Save Button — explicit save button in the wiki editor for immediate persistence

What is this?

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.

Credits

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.

What We Kept from the Original

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

What We Changed & Added

1. From CLI to Desktop Application

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

2. Purpose.md — The Wiki's Soul

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

3. Two-Step Chain-of-Thought Ingest

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)

4. Knowledge Graph with Relevance Model

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

5. Louvain Community Detection

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

6. Graph Insights — Surprising Connections & Knowledge Gaps

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

7. Optimized Query Retrieval Pipeline

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.

8. Multi-Conversation Chat with Persistence

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

9. Thinking / Reasoning Display

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

10. KaTeX Math Rendering

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

11. Review System (Async Human-in-the-Loop)

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

12. Deep Research

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
• Human-in-the-Loop Review — research synthesis pauses at pending_review state; users can preview the clean draft (with <think> blocks stripped), then choose to Save to Wiki, Regenerate, or Discard
• Human-in-the-Loop Review — research synthesis pauses at pending_review state; users can preview the clean draft (with <think> blocks stripped), then choose to Save to Wiki, Regenerate, or Discard
• 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

13. Browser Extension (Web Clipper)

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

14. Multi-format Document Support

The original focuses on text/markdown. We support structured extraction preserving document semantics:

Format	Method
PDF	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

15. File Deletion with Cascade Cleanup

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

16. Configurable Context Window

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

17. Cross-Platform Compatibility

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)

18. Trading Review Enhancements

Dedicated optimizations for stock trading review workflows:

• Quick Review Template — sidebar one-click creation of today's trading review, based on a built-in template covering operations, market environment, mindset/discipline, key reflections, and tomorrow's plan; saved to raw/日复盘/YYYY-MM-DD-复盘.md
• Trade Order Import & FIFO P&L — supports CSV, XLSX, and XLS broker statements; auto-detects headers; generates daily trade markdowns; calculates realized P&L via FIFO and appends summaries to daily reviews
• Excel Float Precision Safety — Rust backend preserves 4 decimal places when parsing Excel floats, avoiding IEEE-754 truncation errors in financial values
• Manual Save Button — explicit "Save" button in the wiki editor title bar for immediate persistence, supplementing auto-save
• LLM Save Guidance — system prompt explicitly informs the LLM that every assistant reply has a "Save to Wiki" button; when the user asks to write/save/generate reflections, the LLM outputs full markdown and guides the user to click the button

19. Security Enhancements

• Clip Server Token Auth — local clip server (127.0.0.1:19827) generates a 32-byte random token on startup; all API endpoints require X-Clip-Token validation to prevent CSRF injection from malicious web pages

20. Other Additions

• 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

Tech Stack

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	pdf-extract
Office	docx-rs + calamine
i18n	react-i18next
State	Zustand
LLM	Streaming fetch (OpenAI, Anthropic, Google, Ollama, Custom)
Web Search	Tavily API

Installation

Pre-built Binaries

Download from Releases:

• macOS: .dmg (Apple Silicon + Intel)
• Windows: .msi
• Linux: .deb / .AppImage

Build from Source

# 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

Chrome Extension

1. Open chrome://extensions
2. Enable "Developer mode"
3. Click "Load unpacked"
4. Select the extension/ directory

Quick Start

1. Launch the app → Create a new project (choose a template)
2. Go to Settings → Configure your LLM provider (API key + model)
3. Go to Sources → Import documents (PDF, DOCX, MD, etc.)
4. Watch the Activity Panel — LLM automatically builds wiki pages
5. Use Chat to query your knowledge base
6. Browse the Knowledge Graph to see connections
7. Check Review for items needing your attention
8. Run Lint periodically to maintain wiki health

Project Structure

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

Star History

License

This project is licensed under the GNU General Public License v3.0 — see LICENSE for details.