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<text class="title">llm-wiki-sync — 从零散笔记到结构化 Wiki</text>
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<text class="box-title" x="780" y="38">2. LLM Extraction</text>
<text class="box-body" x="780" y="70">Summary (24 sentences)</text>
<text class="box-body" x="780" y="98">Key Claims · Quotes · Concepts · Entities</text>
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<text class="datasrc">Data sources: Karpathy gist (LLM Wiki), SamurAI llm-wiki-agent (github.com/SamurAIGPT/llm-wiki-agent), Quartz (jackyzha0/quartz)</text>
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<text class="caption" x="20" y="80">Caption: Chalkboard-style infographic summarizing llm-wiki-sync — an automated pipeline to convert scattered notes (raw/) into structured wiki pages, entities, and a graph for long-term reuse.</text>
<text class="datasrc" x="20" y="120">Annotations: include source links in wiki: https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f; https://github.com/SamurAIGPT/llm-wiki-agent; https://github.com/jackyzha0/quartz</text>
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---
title: "llm-wiki-sync Circular Flow"
topic: technical
data_type: cycle
complexity: moderate
point_count: 7
source_language: zh
user_language: en
---
## Main Topic
A cyclical knowledge management pipeline that transforms raw notes into structured wiki pages through continuous LLM-powered ingestion, extraction, and reuse.
## Learning Objectives
After viewing this infographic, the viewer will understand:
1. The continuous circular flow of llm-wiki-sync from raw notes to reusable knowledge
2. The key extraction outputs: Summary, Claims, Entities, Concepts, Connections
3. How feedback and reuse complete the cycle back to new raw material
## Target Audience
- **Knowledge Level**: Intermediate technical audience
- **Context**: Developers and knowledge workers interested in AI-powered knowledge management
- **Expectations**: Clear understanding of the llm-wiki-sync pipeline and its cyclical nature
## Content Type Analysis
- **Data Structure**: Cyclic process with recurring steps
- **Key Relationships**: Raw → Ingest → Extract → Source Page → Graph/Site → Reuse → Raw (feedback loop)
- **Visual Opportunities**: Circular flow with nodes for each stage, arrows showing direction, center concept
## Key Data Points (Verbatim)
### Core Pipeline Steps
1. **Raw Note** - Original document in raw/ folder
2. **Ingest** - LLM analyzes and extracts structured information
3. **Extract** - Summary, Claims, Quotes, Entities, Concepts, Connections
4. **Source Page** - Structured wiki/sources/ page with frontmatter
5. **Graph & Site** - graph.json and Quartz static site generation
6. **Reuse** - Synthesize, query, create new content from structured knowledge
7. **Feedback Loop** - New raw notes created from reused knowledge
### Extraction Outputs
- **Summary**: 核心主题, 问题域, 方法/机制, 结论/价值
- **Key Claims**: Verifiable assertions extracted from text
- **Key Entities**: LaunchDarkly, HP, Christian Dior, etc.
- **Key Concepts**: RTO, RPO, Feature Flag, Kill Switch, Gradual Rollout
- **Connections**: depends_on, enables, provides relationships
### Key Quotes
- "RTO is about speed: how fast you get back online. RPO is about data: how much you can afford to lose."
- "Deploy != Release. Feature flags change this. You can deploy code to production without releasing it to users."
## Layout × Style Signals
- Content type: cycle → circular-flow
- Tone: technical educational → chalkboard
- Audience: developers → clear, legible, professional
- Complexity: moderate → balanced density with clear visual hierarchy
## Design Instructions (from user input)
- **Layout**: circular-flow (NOT linear - must emphasize recurring cycle)
- **Style**: chalkboard (dark background, hand-drawn chalk accents)
- **Aspect**: 16:9 landscape
- **Language**: English
- Circular flow showing: raw note -> ingest -> extract -> source page -> graph/static site -> reuse/feedback -> knowledge base
- Include core extraction outputs as recurring nodes/callouts
- Keep text concise and legible
- Dark chalkboard background with hand-drawn chalk accents
- Avoid clutter, make cycle visually clear and publication-ready
## Recommended Combinations
1. **circular-flow + chalkboard** (Recommended): Perfect match for cycle/process content with chalkboard aesthetic
2. **hub-spoke + technical-schematic**: For emphasizing central concepts with technical precision
3. **bento-grid + craft-handmade**: For multiple topic overview with friendly handmade feel

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Create a professional infographic following these specifications:
## Image Specifications
- **Type**: Infographic
- **Layout**: circular-flow (Cyclic process showing continuous or recurring steps)
- **Style**: chalkboard (Black chalkboard background with colorful chalk drawing style)
- **Aspect Ratio**: 16:9
- **Language**: English
## Core Principles
- Follow the layout structure precisely for information architecture
- Apply style aesthetics consistently throughout
- If content involves sensitive or copyrighted figures, create stylistically similar alternatives
- Keep information concise, highlight keywords and core concepts
- Use ample whitespace for visual clarity
- Maintain clear visual hierarchy
## Text Requirements
- All text must match the specified style treatment
- Main titles should be prominent and readable
- Key concepts should be visually emphasized
- Labels should be clear and appropriately sized
- Use the specified language for all text content
## Layout Guidelines
- Circular arrangement
- Steps around the circle
- Arrows showing direction
- No clear start/end (continuous)
- Center can hold main concept
- Circle or ring shape
- Directional arrows
- Step nodes evenly spaced
- Icons per step
- Optional center element
## Style Guidelines
- **Background**: Chalkboard Black (#1A1A1A) or Dark Green-Black (#1C2B1C)
- **Texture**: Realistic chalkboard texture with subtle scratches, dust particles, and faint eraser marks
- **Typography**: Hand-drawn chalk lettering style with visible chalk texture. Imperfect baseline adds authenticity.
- **Color Palette**:
- Background: Chalkboard Black (#1A1A1A)
- Primary Text: Chalk White (#F5F5F5)
- Accent 1: Chalk Yellow (#FFE566)
- Accent 2: Chalk Pink (#FF9999)
- Accent 3: Chalk Blue (#66B3FF)
- Accent 4: Chalk Green (#90EE90)
- Accent 5: Chalk Orange (#FFB366)
- **Visual Elements**:
- Hand-drawn chalk illustrations with sketchy, imperfect lines
- Chalk dust effects around text and key elements
- Doodles: stars, arrows, underlines, circles, checkmarks
- Eraser smudges and chalk residue textures
- Wooden frame border optional
- Stick figures and simple icons
- Connection lines with hand-drawn feel
- **Style Rules**:
- Maintain authentic chalk texture on all elements
- Use imperfect, hand-drawn quality throughout
- Add subtle chalk dust and smudge effects
- Create visual hierarchy with color variety
- Include playful doodles and annotations
- DO NOT use perfect geometric shapes
- DO NOT create clean digital-looking lines
---
Generate the infographic based on the content below:
# llm-wiki-sync: Turning Scattered Notes into a Reusable Knowledge Base
## Overview
A cyclical pipeline showing how raw notes are continuously transformed through LLM-powered ingestion into structured wiki pages, then feedback into the knowledge base for reuse.
## The Knowledge Pipeline Cycle (Center Concept)
The llm-wiki-sync pipeline operates as a continuous cycle, not a linear process.
7 stages in the cycle: Raw Note → Ingest → Extract → Source Page → Graph/Site → Reuse → Feedback Loop
## Circular Flow Diagram with 7 Stages:
1. **Raw Note** (Stage 1)
- Original documents stored in raw/ folder
- Contains unprocessed information awaiting structure
- Icon: Stack of paper/note icon
2. **Ingest** (Stage 2)
- LLM analyzes and extracts structured information
- Hermes skill triggers Claude Code for ingestion
- Context check against wiki/index.md prevents duplicates
- Icon: Brain/processing icon
3. **Extract** (Stage 3)
- Six key elements extracted from each document:
- Summary (核心主题, 问题域, 方法/机制, 结论/价值)
- Key Claims (Verifiable assertions)
- Key Quotes (Preserved citations)
- Key Entities (LaunchDarkly, HP, etc.)
- Key Concepts (RTO, RPO, Feature Flag, etc.)
- Connections (depends_on, enables, provides)
- Icon: Six circles/callouts
4. **Source Page** (Stage 4)
- Written to wiki/sources/<slug>.md
- Contains frontmatter and standard sections
- Links use [[PageName]] format
- Icon: Document/page icon
5. **Graph & Site** (Stage 5)
- graph.json: Machine-readable graph structure
- graph.html: Interactive visualization
- Quartz: Static site generation
- Icon: Network/graph icon
6. **Reuse** (Stage 6)
- Query, Synthesize, Write, Connect
- Icon: Multiple arrows pointing outward
7. **Feedback Loop** (Stage 7)
- New insights become new raw notes
- Cycle continues indefinitely
- Icon: Circular arrow completing the cycle
## Key Quotes (to include as callouts):
- "RTO is about speed: how fast you get back online. RPO is about data: how much you can afford to lose."
- "Deploy != Release. Feature flags change this. You can deploy code to production without releasing it to users."
## Design Requirements:
- Circular flow with 7 stages evenly spaced around a circle
- Clockwise arrow direction
- Center contains: "llm-wiki-sync" as main concept
- Each stage is a node with icon + label
- Extraction outputs (6 items) shown as callouts or inner ring
- Dark chalkboard background with hand-drawn chalk accents
- Chalk colors for visual hierarchy
- Imperfect, sketchy lines throughout
- Publication-ready quality
- NO clutter - only essential elements
- Clear hierarchy: title > headlines > labels > descriptions
## Text Labels (in English):
- Headline: "The Knowledge Pipeline Cycle"
- Subhead: "How llm-wiki-sync transforms scattered notes into a reusable knowledge base"
- Stage labels: "Raw Note", "Ingest", "Extract", "Source Page", "Graph & Site", "Reuse", "Feedback"
- Center: "llm-wiki-sync"
- Extraction labels: "Summary", "Claims", "Quotes", "Entities", "Concepts", "Connections"
## Key Constraints:
- 16:9 aspect ratio (landscape)
- All text in English
- Chalkboard style (dark background, chalk-like hand-drawn elements)
- Circular flow layout (NOT linear)
- Publication-ready, visually clear
- No clutter or excessive elements

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# llm-wiki-sync: Turning Scattered Notes into a Reusable Knowledge Base
## Overview
A cyclical pipeline showing how raw notes are continuously transformed through LLM-powered ingestion into structured wiki pages, then feedback into the knowledge base for reuse.
## Learning Objectives
The viewer will understand:
1. The continuous circular flow of llm-wiki-sync from raw notes to reusable knowledge
2. The six key extraction outputs: Summary, Claims, Quotes, Entities, Concepts, Connections
3. How feedback and reuse complete the cycle back to new raw material
---
## Section 1: The Circular Flow (Center Concept)
**Key Concept**: The llm-wiki-sync pipeline operates as a continuous cycle, not a linear process.
**Content**:
- 7 stages in the cycle: Raw Note → Ingest → Extract → Source Page → Graph/Site → Reuse → Feedback Loop
- Each stage feeds into the next, with feedback returning to the beginning
- The cycle is continuous and self-reinforcing
**Visual Element**:
- Type: circular flow diagram
- Subject: 7 stages arranged in a circle with clockwise arrows
- Center label: "llm-wiki-sync Cycle"
- Treatment: chalk style with hand-drawn arrows connecting stages
**Text Labels**:
- Headline: "The Knowledge Pipeline Cycle"
- Stage labels: "Raw Note", "Ingest", "Extract", "Source Page", "Graph/Site", "Reuse", "Feedback"
- Center: "llm-wiki-sync"
---
## Section 2: Stage 1 — Raw Note (Input)
**Key Concept**: Raw notes are the starting point of the cycle.
**Content**:
- Original documents stored in raw/ folder
- Can be any format: markdown, text, research notes
- Contains unprocessed information awaiting structure
**Visual Element**:
- Type: document/note icon
- Subject: Stack of paper or note icon
- Treatment: Chalk sketch style
**Text Labels**:
- Label: "Raw Note"
- Description: "Original input"
---
## Section 3: Stage 2 — Ingest (LLM Analysis)
**Key Concept**: The LLM analyzes raw notes and extracts structured information.
**Content**:
- Hermes skill triggers Claude Code for ingestion
- LLM reads and analyzes the full document
- Context check against wiki/index.md prevents duplicates
**Visual Element**:
- Type: brain/processing icon
- Subject: Brain or gears with chalk lines
- Treatment: Hand-drawn chalk illustration
**Text Labels**:
- Label: "Ingest"
- Description: "LLM Analysis"
---
## Section 4: Stage 3 — Extract (Six Core Outputs)
**Key Concept**: Six key elements are extracted from each document.
**Content**:
1. **Summary**: 核心主题, 问题域, 方法/机制, 结论/价值
2. **Key Claims**: Verifiable assertions extracted from text
3. **Key Quotes**: Preserved citations for reference
4. **Key Entities**: Named people, companies, products (e.g., LaunchDarkly, HP)
5. **Key Concepts**: Abstract terms that can be reused (e.g., RTO, RPO, Feature Flag)
6. **Connections**: Relationships between elements (depends_on, enables, provides)
**Visual Element**:
- Type: 6 callout nodes around center
- Subject: Six boxes or bubbles representing extraction outputs
- Treatment: Chalk circles with icons inside each
**Text Labels**:
- Headline: "Extraction Outputs"
- Labels: "Summary", "Claims", "Quotes", "Entities", "Concepts", "Connections"
---
## Section 5: Stage 4 — Source Page (Structured Output)
**Key Concept**: Extracted information is written as a structured wiki source page.
**Content**:
- Written to wiki/sources/<slug>.md
- Contains frontmatter (id, title, type, tags, sources, last_updated)
- Standard sections: Summary, Key Claims, Key Quotes, Key Concepts, Key Entities, Connections, Contradictions
- Links use [[PageName]] format for interconnections
**Visual Element**:
- Type: document/page icon
- Subject: Page with visible structure headers
- Treatment: Chalk sketch with text lines
**Text Labels**:
- Label: "Source Page"
- Description: "wiki/sources/*.md"
---
## Section 6: Stage 5 — Graph & Static Site
**Key Concept**: Structured pages generate knowledge graphs and static websites.
**Content**:
- graph.json: Machine-readable graph structure
- graph.html: Interactive visualization
- Quartz: Static site generation for sharing/export
- Connections become edges in the knowledge graph
**Visual Element**:
- Type: network/graph icon
- Subject: Connected nodes representing knowledge graph
- Treatment: Chalk diagram with nodes and edges
**Text Labels**:
- Label: "Graph & Site"
- Description: "graph.json + Quartz"
---
## Section 7: Stage 6 — Reuse (Knowledge Application)
**Key Concept**: Structured knowledge enables multiple reuse scenarios.
**Content**:
- Query: Ask questions against the knowledge base
- Synthesize: Create new content from existing knowledge
- Write: Generate articles, reports from source material
- Connect: Link ideas across different source pages
**Visual Element**:
- Type: multiple arrows pointing outward
- Subject: Reuse scenarios as icons (question, document, pen)
- Treatment: Chalk illustration
**Text Labels**:
- Label: "Reuse"
- Sub-labels: "Query", "Synthesize", "Write", "Connect"
---
## Section 8: Stage 7 — Feedback Loop (Continuous Cycle)
**Key Concept**: Reuse generates new raw notes, completing the cycle.
**Content**:
- New insights from synthesis become new raw notes
- Updated knowledge feeds back to raw/ folder
- Cycle continues indefinitely
- Each iteration strengthens the knowledge base
**Visual Element**:
- Type: circular arrow
- Subject: Feedback loop arrow returning to Raw Note stage
- Treatment: Large chalk arrow completing the circle
**Text Labels**:
- Label: "Feedback Loop"
- Description: "New notes → Cycle repeats"
---
## Data Points (Verbatim)
### Key Quotes
- "RTO is about speed: how fast you get back online. RPO is about data: how much you can afford to lose."
- "Deploy != Release. Feature flags change this. You can deploy code to production without releasing it to users."
### Key Entities
- LaunchDarkly (Feature Flag management platform)
- HP (example enterprise)
- Christian Dior (example case)
### Key Concepts
- RTO (Recovery Time Objective)
- RPO (Recovery Point Objective)
- Feature Flag (特性开关)
- Kill Switch (紧急关闭机制)
- 渐进式发布 (Gradual Rollout)
---
## Design Instructions
### Layout Preferences
- Circular flow with 7 stages evenly spaced around a circle
- Clockwise arrow direction
- Center contains the main concept "llm-wiki-sync"
- Each stage is a node with icon + label
- Extraction outputs (6 items) shown as callouts or inner ring
### Style Preferences
- Chalkboard: Dark background (#1A1A1A)
- Hand-drawn chalk style for all elements
- Chalk colors: white, yellow, pink, blue, green, orange
- Imperfect, sketchy lines throughout
- Chalk dust effects for authenticity
### Text Requirements
- All text in English
- Legible font sizes (minimum 14pt for labels)
- Clear hierarchy: title > headlines > labels > descriptions
- Ample whitespace between stages
### Visual Clarity
- Avoid clutter - only essential elements
- Each stage should be clearly distinguishable
- Arrows should clearly indicate flow direction
- Publication-ready quality

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- 在 Obsidian 中可以直接通过关系图graph view查看笔记间的关联在 llm-wiki-agent 中可以通过 wiki-graph 构建并在 graph.html / graph/graph.json 中可视化展示。
- 我们的实现基于 SamurAI 的 llm-wiki-agent并在其上加入了企业级的同步、审计与 Hermes skill 封装,最终通过 Quartz 静态站把生成的 wiki 内容对外展示与分享。
<<<<<<< Updated upstream:Hermes/xingzhi/用 LLM把零散资料变成可复用的知识库 —— llm-wiki-sync 的实现与示例解析.md
=======
- 通过这种方式,可以很容易地把所有的知识进行结构化与关联化:
- 在 Obsidian 中可以直接通过关系图谱graph view查看笔记之间的连接
- 在 llm-wiki-agent 中可以通过 wiki-graph 构建并可视化关系图谱graph.html / graph/graph.json以便在浏览器中交互式查看所有内容的关联关系。
如果你需要,我可以:
- 现在对 RTO vs RPO 的源文件再跑一遍演示,输出模型中间结果(断言置信度、实体识别置信度、连接候选)与最终写入 wiki 的 diff
- 直接把这篇更新再提交并推到 Git我可以自动 commit & push
---
## Infographic Asset
![llm-wiki-sync Circular Flow Infographic](llm-wiki-sync-circular-flow-infographic.png)
**Infographic**: The Knowledge Pipeline Cycle — circular flow showing how llm-wiki-sync transforms scattered notes into a reusable knowledge base.
- Layout: circular-flow (7-stage cycle)
- Style: chalkboard (dark background, hand-drawn chalk accents)
- Aspect ratio: 16:9
- Prompt file: `infographic/llm-wiki-sync-circular-flow/prompts/infographic.md`
- Image: `llm-wiki-sync-circular-flow-infographic.png`
>>>>>>> Stashed changes:Hermes/xingzhi/llm-wiki-sync_公众号稿.md