/note-system-processor

note system processor

Primary LanguagePython

markdown_processor

需求1

格式化 字幕,代码等,方便输入chatgpt. 生成相关总结内容.

需求2--视频分段笔记

然后根据总结的时间戳生成拆分的视频笔记

视频显示格式转换

html显示的视频转换为autohotkey的视频播放外链. html上方添加autohotkey的播放外链. autohotkey的外链转换成html显示的视频. autohotkey的视频播放外链下方添加html显示的视频.

Book Workflow

  • Download books from zlib.
  • OCR if needed.
  • Categorize books using the CLC classification system based on their ISBN or filename.
  • Determine the book's filename, preferably in English.
  • Create a root directory for book notes based on the book's filename.
  • Initialize the note folder structure.
  • Store PDF files under the 'big assets' subfolder within the 'assets' folder.
  • Add bookmarks to the PDF.
  • Annotate the PDF to emphasize key points.
  • Convert the PDF into plain text.
  • Transform the plain text into markdown.
  • Add headings to the markdown.
  • Use ChatGPT to check spelling and grammar in the markdown.
  • Write summaries for each chapter following the heading structure.
  • Complete the markdown with images, tables, footnotes, endnotes, etc.
  • Integrate the key points into your personal knowledge system.
graph TD
    Node_1["Download Books from zlib"]
    Node_2["Categorize Books Using CLC System Based on ISBN or Filename"]
    Node_3["Determine Book's Filename (Preferably in English)"]
    Node_4["Create Root Directory for Book Notes Based on Filename"]
    Node_4_1["Initialize Note Folder Structure"]
    Node_5["Store PDFs in 'big assets' Subfolder Under 'assets'"]
    Node_6["Add Bookmarks to PDF"]
    Node_7["Annotate PDF for Key Points"]
    Node_8["Convert PDF to Plain Text"]
    Node_9["Transform Plain Text into Markdown"]
    Node_10["Add Headings to Markdown"]
    Node_11["Use ChatGPT for Spelling and Grammar Check in Markdown"]
    Node_12["Write Chapter Summaries Based on Headings"]
    Node_13["Complete Markdown with Images, Tables, Footnotes, Endnotes"]


    Node_1 --> Node_2
    Node_2 --> Node_3
    Node_3 --> Node_4
    Node_4-->Node_4_1
    Node_4_1 --> Node_5
    Node_5 --> Node_6
    Node_6 --> Node_7
    Node_5 --> Node_8
    Node_8 --> Node_9
    Node_9 --> Node_10
    Node_10 --> Node_11
    Node_7 --> Node_12
    Node_11 --> Node_12
    Node_12 --> Node_13
    Node_12 --> output
subgraph output ["Output"]
  Node_6_1702953694["Note Taking (Visual Methods)"] --> Node_7_1702953694[Active Recall]
  Node_7_1702953694 --> Node_8_1702953694[Sleep and Physical Exercise]
  Node_8_1702953694 --> Node_9_1702953694[Feedback and Reflection]
  Node_9_1702953694 --> Node_10_1702953694["Integrate Knowledge into Personal Framework"]
end
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mermaid Workflow

  • Change Node_\d{1,3}timestamp1 to Node_\d{1,3}_timestamp1.
    • This step to prevent the modification of already standardized node names:
  • Change Node\d{1,3} to Node_\d{1,3}.
  • Change Node_\d{1,3} to Node_\d{1,3}_timestamp2.
  • Change Node__\d{1,3}timestamp1 to Node\d{1,3}_timestamp1.
graph TD
    subgraph sub_1["Prevention"]
    Node_1["Node_\d{1,3}_timestamp1"]-->Node_1_1["Node__\d{1,3}_timestamp1"]
    Node_2["Node__\d{1,3}_timestamp1"]-->Node_2_2["Node_\d{1,3}_timestamp1"]
    end
    subgraph sub_2["Convertion"]
    Node_1_1-->Node_3["Node\d{1,3}"]-->Node_4["Node_\d{1,3}"]
    Node_4-->Node_5["Node_\d{1,3}_timestamp2"]-->Node_2
    end
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