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carocut-planner-analysis

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视频素材分析与清点。解析 PDF 文档提取文本/图片/表格,组织原始素材目录,生成结构化的 inventory.yaml 素材清单。

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Material Analysis

Extracts structured content from source materials (PDF, images, videos, audio) and creates an organized inventory for video production planning.

Purpose

Transform user-provided raw materials into structured data that can drive production planning. This step creates the foundation for all subsequent workflow stages.

Supported Input Types

TypeExtensionsExtraction Method
PDF documents.pdfText blocks, images, tables via material decompose tool
Images.png, .jpg, .jpeg, .webpCopied to raws/images/existing/
Videos.mp4, .mov, .webmCopied to raws/videos/
Audio.mp3, .wav, .m4aCopied to raws/audio/
Text.txt, .mdRead and indexed
URLhttp/httpsText, images, tables extracted via web crawling

Output Structure

raws/
  images/
    existing/            # User-provided images (organized)
    crawled/             # Images downloaded from URL
      crawled_img_001.jpg
      crawled_img_002.png
    page_01_img_01.png   # Images extracted from PDF
    page_02_img_01.png
    table_01.png         # Table screenshots from PDF
  data.json              # Structured content from PDF or URL
  content.txt            # Plain text extraction
  inventory.yaml         # Complete material inventory

data.json Format

{
  "stats": {
    "total": 45,
    "pages": 12,
    "by_type": { "text": 30, "title": 5, "image": 7, "table": 3 }
  },
  "elements": [
    { "type": "title", "page": 1, "content": "Introduction" },
    { "type": "text", "page": 1, "content": "Text content from this section..." },
    { "type": "image", "page": 1, "path": "images/img_001.png" },
    { "type": "table", "page": 3, "path": "images/table_001.png", "context": {
      "before": ["Previous text for context..."],
      "after": ["Following text..."]
    }}
  ]
}

inventory.yaml Format

inventory:
  source_files:
    - name: presentation.pdf
      type: pdf
      pages: 12
      extracted: true

  images:
    - path: images/page_01_img_01.png
      source: presentation.pdf
      page: 1
      description: "Architecture diagram"

    - path: images/existing/logo.png
      source: user_provided
      description: "Company logo"

  text_blocks: 30
  tables: 2
  total_images: 7

Workflow

digraph material_analysis {
    "User provides materials" [shape=doublecircle];
    "Create raws/ directory" [shape=box];
    "Has PDF?" [shape=diamond];
    "Decompose PDF" [shape=box];
    "Has URL?" [shape=diamond];
    "Crawl URL" [shape=box];
    "Organize other files" [shape=box];
    "Generate inventory.yaml" [shape=box];
    "Report to user" [shape=box];
    "Proceed to STEP-2" [shape=doublecircle];

    "User provides materials" -> "Create raws/ directory";
    "Create raws/ directory" -> "Has PDF?";
    "Has PDF?" -> "Decompose PDF" [label="yes"];
    "Has PDF?" -> "Has URL?" [label="no"];
    "Decompose PDF" -> "Has URL?";
    "Has URL?" -> "Crawl URL" [label="yes"];
    "Has URL?" -> "Organize other files" [label="no"];
    "Crawl URL" -> "Organize other files";
    "Organize other files" -> "Generate inventory.yaml";
    "Generate inventory.yaml" -> "Report to user";
    "Report to user" -> "Proceed to STEP-2";
}

PDF Decomposition Patterns

When decomposing PDFs, the following extraction patterns apply:

  • Section titles: Detected by font size changes, bold formatting, or structural patterns (e.g., "Chapter X", numbered headings)
  • Images: Extracted at original resolution when possible, saved as page_{NN}_img_{NN}.png
  • Tables: Complex table layouts may not be detected automatically; capture as screenshots saved as table_{NN}.png
  • Figure references: Cross-references between text and images are preserved in data.json section entries
  • Text encoding: UTF-8 expected; non-UTF-8 PDFs may produce encoding errors

Manual Organization (When No PDF)

If user provides only images, videos, or other files without a PDF:

  1. Create directory structure:

    raws/
      images/existing/
      videos/
      audio/
    
  2. Copy files to appropriate directories

  3. Create inventory.yaml manually:

    inventory:
      source_files: []
      images:
        - path: images/existing/diagram.png
          source: user_provided
          description: "User-provided diagram"
      text_blocks: 0
      tables: 0
      total_images: 1
    

Resource Source Definitions

SourceDescriptionAvailability
user_providedFiles directly supplied by the userAvailable in raws/images/existing/
pdf_extractedImages and tables extracted from PDF documentsAvailable after decomposition
retrieveStock images to be fetched from Pexels/PixabayResolved in STEP-4
generateCustom images to be generated via Caro LLM APIResolved in STEP-4
url_crawledImages downloaded from crawled web pagesAvailable after crawling

User Communication

Successful Analysis

材料分析完成。

来源: presentation.pdf (12 页)

提取结果:
  文本块: 30
  图片: 5
  表格: 2

输出目录: raws/
  - data.json (结构化内容)
  - images/ (7 个文件)
  - inventory.yaml (材料清单)

请确认材料是否完整,然后继续下一步。

With User-Provided Files

材料整理完成。

用户提供:
  - 3 张图片 -> raws/images/existing/
  - 1 个视频 -> raws/videos/
  - presentation.pdf (12 页)

PDF 提取结果:
  文本块: 30
  图片: 5

总计: 9 个可用素材

材料清单: raws/inventory.yaml
请确认后继续。

Common Issues

IssueCauseSolution
PDF images blurryLow resolution in source PDFUse higher quality source or request original images
Tables not detectedComplex table layoutExtract tables manually as screenshots
Text encoding errorsNon-UTF8 PDF encodingRe-export PDF with UTF-8 encoding
Missing sectionsTitle pattern mismatchAdjust title detection parameters when invoking decompose tool

Exit Conditions

  • Success: All materials organized, inventory.yaml created, proceed to STEP-2
  • Partial: Some materials processed with warnings, user confirms to proceed
  • Failure: Critical extraction errors, user must provide alternative materials

File Naming Conventions

  • All paths in inventory.yaml are relative to raws/
  • Image filenames follow pattern: page_{NN}_img_{NN}.png
  • Table screenshots follow pattern: table_{NN}.png
  • User-provided files go to corresponding existing/ subdirectories

Incremental Mode

增量模式用于在已有素材库基础上添加新材料,不重新解析已处理的文件。

输入

  • dispatch context 中 amendment 字段指定的新素材文件

执行规则

  1. 读取现有 raws/inventory.yaml,了解已有素材清单
  2. 仅对新增材料执行解析和组织:
    • 新 PDF 文件:执行 decompose,提取内容追加到 data.json
    • 新图片/视频/音频:复制到对应目录
  3. 更新 inventory.yaml,追加新素材条目,保留已有条目不变
  4. 不重新扫描或验证已有产物

产出

  • 更新后的 raws/inventory.yaml
  • 新增文件路径摘要

不执行

  • 不重新解析已存在的 PDF
  • 不重新组织已分类的文件
  • 不覆盖 raws/ 中已有的文件