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This skill goes beyond simple text extraction by understanding document structure, detecting layout regions, and preserving the logical reading order across complex formatting.\n\n## Instructions\n\nWhen a user asks to parse a complex document or extract structured content from a document with tables, figures, or multi-column layouts, follow these steps:\n\n### Step 1: Install docling\n\n```bash\npip install docling\n```\n\n### Step 2: Load and convert the document\n\nUse docling's DocumentConverter to parse the document:\n\n```python\nfrom docling.document_converter import DocumentConverter\n\ndef parse_document(file_path):\n    converter = DocumentConverter()\n    result = converter.convert(file_path)\n    return result\n```\n\nSupported input formats: PDF, DOCX, PPTX, HTML, images (PNG, JPG).\n\n### Step 3: Export to the desired format\n\n**Export as Markdown** (preserves headings, tables, lists):\n\n```python\ndef to_markdown(result):\n    return result.document.export_to_markdown()\n```\n\n**Export as structured JSON** (full document tree):\n\n```python\nimport json\n\ndef to_json(result):\n    doc_dict = result.document.export_to_dict()\n    return json.dumps(doc_dict, indent=2)\n```\n\n**Extract only tables:**\n\n```python\ndef extract_tables(result):\n    tables = []\n    for table in result.document.tables:\n        df = table.export_to_dataframe()\n        tables.append(df)\n    return tables\n```\n\n### Step 4: Handle specific content types\n\n**For tables**: Export each table as a pandas DataFrame or CSV:\n\n```python\nimport pandas as pd\n\ndef save_tables(result, output_dir=\".\"):\n    for i, table in enumerate(result.document.tables):\n        df = table.export_to_dataframe()\n        output_path = f\"{output_dir}\u002Ftable_{i+1}.csv\"\n        df.to_csv(output_path, index=False)\n    return f\"Saved {len(result.document.tables)} tables\"\n```\n\n**For figures**: Extract figure metadata and captions:\n\n```python\ndef extract_figures(result):\n    figures = []\n    for item in result.document.items:\n        if hasattr(item, 'image') and item.image:\n            figures.append({\n                \"caption\": getattr(item, 'caption', 'No caption'),\n                \"page\": getattr(item, 'page_no', None)\n            })\n    return figures\n```\n\n### Step 5: Present the results\n\nFormat output based on user needs:\n- **Full document**: Markdown export with all content preserved\n- **Tables only**: CSV files or formatted markdown tables\n- **Summary**: Document structure outline with section headings and content types found\n- **Specific sections**: Extract targeted parts by heading or page range\n\n## Examples\n\n### Example 1: Parse an academic paper\n\n**User request:** \"Parse this research paper and extract all the tables: paper.pdf\"\n\n**Actions taken:**\n1. Convert paper.pdf using docling\n2. Identify 4 tables across 12 pages\n3. Export each table as CSV\n\n**Output:**\n```\nParsed paper.pdf (12 pages)\n\nDocument structure:\n  - Title: \"Machine Learning Approaches for Climate Prediction\"\n  - Sections: Abstract, Introduction, Methods, Results, Discussion, References\n  - Tables found: 4\n  - Figures found: 6\n\nTables extracted:\n  1. table_1.csv - \"Dataset Characteristics\" (5 cols, 8 rows) - Page 4\n  2. table_2.csv - \"Model Hyperparameters\" (4 cols, 12 rows) - Page 6\n  3. table_3.csv - \"Performance Comparison\" (6 cols, 5 rows) - Page 8\n  4. table_4.csv - \"Ablation Study Results\" (5 cols, 7 rows) - Page 9\n\nAll tables saved to current directory.\n```\n\n### Example 2: Convert a financial report to Markdown\n\n**User request:** \"Convert this quarterly report to Markdown so I can edit it: Q3-report.pdf\"\n\n**Actions taken:**\n1. Parse Q3-report.pdf with docling (handles multi-column layout)\n2. Export as clean Markdown preserving structure\n\n**Output:**\n```\nConverted Q3-report.pdf to Markdown\n\nDocument details:\n  - Pages: 24\n  - Layout: Multi-column (2-column on pages 3-20)\n  - Tables: 8 (all converted to Markdown tables)\n  - Figures: 5 (referenced with captions)\n  - Sections: 12 headings detected\n\nOutput saved to: Q3-report.md (14,200 words)\n\nThe multi-column layout has been linearized into single-column reading order.\nAll tables are preserved as Markdown tables.\n```\n\n### Example 3: Extract structured data from an invoice\n\n**User request:** \"Parse this invoice and give me the line items as JSON\"\n\n**Actions taken:**\n1. Parse invoice.pdf with docling\n2. Identify the main line items table\n3. Export as structured JSON\n\n**Output:**\n```json\n{\n  \"invoice_number\": \"INV-2024-0847\",\n  \"date\": \"2024-11-15\",\n  \"vendor\": \"Acme Supplies Inc.\",\n  \"line_items\": [\n    {\"item\": \"Office Chairs\", \"qty\": 10, \"unit_price\": 299.99, \"total\": 2999.90},\n    {\"item\": \"Standing Desks\", \"qty\": 5, \"unit_price\": 549.00, \"total\": 2745.00},\n    {\"item\": \"Monitor Arms\", \"qty\": 15, \"unit_price\": 89.50, \"total\": 1342.50}\n  ],\n  \"subtotal\": 7087.40,\n  \"tax\": 637.87,\n  \"total\": 7725.27\n}\n```\n\n## Guidelines\n\n- Docling handles complex layouts automatically including multi-column text, nested tables, and mixed content. 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