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This unified representation enables downstream processors to work format-agnostically without reimplementing logic for each input type.","intents":["I need to ingest documents in multiple formats and process them uniformly without writing separate parsing logic for each","I want to build a document processing pipeline that works regardless of whether users upload PDFs, Word docs, or HTML files","I need to extract structured content from diverse document sources for RAG or gen AI applications"],"best_for":["teams building document-agnostic ETL pipelines","developers creating gen AI applications that need to ingest varied document types","enterprises migrating legacy document workflows to modern LLM-powered systems"],"limitations":["PDF parsing accuracy depends on PDF structure and encoding; scanned PDFs without OCR will fail to extract text","DOCX support limited to standard Office formats; complex VBA macros or embedded objects may not parse correctly","HTML parsing assumes well-formed markup; malformed or heavily JavaScript-dependent pages may produce incomplete output","No built-in support for proprietary formats (Excel, PowerPoint, Visio) — requires format-specific extensions"],"requires":["Python 3.8+","pdfplumber library for PDF parsing","python-docx library for DOCX parsing","BeautifulSoup4 for HTML parsing","Optional: pytesseract and Tesseract OCR for scanned PDF text extraction"],"input_types":["PDF files (text-based and scanned)","DOCX files (Microsoft Word)","HTML files and markup","Markdown files","Plain text"],"output_types":["Unified document object model (DoclingDocument)","Structured JSON representation","Markdown with preserved layout","Serialized document tree"],"categories":["data-processing-analysis","document-parsing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-docling__cap_1","uri":"capability://data.processing.analysis.layout.aware.document.segmentation.and.structure.extraction","name":"layout-aware document segmentation and structure extraction","description":"Analyzes document layout using computer vision techniques (likely bounding box detection and spatial analysis) to identify logical document structure including headers, paragraphs, tables, lists, and sections. Preserves spatial relationships and reading order rather than treating documents as flat text, enabling reconstruction of semantic document structure for downstream processing.","intents":["I need to preserve document structure and layout when converting PDFs to markdown or JSON for LLM processing","I want to identify and extract tables, headers, and sections from documents while maintaining their hierarchical relationships","I need to understand the reading order and spatial organization of content for accurate content extraction"],"best_for":["developers building document-to-markdown converters for RAG systems","teams extracting structured data from complex multi-column layouts","applications requiring semantic document understanding beyond raw text extraction"],"limitations":["Layout detection accuracy degrades on scanned documents with poor image quality or unusual fonts","Complex multi-column layouts with irregular spacing may be misinterpreted as separate sections","Requires sufficient visual contrast between content and background; low-contrast PDFs may fail segmentation","No support for detecting visual elements like charts, diagrams, or embedded images beyond text regions"],"requires":["Python 3.8+","Computer vision library (likely OpenCV or similar for bounding box detection)","PDF with embedded text layer (not scanned images)","Sufficient document resolution (minimum 72 DPI recommended)"],"input_types":["PDF files with text layers","DOCX files with formatting metadata","HTML with semantic markup"],"output_types":["Hierarchical document tree with section/paragraph/table nodes","Bounding box coordinates for each element","Reading order sequence","Markdown with preserved heading hierarchy"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-docling__cap_10","uri":"capability://data.processing.analysis.page.level.document.processing.and.analysis","name":"page-level document processing and analysis","description":"Provides page-level access to document structure, enabling processing of individual pages or page ranges. 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Each element is tagged with its type, enabling downstream processors to handle different content types appropriately. Classification is based on visual properties and structural patterns.","intents":["I want to identify different content types (headings, lists, tables) in documents for selective processing","I need to apply different formatting or processing rules based on content type","I want to extract specific content types (e.g., all code blocks or tables) from documents"],"best_for":["applications requiring content-type-aware processing","systems extracting specific content types from mixed documents","developers building document analysis tools that need semantic understanding"],"limitations":["Classification accuracy depends on document formatting consistency; poorly formatted documents may have misclassified elements","Ambiguous elements (e.g., formatted text that looks like a heading but isn't) may be misclassified","Custom or unusual content types may not be recognized","No support for semantic understanding beyond visual/structural patterns"],"requires":["Python 3.8+","docling package","Well-formatted documents with consistent styling"],"input_types":["Parsed documents in unified representation"],"output_types":["Content elements with type tags","Filtered content by type","Type-specific metadata"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-docling__cap_2","uri":"capability://data.processing.analysis.table.detection.and.structured.extraction","name":"table detection and structured extraction","description":"Identifies table regions within documents using layout analysis and extracts table content into structured formats (JSON, CSV, or markdown). Handles table cell detection, row/column identification, and cell content extraction while preserving table relationships and metadata. Supports both simple and complex tables with merged cells or irregular structures.","intents":["I need to extract tables from PDFs and convert them to CSV or JSON for data analysis","I want to preserve table structure when converting documents to markdown for LLM processing","I need to identify and extract tabular data from mixed-content documents without manual intervention"],"best_for":["data analysts extracting tables from research papers or financial documents","teams building document-to-database pipelines","developers creating RAG systems that need to preserve tabular data structure"],"limitations":["Complex tables with merged cells, nested headers, or irregular layouts may have extraction errors","Tables in scanned PDFs without OCR will not have extractable text content","Very large tables (100+ columns) may exceed processing memory or produce malformed output","No support for detecting implicit tables (data arranged in columns without visible borders)"],"requires":["Python 3.8+","PDF with text layer or DOCX with table markup","Table detection model or heuristics (bounding box analysis)","Optional: table structure recognition model for complex layouts"],"input_types":["PDF files containing tables","DOCX files with table objects","HTML tables"],"output_types":["JSON with table structure and cell contents","CSV format","Markdown table syntax","Structured table object with row/column metadata"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-docling__cap_3","uri":"capability://data.processing.analysis.document.to.markdown.conversion.with.layout.preservation","name":"document-to-markdown conversion with layout preservation","description":"Converts parsed documents to markdown format while preserving document structure, hierarchy, and layout information. Maps document elements (headers, lists, tables, code blocks) to appropriate markdown syntax and maintains heading levels, emphasis, and structural relationships. Output markdown is suitable for downstream LLM processing and RAG systems.","intents":["I want to convert PDFs to markdown for ingestion into RAG systems or LLM applications","I need to preserve document structure and formatting when converting to text-based formats","I want to generate clean, readable markdown from complex documents for documentation purposes"],"best_for":["teams building RAG pipelines that ingest documents as markdown","developers creating LLM-powered document analysis tools","documentation teams converting legacy PDFs to markdown-based systems"],"limitations":["Complex formatting (multi-column layouts, sidebars, footnotes) may not convert cleanly to linear markdown","Images and visual elements are referenced but not embedded in markdown output","Markdown output may require post-processing to achieve desired formatting for specific use cases","Very large documents may produce markdown files that exceed LLM context windows"],"requires":["Python 3.8+","Parsed document in Docling's unified representation","Markdown generation library (likely built-in or using standard markdown library)"],"input_types":["Docling unified document model","Parsed PDF, DOCX, or HTML"],"output_types":["Markdown (.md) files","Markdown strings","Markdown with YAML frontmatter"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-docling__cap_4","uri":"capability://data.processing.analysis.ocr.enabled.text.extraction.for.scanned.documents","name":"ocr-enabled text extraction for scanned documents","description":"Integrates with OCR engines (likely Tesseract via pytesseract) to extract text from scanned PDFs and image-based documents where no embedded text layer exists. Applies OCR selectively to regions identified as text by layout analysis, combining OCR results with document structure to produce searchable, structured output from image-based documents.","intents":["I need to extract text from scanned PDFs that don't have embedded text layers","I want to process legacy documents that are stored as images and make them searchable","I need to handle mixed documents with both native text and scanned pages"],"best_for":["enterprises digitizing legacy paper documents","teams processing historical archives or scanned books","applications requiring comprehensive document processing including scanned content"],"limitations":["OCR accuracy depends heavily on image quality, resolution, and font clarity; poor scans produce unreliable text","OCR processing is significantly slower than native text extraction (10-100x slower per page)","Handwritten text recognition is limited or unavailable depending on OCR engine","OCR may struggle with non-Latin scripts, mathematical notation, or specialized symbols","Requires Tesseract OCR engine installation and language data files (adds ~500MB+ disk space)"],"requires":["Python 3.8+","Tesseract OCR engine installed on system","pytesseract Python library","Language data files for target languages","Scanned PDF or image files with minimum 150 DPI resolution recommended"],"input_types":["Scanned PDF files","Image files (PNG, JPG, TIFF)","Mixed documents with text and scanned pages"],"output_types":["Extracted text with confidence scores","Structured document with OCR metadata","Searchable PDF (if re-rendering)","Markdown with OCR-extracted content"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-docling__cap_5","uri":"capability://code.generation.editing.programmatic.document.processing.via.python.sdk","name":"programmatic document processing via python sdk","description":"Provides a Python SDK with object-oriented API for document parsing, transformation, and export. Exposes document model classes, parsing methods, and export functions that developers can use in Python applications. Supports method chaining and pipeline composition for building complex document processing workflows without CLI invocation.","intents":["I want to integrate document parsing into my Python application without calling external processes","I need to build a document processing pipeline that chains multiple operations (parse → segment → extract → export)","I want to programmatically access and manipulate parsed document structure in my code"],"best_for":["Python developers building document processing applications","teams integrating document parsing into larger Python-based systems","developers building gen AI applications that need document ingestion"],"limitations":["Python-only; no native support for other languages (though can be wrapped via subprocess or REST API)","Performance depends on Python interpreter speed; CPU-intensive operations may be slower than compiled alternatives","Memory usage can be significant for large documents; no streaming API for processing documents larger than available RAM","Requires Python environment setup and dependency management"],"requires":["Python 3.8+","docling package installed via pip","All format-specific dependencies (pdfplumber, python-docx, BeautifulSoup4, etc.)"],"input_types":["File paths to documents","File-like objects","Document URLs (if supported)"],"output_types":["DoclingDocument objects","Serialized JSON","Markdown strings","Exported files (markdown, JSON, etc.)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-docling__cap_6","uri":"capability://automation.workflow.command.line.interface.for.batch.document.processing","name":"command-line interface for batch document processing","description":"Provides a CLI tool for processing documents in batch mode without writing Python code. Supports specifying input/output formats, processing options, and export targets via command-line arguments. Enables integration with shell scripts, CI/CD pipelines, and non-Python workflows for document conversion and processing.","intents":["I want to convert a batch of PDFs to markdown from the command line without writing code","I need to integrate document processing into a shell script or CI/CD pipeline","I want to quickly test document parsing on a file without opening a Python REPL"],"best_for":["DevOps engineers integrating document processing into CI/CD pipelines","non-developers using document processing in shell scripts","teams doing one-off document conversions without building applications"],"limitations":["CLI interface may not expose all SDK capabilities; advanced options may require Python code","Batch processing via CLI is slower than programmatic API for large volumes due to process startup overhead","Error handling and progress reporting may be limited compared to programmatic API","No built-in parallelization; processing multiple files sequentially"],"requires":["Python 3.8+ with docling installed","Command-line shell (bash, zsh, PowerShell, etc.)","File system access to input documents"],"input_types":["File paths (single or glob patterns)","Directory paths for batch processing"],"output_types":["Markdown files","JSON files","Console output"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-docling__cap_7","uri":"capability://data.processing.analysis.document.serialization.and.deserialization","name":"document serialization and deserialization","description":"Converts parsed documents to/from serialized formats (JSON, YAML, or custom binary formats) for storage, transmission, and reconstruction. Enables saving parsed document structure to disk and reloading it without re-parsing the original file. Supports round-trip serialization where deserialized documents maintain full fidelity.","intents":["I want to cache parsed documents to avoid re-parsing the same file multiple times","I need to transmit parsed document structure over a network or API","I want to store document structure in a database for later retrieval and processing"],"best_for":["applications processing the same documents repeatedly","systems transmitting parsed documents between services","teams building document processing pipelines with caching layers"],"limitations":["Serialized format may be larger than original document for simple documents","Deserialization requires matching docling version; format changes between versions may break compatibility","No built-in compression; serialized JSON can be large for complex documents","Metadata about original file (path, modification time) may not be preserved"],"requires":["Python 3.8+","docling package","JSON or YAML library (standard library)"],"input_types":["DoclingDocument objects","JSON strings or files","YAML files"],"output_types":["JSON strings or files","YAML files","DoclingDocument objects (deserialized)","Binary serialized format (if supported)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-docling__cap_8","uri":"capability://tool.use.integration.format.specific.configuration.and.options","name":"format-specific configuration and options","description":"Allows fine-grained control over parsing behavior for each document format through configuration objects or parameters. Enables users to specify OCR language, PDF extraction method, HTML parsing rules, or other format-specific options without modifying core parsing logic. Configuration is passed to format-specific parsers to customize behavior.","intents":["I need to extract text from PDFs using a specific method (e.g., pdfplumber vs PyPDF2)","I want to specify OCR language for documents in non-English languages","I need to customize HTML parsing rules for documents with non-standard markup"],"best_for":["developers processing documents with specific requirements or edge cases","teams handling documents in multiple languages","applications requiring fine-tuned parsing behavior for specific document types"],"limitations":["Configuration options vary by format; no unified configuration interface across all formats","Advanced options may require understanding of underlying parser libraries","Configuration changes may not be backward compatible across docling versions","Some options may conflict or produce unexpected results if misconfigured"],"requires":["Python 3.8+","docling package","Knowledge of format-specific parser options"],"input_types":["Configuration dictionaries or objects","Command-line arguments (for CLI)"],"output_types":["Parsed documents with custom behavior"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-docling__cap_9","uri":"capability://data.processing.analysis.document.metadata.extraction.and.preservation","name":"document metadata extraction and preservation","description":"Extracts and preserves document metadata (title, author, creation date, language, page count) from source documents and includes it in the unified document representation. Metadata is accessible programmatically and can be exported alongside document content. Supports metadata from PDF properties, DOCX document properties, and HTML meta tags.","intents":["I want to extract document metadata (author, title, creation date) for cataloging or filtering","I need to preserve document provenance information when processing documents","I want to identify document language automatically for multi-language processing"],"best_for":["document management systems that need to catalog and organize documents","teams building document search or discovery systems","applications requiring document provenance tracking"],"limitations":["Metadata availability depends on source document; not all documents contain metadata","Metadata may be incomplete, incorrect, or intentionally omitted by document creators","Scanned PDFs typically have no metadata; OCR cannot extract metadata","Metadata format varies by document type; no guaranteed fields across all formats"],"requires":["Python 3.8+","docling package","Source documents with embedded metadata"],"input_types":["PDF files with document properties","DOCX files with document properties","HTML files with meta tags"],"output_types":["Metadata dictionary or object","JSON with metadata fields","Metadata included in serialized document"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","pdfplumber library for PDF parsing","python-docx library for DOCX parsing","BeautifulSoup4 for HTML parsing","Optional: pytesseract and Tesseract OCR for scanned PDF text extraction","Computer vision library (likely OpenCV or similar for bounding box detection)","PDF with embedded text layer (not scanned images)","Sufficient document resolution (minimum 72 DPI recommended)","docling package","Well-formatted documents with consistent styling"],"failure_modes":["PDF parsing accuracy depends on PDF structure and encoding; scanned PDFs without OCR will fail to extract text","DOCX support limited to standard Office formats; complex VBA macros or embedded objects may not parse correctly","HTML parsing assumes well-formed markup; malformed or heavily JavaScript-dependent pages may produce incomplete output","No built-in support for proprietary formats (Excel, PowerPoint, Visio) — requires format-specific extensions","Layout detection accuracy degrades on scanned documents with poor image quality or unusual fonts","Complex multi-column layouts with irregular spacing may be misinterpreted as separate sections","Requires sufficient visual contrast between content and background; low-contrast PDFs may fail segmentation","No support for detecting visual elements like charts, diagrams, or embedded images beyond text regions","Page-level processing may be slower than document-level processing due to overhead","Cross-page elements (headers, footers, page breaks) may not be properly handled in page-level processing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:18.279Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-docling","compare_url":"https://unfragile.ai/compare?artifact=pypi-docling"}},"signature":"zGrgf0hr6hnytlJ5yOhlIzwpfFQy2tCWnGCyzfrV++NEn6kjnXgA2eM2na3BWnqsBhgw+/L7SA2OAEJhwbYTBw==","signedAt":"2026-06-23T10:53:34.254Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-docling","artifact":"https://unfragile.ai/pypi-docling","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-docling","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}