{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"docling","slug":"docling","name":"Docling","type":"repo","url":"https://github.com/DS4SD/docling","page_url":"https://unfragile.ai/docling","categories":["documentation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"docling__cap_0","uri":"capability://data.processing.analysis.multi.format.document.ingestion.with.unified.parsing.pipeline","name":"multi-format document ingestion with unified parsing pipeline","description":"Accepts PDFs, DOCX, PPTX, images, and HTML as input and routes each through format-specific parsers before converting to a unified internal document representation. Uses format detection to select appropriate extraction engines (e.g., pdfplumber or pypdf for PDFs, python-docx for DOCX, PIL for images), normalizing all outputs into a common DoclingDocument AST that preserves structural metadata.","intents":["I need to process documents in multiple formats without writing separate parsing logic for each","I want a single API that handles PDFs, Word docs, PowerPoints, and images uniformly","I need to preserve document structure and metadata across different input formats"],"best_for":["data engineers building document processing pipelines","RAG system builders ingesting heterogeneous document sources","teams migrating from format-specific tools to unified processing"],"limitations":["PPTX support is limited to text extraction; slide layout and speaker notes handling is basic","Image quality directly impacts OCR accuracy; low-resolution or heavily compressed images may produce garbled text","No support for encrypted or password-protected PDFs without pre-decryption","Processing large documents (>500 pages) may require memory optimization or chunking strategies"],"requires":["Python 3.9+","pdfplumber or pypdf library for PDF parsing","python-docx for DOCX support","Pillow (PIL) for image handling","Optional: Tesseract or EasyOCR for OCR on image-based PDFs"],"input_types":["PDF files (.pdf)","Microsoft Word documents (.docx)","PowerPoint presentations (.pptx)","Images (.png, .jpg, .jpeg, .tiff, .bmp)","HTML files (.html, .htm)"],"output_types":["DoclingDocument (internal AST representation)","Markdown (.md)","JSON (structured metadata)","Plain text"],"categories":["data-processing-analysis","document-parsing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"docling__cap_1","uri":"capability://data.processing.analysis.layout.aware.document.structure.analysis","name":"layout-aware document structure analysis","description":"Analyzes spatial positioning, bounding boxes, and visual hierarchy of document elements (text blocks, tables, images, headers) to reconstruct logical reading order and document structure. Uses computer vision techniques to detect page regions, classify element types by position and styling, and build a hierarchical representation that preserves the original layout semantics rather than flattening to linear text.","intents":["I need to preserve the original document layout and reading order when extracting content","I want to identify headers, sections, and hierarchical structure from visual layout cues","I need to distinguish between main content, sidebars, footers, and other layout regions"],"best_for":["document understanding systems that require layout preservation","RAG pipelines where spatial context improves retrieval relevance","teams building document-to-markdown converters that maintain structure"],"limitations":["Complex multi-column layouts may be misclassified if columns are not clearly separated","Heavily styled or graphically-intensive documents may confuse layout detection","Rotated text or unusual orientations are not reliably detected","Layout analysis adds 100-300ms per page depending on document complexity"],"requires":["Python 3.9+","OpenCV or similar computer vision library for region detection","Document must have extractable text layer (scanned images require OCR first)"],"input_types":["PDF with text layer","DOCX with preserved formatting","HTML with semantic markup"],"output_types":["DoclingDocument with hierarchical structure","Bounding box coordinates (x, y, width, height)","Element type classifications (heading, body, table, image, etc.)"],"categories":["data-processing-analysis","layout-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"docling__cap_10","uri":"capability://data.processing.analysis.multi.language.document.support.with.language.detection","name":"multi-language document support with language detection","description":"Automatically detects the language of document content and applies language-specific processing (OCR language models, text segmentation, heading detection) appropriate to the detected language. Supports 50+ languages including CJK, Arabic, Devanagari, and Latin scripts, with configurable language hints for ambiguous cases. Preserves language information in document metadata for downstream processing.","intents":["I need to process documents in multiple languages without manual configuration","I want OCR and text extraction to work correctly for non-English documents","I need to preserve language information for multilingual RAG systems"],"best_for":["organizations processing multilingual document collections","global RAG systems supporting multiple languages","teams building document processing for international markets"],"limitations":["Language detection accuracy is ~95% for pure-language documents; mixed-language documents may be misclassified","Some languages (e.g., CJK) require language-specific OCR models; accuracy varies by language","Text segmentation (word/character boundaries) varies by language; some languages have no word boundaries","Heading detection heuristics are English-centric; non-Latin scripts may have lower accuracy","No support for right-to-left (RTL) text reordering; RTL content may be extracted in logical order"],"requires":["Python 3.9+","Language detection library (langdetect, textblob, or similar)","Language-specific OCR models for non-English languages (optional but recommended)"],"input_types":["Documents in any supported language","Mixed-language documents (with language hints)"],"output_types":["DoclingDocument with language metadata","Per-element language tags","Language-specific text extraction"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"docling__cap_11","uri":"capability://automation.workflow.streaming.document.processing.for.large.files","name":"streaming document processing for large files","description":"Processes large documents (>100 MB) in a streaming fashion, parsing pages or sections incrementally rather than loading the entire document into memory. Yields DoclingDocument chunks as they are processed, enabling memory-efficient handling of very large files and progressive output generation without waiting for complete document processing.","intents":["I need to process very large documents without running out of memory","I want to start processing results before the entire document is parsed","I need to handle documents that are too large to fit in RAM"],"best_for":["data engineers processing multi-gigabyte document archives","streaming pipelines that need progressive output","systems with memory constraints (embedded, serverless)"],"limitations":["Cross-page layout analysis is limited; page-level structure may not be fully preserved","Table extraction may fail if tables span multiple pages and pages are processed independently","Progress tracking is less accurate for streaming; total document size may be unknown","Streaming mode requires careful handling of state; some analyses require full document context","Output chunks may not align with logical document boundaries (chapters, sections)"],"requires":["Python 3.9+","Document format must support streaming (PDF with page-based structure)","Sufficient RAM for one page/section at a time (typically <50 MB)"],"input_types":["Large PDF files","Streaming file objects"],"output_types":["Generator/iterator of DoclingDocument chunks","Progressive output suitable for streaming consumption"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"docling__cap_12","uri":"capability://data.processing.analysis.document.chunking.with.semantic.awareness.and.overlap.control","name":"document chunking with semantic awareness and overlap control","description":"Splits extracted document structure into chunks suitable for RAG systems, respecting semantic boundaries (paragraphs, sections, tables) rather than naive character-count splitting. Implements configurable chunk size, overlap, and boundary detection to preserve semantic coherence while enabling efficient retrieval. Maintains chunk metadata (source page, section, confidence) for traceability.","intents":["I need to chunk documents for RAG systems while preserving semantic coherence","I want to control chunk size and overlap for optimal retrieval performance","I need to maintain traceability of chunks back to source documents"],"best_for":["RAG system builders preparing documents for vector embedding and retrieval","teams optimizing chunk size and overlap for retrieval quality","systems requiring chunk-level traceability for citation and verification"],"limitations":["Semantic boundary detection depends on document structure; poorly-structured documents may produce suboptimal chunks","Chunk size configuration requires tuning for specific embedding models and retrieval systems; no universal optimal size","Very large semantic units (e.g., long tables) may exceed chunk size limits and require splitting","Overlap increases storage and retrieval overhead; optimal overlap depends on retrieval algorithm"],"requires":["Python 3.9+","extracted document structure in DoclingDocument format"],"input_types":["DoclingDocument (internal structured format)"],"output_types":["list of document chunks with metadata","chunk boundaries and overlap information"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"docling__cap_2","uri":"capability://data.processing.analysis.table.extraction.with.cell.level.content.preservation","name":"table extraction with cell-level content preservation","description":"Detects table regions within documents using visual boundary detection and extracts cell contents while maintaining row/column relationships. Handles merged cells, multi-line cell content, and nested tables by parsing table structure into a normalized grid representation with explicit row and column indices, then exports to structured formats (JSON, Markdown table syntax) that preserve cell boundaries and relationships.","intents":["I need to extract tables from PDFs and convert them to structured data (CSV, JSON)","I want to preserve table structure including merged cells and multi-line content","I need to identify and extract specific columns or rows from complex tables"],"best_for":["financial document processing (extracting tables from annual reports)","data extraction pipelines that require tabular data in structured formats","teams building document-to-database ETL workflows"],"limitations":["Merged cells are normalized to single cells; original merge structure is not preserved in output","Tables with irregular borders or no visible borders may not be detected","Nested tables (tables within table cells) are flattened or may cause parsing errors","Very wide tables (>20 columns) may have column alignment issues if cells have varying heights","Handwritten or stylized table content may be misread by OCR"],"requires":["Python 3.9+","Document must have clear table boundaries (visual or structural)","For scanned tables: OCR engine (Tesseract, EasyOCR, or cloud-based)"],"input_types":["PDF with embedded tables","DOCX with native table objects","Images of tables (.png, .jpg)"],"output_types":["JSON (array of rows with column keys)","CSV format","Markdown table syntax","DoclingDocument table objects with cell-level metadata"],"categories":["data-processing-analysis","table-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"docling__cap_3","uri":"capability://image.visual.ocr.integration.for.image.based.and.scanned.documents","name":"ocr integration for image-based and scanned documents","description":"Detects when documents contain image-only content (scanned PDFs, photographs) and automatically routes them through an OCR engine (Tesseract, EasyOCR, or cloud-based APIs) to extract text. Preserves spatial positioning of recognized text by mapping OCR bounding boxes back to document coordinates, enabling layout analysis and table extraction to work on scanned documents with minimal quality loss.","intents":["I need to extract text from scanned PDFs or photographs of documents","I want OCR to run automatically when text extraction fails","I need to preserve text positioning from OCR results for layout reconstruction"],"best_for":["organizations processing legacy scanned document archives","document digitization pipelines","RAG systems that must handle both digital and scanned documents"],"limitations":["OCR accuracy degrades significantly with low-resolution images (<150 DPI) or heavy compression","Handwritten text recognition is unreliable; printed text only","Non-Latin scripts (CJK, Arabic, Devanagari) have lower accuracy than English","OCR processing adds 500ms-5s per page depending on image quality and engine choice","Tesseract (free) is slower and less accurate than commercial engines (AWS Textract, Google Vision)","No built-in language detection; must specify language code for optimal accuracy"],"requires":["Python 3.9+","Tesseract binary (for local OCR) OR EasyOCR library OR cloud API credentials (AWS, Google, Azure)","Image quality: minimum 100 DPI recommended; 300+ DPI for best accuracy"],"input_types":["Scanned PDF files","Image files (.png, .jpg, .tiff)","Mixed documents (some pages digital, some scanned)"],"output_types":["Extracted text with bounding box coordinates","DoclingDocument with OCR-sourced text layer","Confidence scores per recognized text region"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"docling__cap_4","uri":"capability://text.generation.language.document.to.markdown.conversion.with.structure.preservation","name":"document-to-markdown conversion with structure preservation","description":"Converts DoclingDocument AST to Markdown format, mapping document structure (headings, lists, tables, emphasis) to Markdown syntax while preserving hierarchical relationships. Uses the layout analysis output to infer heading levels from visual hierarchy, converts table structures to Markdown table syntax, and preserves inline formatting (bold, italic, links) from source documents.","intents":["I want to convert PDFs to Markdown for use in documentation systems or version control","I need to preserve document structure (headings, lists, tables) when converting to Markdown","I want to generate Markdown that's readable and properly formatted for downstream processing"],"best_for":["documentation teams converting legacy PDFs to Markdown-based systems","knowledge base builders preparing documents for wiki or static site generators","RAG systems that need Markdown as an intermediate format for chunking"],"limitations":["Complex formatting (multi-column layouts, text wrapping around images) cannot be fully represented in Markdown","Images are referenced but not embedded; image paths must be manually corrected","Footnotes and endnotes are converted to inline text; reference structure is lost","Markdown table syntax cannot represent merged cells or complex nested structures","Hyperlinks are preserved only if they exist in source document; no automatic link generation"],"requires":["Python 3.9+","DoclingDocument object (output from document ingestion pipeline)"],"input_types":["DoclingDocument AST"],"output_types":["Markdown (.md) text","UTF-8 encoded string"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"docling__cap_5","uri":"capability://data.processing.analysis.json.export.with.full.metadata.and.spatial.coordinates","name":"json export with full metadata and spatial coordinates","description":"Exports DoclingDocument to JSON format with complete metadata including bounding boxes, element types, confidence scores, and hierarchical relationships. Each element (text block, table, image, heading) is represented as a JSON object with spatial coordinates (page number, x, y, width, height), content, and type classification, enabling downstream systems to reconstruct document layout or perform spatial queries.","intents":["I need document data in JSON format for integration with downstream systems","I want to preserve spatial coordinates and metadata for layout reconstruction","I need to query or filter document elements by type, position, or content"],"best_for":["API builders exposing document processing as a service","teams building custom document analysis tools on top of Docling","RAG systems that need structured metadata for relevance ranking"],"limitations":["JSON output can be very large for multi-page documents (10+ MB for 100-page PDFs)","Nested structures may be deeply nested, requiring careful parsing by consumers","No schema validation; consumers must handle variable element types","Spatial coordinates are in document-specific units; no automatic normalization to standard DPI"],"requires":["Python 3.9+","DoclingDocument object"],"input_types":["DoclingDocument AST"],"output_types":["JSON (.json) file or string","Structured data with nested objects and arrays"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"docling__cap_6","uri":"capability://automation.workflow.batch.document.processing.with.progress.tracking","name":"batch document processing with progress tracking","description":"Processes multiple documents in sequence or parallel, with configurable batch size, timeout handling, and progress reporting. Implements error handling per-document so that failures in one document don't halt the entire batch, and provides callbacks or logging for monitoring processing status, memory usage, and performance metrics across the batch.","intents":["I need to process hundreds or thousands of documents efficiently","I want to monitor progress and handle failures gracefully in batch operations","I need to optimize memory usage when processing large document collections"],"best_for":["data engineers building document processing pipelines","teams processing document archives or bulk ingestion tasks","RAG system builders preparing large document collections"],"limitations":["No built-in distributed processing; batch processing is single-machine only","Memory usage scales linearly with batch size; large batches may require chunking","No automatic retry logic for transient failures (e.g., OCR service timeouts)","Progress tracking adds ~5-10% overhead per batch","No built-in persistence; failed documents must be re-processed manually"],"requires":["Python 3.9+","Sufficient RAM for batch size (estimate ~50-100 MB per document)","Optional: multiprocessing or concurrent.futures for parallel processing"],"input_types":["List of file paths","List of file objects","Directory path with wildcard filtering"],"output_types":["List of DoclingDocument objects","Progress logs with per-document status","Error reports with failure reasons"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"docling__cap_7","uri":"capability://memory.knowledge.document.chunking.for.rag.with.semantic.awareness","name":"document chunking for rag with semantic awareness","description":"Splits documents into chunks optimized for RAG systems by respecting document structure (chapters, sections, paragraphs) rather than naive character-count splitting. Uses layout analysis to identify logical boundaries (heading changes, section breaks) and creates chunks that preserve semantic coherence, with configurable chunk size, overlap, and metadata preservation (source page, section title, heading hierarchy).","intents":["I need to chunk documents for RAG without breaking semantic units","I want chunks that respect document structure and maintain context","I need to preserve metadata (page numbers, section titles) for retrieval attribution"],"best_for":["RAG system builders preparing documents for embedding and retrieval","teams building semantic search over document collections","LLM application developers needing context-aware document chunking"],"limitations":["Chunking strategy is fixed; no support for custom chunking logic","Very large sections (>4000 tokens) may exceed embedding model context windows","Chunk boundaries may not align perfectly with semantic units in poorly-structured documents","Metadata preservation adds ~10-20% to chunk size","No automatic optimization for specific embedding models or token limits"],"requires":["Python 3.9+","DoclingDocument object with layout analysis output","Optional: tiktoken or similar for accurate token counting"],"input_types":["DoclingDocument AST"],"output_types":["List of chunk objects with content, metadata, and source references","JSON or structured format compatible with vector databases"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"docling__cap_8","uri":"capability://data.processing.analysis.custom.element.classification.and.tagging","name":"custom element classification and tagging","description":"Allows users to define custom classification rules or provide trained models to tag document elements (text blocks, tables, images) with domain-specific labels (e.g., 'disclaimer', 'product-spec', 'pricing-table'). Integrates with the layout analysis pipeline to apply classifiers to detected elements and attach custom tags to the DoclingDocument AST, enabling downstream filtering or specialized processing based on element type.","intents":["I need to identify and tag specific types of content in documents (e.g., disclaimers, pricing tables)","I want to apply domain-specific classification to document elements","I need to filter or extract only certain types of elements from documents"],"best_for":["domain-specific document processing (legal, financial, technical)","teams building specialized RAG systems with content-type-aware retrieval","organizations with custom document classification requirements"],"limitations":["Custom classifiers must be trained separately; no built-in training pipeline","Classification accuracy depends on quality of training data and feature engineering","No pre-trained domain models; users must provide their own classifiers","Classifier integration requires custom code; no declarative configuration","Performance overhead depends on classifier complexity (10-100ms per element)"],"requires":["Python 3.9+","DoclingDocument object","Custom classifier (scikit-learn, PyTorch, or similar)","Training data for custom classifier"],"input_types":["DoclingDocument AST with detected elements"],"output_types":["DoclingDocument with custom tags and classifications","Filtered element lists by classification"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"docling__cap_9","uri":"capability://data.processing.analysis.document.comparison.and.diff.detection","name":"document comparison and diff detection","description":"Compares two DoclingDocument objects to identify structural and content differences, including added/removed elements, modified text, table changes, and layout shifts. Produces a diff report showing which elements changed, their locations, and the nature of changes (content modification, structural reorganization, element addition/deletion), useful for version control or change tracking in document processing pipelines.","intents":["I need to track changes between document versions","I want to identify what changed in a document after re-processing or updates","I need to generate change reports for document audit trails"],"best_for":["document management systems with version control","teams tracking changes in iteratively updated documents","compliance and audit workflows requiring change documentation"],"limitations":["Diff detection is element-level; fine-grained character-level diffs are not supported","Structural reorganization (e.g., section reordering) may be misidentified as deletions + additions","No automatic merging of conflicting changes; diff is read-only","Diff output can be large for documents with many changes","No built-in visualization; 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