{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-unstructured-io--unstructured","slug":"unstructured-io--unstructured","name":"unstructured","type":"mcp","url":"https://www.unstructured.io/","page_url":"https://unfragile.ai/unstructured-io--unstructured","categories":["mcp-servers","rag-knowledge","deployment-infra","data-pipelines"],"tags":["data-pipelines","deep-learning","document-image-analysis","document-image-processing","document-parser","document-parsing","docx","donut","information-retrieval","langchain","llm","machine-learning","ml","natural-language-processing","nlp","ocr","pdf","pdf-to-json","pdf-to-text","preprocessing"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-unstructured-io--unstructured__cap_0","uri":"capability://data.processing.analysis.auto.detection.file.type.routing.with.format.specific.partitioners","name":"auto-detection file type routing with format-specific partitioners","description":"Implements a registry-based partitioning system that automatically detects document file types (PDF, DOCX, PPTX, XLSX, HTML, images, email, audio, plain text, XML) via FileType enum and routes to specialized format-specific processors through _PartitionerLoader. The partition() entry point in unstructured/partition/auto.py orchestrates this routing, dynamically loading only required dependencies for each format to minimize memory overhead and startup latency.","intents":["I need to process a batch of mixed document types without writing format-specific code","I want to extract structured elements from any document format with a single API call","I need to handle 30+ document formats with automatic format detection"],"best_for":["data engineers building document ETL pipelines","RAG system builders ingesting heterogeneous document sources","teams migrating from format-specific parsers to unified extraction"],"limitations":["Format detection relies on file extension and magic bytes; ambiguous formats may require explicit strategy specification","Lazy-loading partitioners adds ~50-200ms overhead on first invocation for each format type","Some legacy formats (e.g., RTF, WordPerfect) require external converter dependencies"],"requires":["Python 3.9+","unstructured library installed","Format-specific optional dependencies (e.g., pdf2image for PDF, python-docx for DOCX)"],"input_types":["file path (string)","file bytes (binary)","file-like object"],"output_types":["List[Element] — standardized typed element objects"],"categories":["data-processing-analysis","document-parsing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_1","uri":"capability://data.processing.analysis.multi.strategy.pdf.and.image.processing.with.ocr.fallback.pipeline","name":"multi-strategy pdf and image processing with ocr fallback pipeline","description":"Implements a three-tier processing strategy pipeline for PDFs and images: FAST (PDFMiner text extraction only), HI_RES (layout detection + element extraction via unstructured-inference), and OCR_ONLY (Tesseract/Paddle OCR agents). The system automatically selects or allows explicit strategy specification, with intelligent fallback logic that escalates from text extraction to layout analysis to OCR when content is unreadable. Bounding box analysis and layout merging algorithms reconstruct document structure from spatial coordinates.","intents":["I need to extract text from PDFs with varying quality (digital vs scanned) with automatic strategy selection","I want to preserve document layout and spatial relationships (coordinates, page numbers) during extraction","I need OCR capabilities for scanned documents but want fast text extraction for digital PDFs"],"best_for":["document processing pipelines handling mixed digital and scanned content","teams requiring layout-aware extraction for structured documents (invoices, forms, reports)","RAG systems needing spatial metadata for document chunking"],"limitations":["HI_RES strategy requires unstructured-inference dependency (adds ~500MB model files); slower than FAST by 3-5x","OCR accuracy degrades significantly below 150 DPI; requires image preprocessing for best results","Layout detection may fail on complex multi-column documents or non-standard page orientations","Bounding box merging algorithm can over-aggregate adjacent elements in dense layouts"],"requires":["Python 3.9+","pdf2image library for PDF rasterization","unstructured-inference for HI_RES strategy (optional but recommended)","Tesseract or Paddle OCR installed for OCR_ONLY strategy","Minimum 2GB RAM for HI_RES processing"],"input_types":["PDF file path or bytes","image file (PNG, JPG, TIFF, BMP)","scanned document image"],"output_types":["List[Element] with coordinates, page numbers, and layout metadata"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_10","uri":"capability://data.processing.analysis.table.structure.extraction.with.cell.level.granularity","name":"table structure extraction with cell-level granularity","description":"Implements table detection and extraction that preserves table structure (rows, columns, cell content) with cell-level metadata (coordinates, merged cells). Supports extraction from PDFs (via layout detection), images (via OCR), and Office documents (via native parsing). Handles complex tables (nested headers, merged cells, multi-line cells) with configurable extraction strategies.","intents":["I need to extract tables from documents and convert to structured format (CSV, JSON, database)","I want to preserve table structure and cell relationships for downstream processing","I need to handle complex tables with merged cells and multi-line content"],"best_for":["financial document processing (invoices, statements, reports)","data extraction pipelines converting documents to databases","teams building document-to-database ETL systems"],"limitations":["Table detection relies on layout analysis; tables without clear borders may be missed","Merged cell handling is heuristic-based; complex merging patterns may be incorrectly reconstructed","Multi-line cell content may be split across rows; requires post-processing to reconstruct","Nested tables are flattened; hierarchical table structure is not preserved","OCR-extracted tables have lower accuracy than native format parsing"],"requires":["Python 3.9+","unstructured library with table extraction","Optional: unstructured-inference for layout detection"],"input_types":["PDF, image, or Office document containing tables"],"output_types":["Table Element with cell-level structure","CSV, JSON, or database-compatible format"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_11","uri":"capability://data.processing.analysis.image.extraction.and.embedded.image.handling","name":"image extraction and embedded image handling","description":"Implements image detection and extraction from documents (PDFs, Office files, HTML) that preserves image metadata (dimensions, coordinates, alt text, captions). Supports image-to-text conversion via OCR for image content analysis. Extracts images as separate Element objects with links to source document location. Handles image preprocessing (rotation, deskewing) for improved OCR accuracy.","intents":["I need to extract images from documents and preserve their location context","I want to convert image content to text via OCR for full-text search","I need to identify and extract diagrams, charts, or infographics from documents"],"best_for":["document analysis pipelines requiring image-aware extraction","RAG systems that need to index image content alongside text","teams building document viewers or annotation tools"],"limitations":["Image extraction is metadata-only; binary image data is not embedded in output","Image-to-text conversion via OCR is slow and less accurate than native text extraction","Diagram and chart understanding requires specialized models; not built-in","Image preprocessing (rotation, deskewing) may fail on complex layouts"],"requires":["Python 3.9+","pdf2image for PDF image extraction","Optional: Tesseract or Paddle OCR for image-to-text conversion"],"input_types":["PDF, Office document, or HTML with embedded images"],"output_types":["Image Element with metadata (dimensions, coordinates, alt text)","OCR-extracted text from images"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_12","uri":"capability://data.processing.analysis.serialization.to.multiple.output.formats.json.csv.markdown.parquet","name":"serialization to multiple output formats (json, csv, markdown, parquet)","description":"Implements serialization layer (unstructured/staging/base.py 103-229) that converts extracted Element objects to multiple output formats (JSON, CSV, Markdown, Parquet, XML) while preserving metadata. Supports custom serialization schemas, filtering by element type, and format-specific optimizations. Enables lossless round-trip conversion for certain formats.","intents":["I need to export extracted documents to multiple formats for different downstream systems","I want to preserve metadata during serialization for downstream processing","I need to convert documents to formats compatible with databases or data warehouses"],"best_for":["data pipelines requiring format-agnostic document processing","teams exporting documents to multiple systems (databases, data lakes, search engines)","data engineers building ETL workflows"],"limitations":["Format-specific optimizations may lose metadata; JSON preserves more information than CSV","Lossless round-trip conversion is not guaranteed for all formats","Custom serialization schemas require schema definition; no automatic schema inference","Large documents may produce very large JSON/CSV files; Parquet is more efficient"],"requires":["Python 3.9+","unstructured library with serialization support","Optional: pyarrow for Parquet output"],"input_types":["List[Element] from partitioner output"],"output_types":["JSON, CSV, Markdown, Parquet, XML files or strings"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_13","uri":"capability://data.processing.analysis.bounding.box.analysis.and.spatial.coordinate.management","name":"bounding box analysis and spatial coordinate management","description":"Implements bounding box utilities for analyzing spatial relationships between document elements (coordinates, page numbers, relative positioning). Supports coordinate normalization across different page sizes and DPI settings. Enables spatial queries (e.g., find elements within a region) and layout reconstruction from coordinates. Used internally by layout detection and element merging algorithms.","intents":["I need to preserve spatial information for document reconstruction or highlighting","I want to query elements by spatial location (e.g., find all text in header region)","I need to normalize coordinates across documents with different page sizes"],"best_for":["document viewer and annotation tools requiring spatial metadata","layout-aware RAG systems that need to understand document structure","teams building document reconstruction or highlighting features"],"limitations":["Coordinate systems vary across document formats; normalization may introduce rounding errors","Spatial queries are O(n) without indexing; large documents may be slow","Bounding box merging is heuristic-based; adjacent elements may be incorrectly merged","No built-in support for rotated or skewed text; assumes axis-aligned bounding boxes"],"requires":["Python 3.9+","unstructured library with bounding box utilities"],"input_types":["Element objects with coordinate metadata"],"output_types":["Normalized coordinates, spatial queries, layout analysis"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_14","uri":"capability://data.processing.analysis.evaluation.framework.and.metrics.collection.for.extraction.quality","name":"evaluation framework and metrics collection for extraction quality","description":"Implements evaluation framework (unstructured/metrics/) that measures extraction quality through text metrics (precision, recall, F1 score) and table metrics (cell accuracy, structure preservation). Supports comparison against ground truth annotations and enables benchmarking across different strategies and document types. Collects processing metrics (time, memory, cost) for performance monitoring.","intents":["I need to measure extraction quality and compare different strategies","I want to benchmark extraction performance across document types","I need to validate extraction accuracy against ground truth data"],"best_for":["teams optimizing extraction quality for specific document domains","data engineers validating extraction pipelines","researchers benchmarking document processing approaches"],"limitations":["Evaluation requires ground truth annotations; manual annotation is time-consuming","Metrics are aggregate-level; no element-level quality assessment","Benchmarking is offline; no real-time quality monitoring","Cost metrics require integration with cloud provider APIs"],"requires":["Python 3.9+","unstructured library with evaluation framework","Ground truth annotations in supported format"],"input_types":["Extracted elements and ground truth annotations"],"output_types":["Quality metrics (precision, recall, F1), performance metrics (time, memory)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_15","uri":"capability://tool.use.integration.api.client.integration.and.cloud.platform.support","name":"api client integration and cloud platform support","description":"Provides API client abstraction (unstructured/api/) for integration with cloud document processing services and hosted Unstructured platform. Supports authentication, request batching, and result streaming. Enables seamless switching between local processing and cloud-hosted extraction for cost/performance optimization. Includes retry logic and error handling for production reliability.","intents":["I need to process documents via cloud API for scalability without managing infrastructure","I want to switch between local and cloud processing based on cost/performance tradeoffs","I need reliable document processing with automatic retries and error handling"],"best_for":["teams requiring scalable document processing without infrastructure management","enterprises with strict data residency requirements (local processing option)","production systems requiring high reliability and SLA compliance"],"limitations":["Cloud API requires network connectivity; offline processing not possible","API costs scale with document volume; local processing may be cheaper for large batches","Cloud API latency is higher than local processing; not suitable for real-time applications","Data privacy concerns with cloud processing; requires data residency compliance"],"requires":["Python 3.9+","API key for Unstructured cloud platform","Network connectivity for API calls"],"input_types":["Document file path or bytes"],"output_types":["List[Element] from cloud API response"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_2","uri":"capability://data.processing.analysis.structured.element.type.hierarchy.with.rich.metadata.extraction","name":"structured element type hierarchy with rich metadata extraction","description":"Defines a typed element model (unstructured/documents/elements.py) with 20+ element types (Title, NarrativeText, Table, Image, Header, Footer, PageBreak, etc.) that represent document components. Each element carries rich metadata including bounding box coordinates, page numbers, language detection, table structure (rows/columns), image dimensions, and custom key-value pairs. The metadata system supports serialization to JSON, CSV, Markdown, and other formats while preserving structural information.","intents":["I need to distinguish between different document components (titles, body text, tables, images) for downstream processing","I want to preserve spatial information (coordinates, page numbers) for document reconstruction or highlighting","I need to extract table structure with row/column information for database ingestion"],"best_for":["RAG systems that need semantic element types for better chunking and retrieval","document analysis pipelines requiring element-level classification","teams building document viewers or annotation tools needing spatial metadata"],"limitations":["Element type classification relies on heuristics and layout analysis; misclassification occurs in ambiguous cases (e.g., captions vs body text)","Metadata serialization adds ~10-20% overhead to processing time","Table extraction accuracy depends on layout detection quality; complex nested tables may be flattened","Language detection requires additional NLP model; adds ~100-200ms per document"],"requires":["Python 3.9+","unstructured library with metadata extraction enabled","Optional: langdetect or textblob for language detection"],"input_types":["List[Element] from partitioner output"],"output_types":["Typed Element objects with metadata","JSON/CSV/Markdown serialized output"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_3","uri":"capability://data.processing.analysis.intelligent.document.chunking.for.embedding.and.rag.pipelines","name":"intelligent document chunking for embedding and rag pipelines","description":"Provides chunking capabilities that split extracted elements into semantically coherent chunks optimized for embedding models and RAG retrieval. The chunking system respects element boundaries (e.g., keeps paragraphs together), supports configurable chunk size and overlap, and can leverage element metadata (type, coordinates) to make intelligent splitting decisions. Integration with LangChain enables seamless pipeline composition for vector database ingestion.","intents":["I need to split documents into chunks optimized for embedding models (512-1024 tokens)","I want to preserve semantic coherence when chunking (keep paragraphs and tables intact)","I need to chunk documents while maintaining metadata for source attribution in RAG systems"],"best_for":["RAG system builders preparing documents for vector database ingestion","teams building semantic search systems over document collections","LLM application developers needing context-aware document chunking"],"limitations":["Chunking strategy is heuristic-based; optimal chunk size depends on embedding model and downstream LLM context window","Element-aware chunking may produce uneven chunk sizes if elements are very large or very small","Metadata preservation adds complexity; some serialization formats may lose chunk-level metadata","No built-in support for hierarchical chunking (e.g., chapter-level then paragraph-level)"],"requires":["Python 3.9+","unstructured library with chunking module","Optional: LangChain for pipeline integration","Tokenizer for accurate chunk size calculation (e.g., tiktoken for OpenAI models)"],"input_types":["List[Element] from partitioner output"],"output_types":["List[Chunk] with text and metadata","LangChain Document objects for pipeline integration"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_4","uri":"capability://data.processing.analysis.office.document.extraction.docx.pptx.xlsx.with.style.and.structure.preservation","name":"office document extraction (docx, pptx, xlsx) with style and structure preservation","description":"Implements specialized partitioners for Microsoft Office formats that extract text, tables, and images while preserving document structure (headings, lists, formatting). Uses python-docx, python-pptx, and openpyxl libraries to parse Office XML formats and reconstruct logical document hierarchy. Supports extraction of embedded images, hyperlinks, and table structure with cell-level granularity.","intents":["I need to extract text and tables from Word documents while preserving heading hierarchy","I want to extract slides and speaker notes from PowerPoint presentations","I need to convert Excel spreadsheets to structured element format for RAG ingestion"],"best_for":["enterprise document processing pipelines handling Office file formats","teams building knowledge bases from corporate documents (reports, presentations, spreadsheets)","document conversion workflows requiring format-agnostic output"],"limitations":["Complex formatting (merged cells, nested tables, text boxes) may not be fully preserved","Embedded objects (charts, SmartArt) are not extracted; only text and images","Macro-enabled documents (.docm, .xlsm) are processed as standard formats; macros are not executed","Presentation extraction loses animation and transition information"],"requires":["Python 3.9+","python-docx library for DOCX","python-pptx library for PPTX","openpyxl library for XLSX"],"input_types":["DOCX file path or bytes","PPTX file path or bytes","XLSX file path or bytes"],"output_types":["List[Element] with heading hierarchy, table structure, and embedded images"],"categories":["data-processing-analysis","document-parsing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_5","uri":"capability://data.processing.analysis.html.and.web.content.extraction.with.semantic.tag.parsing","name":"html and web content extraction with semantic tag parsing","description":"Implements HTML partitioner that parses HTML/XML documents using BeautifulSoup or lxml, extracts semantic content from tags (h1-h6 for headings, p for paragraphs, table for tables), and reconstructs document structure. Handles common web patterns (navigation, sidebars, footers) by filtering noise elements. Supports extraction of links, metadata (title, description), and image alt text.","intents":["I need to extract clean text from web pages while preserving heading structure","I want to convert HTML documentation to structured elements for RAG ingestion","I need to extract tables and links from web content"],"best_for":["web scraping pipelines that need structured output","teams building knowledge bases from HTML documentation (API docs, wikis)","RAG systems ingesting web content"],"limitations":["JavaScript-rendered content is not extracted; requires pre-rendering with Selenium or Playwright","Noise filtering (ads, navigation) is heuristic-based and may fail on non-standard layouts","CSS styling information is not preserved; only semantic HTML structure","Malformed HTML may produce unexpected results; requires HTML cleanup/validation"],"requires":["Python 3.9+","beautifulsoup4 or lxml for HTML parsing","Optional: requests for web fetching"],"input_types":["HTML file path or bytes","HTML string","URL (requires additional fetching)"],"output_types":["List[Element] with heading hierarchy and table structure"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_6","uri":"capability://data.processing.analysis.email.and.message.format.extraction.with.thread.reconstruction","name":"email and message format extraction with thread reconstruction","description":"Implements partitioners for email formats (EML, MSG, MBOX) and message protocols that extract message headers (from, to, subject, date), body text, and attachments. Reconstructs email threads by parsing In-Reply-To and References headers. Supports extraction of quoted text and signature detection to separate original content from replies.","intents":["I need to extract text from email archives for knowledge base ingestion","I want to reconstruct email conversations and threads for analysis","I need to separate original email content from quoted replies"],"best_for":["enterprise email archival and compliance systems","teams building knowledge bases from email communications","email analysis and conversation mining pipelines"],"limitations":["HTML email bodies require additional parsing; plain text extraction may lose formatting","Attachment extraction is metadata-only; binary content is not processed","Thread reconstruction relies on header parsing; may fail with non-standard email clients","Signature detection is heuristic-based and may incorrectly classify content"],"requires":["Python 3.9+","email library (standard library) for EML parsing","Optional: python-docx for MSG format (requires additional dependencies)"],"input_types":["EML file path or bytes","MSG file path or bytes","MBOX file path or bytes"],"output_types":["List[Element] with email metadata and thread structure"],"categories":["data-processing-analysis","document-parsing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_7","uri":"capability://data.processing.analysis.audio.transcription.and.speech.to.text.extraction","name":"audio transcription and speech-to-text extraction","description":"Implements audio partitioner that transcribes speech to text using Whisper or other speech recognition models. Extracts speaker segments, timestamps, and confidence scores. Supports multiple audio formats (MP3, WAV, FLAC, OGG) and handles long-form audio by chunking into segments for processing. Integrates with language detection for multilingual support.","intents":["I need to extract text from audio files (podcasts, meetings, interviews) for RAG ingestion","I want to transcribe audio with timestamps for video synchronization","I need to support multilingual audio transcription"],"best_for":["teams building knowledge bases from audio content (podcasts, webinars, meetings)","RAG systems ingesting multimedia content","accessibility pipelines generating transcripts from audio"],"limitations":["Transcription quality depends on audio quality; background noise significantly degrades accuracy","Speaker diarization (identifying who spoke) requires additional models; not built-in","Long-form audio requires chunking; segment boundaries may split sentences","Whisper model requires GPU for reasonable performance; CPU transcription is very slow","No support for audio-specific metadata (bitrate, channels, duration) extraction"],"requires":["Python 3.9+","openai-whisper or similar speech recognition model","librosa or pydub for audio processing","GPU recommended for reasonable performance (CUDA 11.8+)"],"input_types":["MP3, WAV, FLAC, OGG, M4A audio files"],"output_types":["List[Element] with transcribed text and timestamps"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_8","uri":"capability://data.processing.analysis.language.detection.and.multilingual.content.handling","name":"language detection and multilingual content handling","description":"Implements language detection at document and element level using langdetect or textblob, enabling multilingual document processing. Detects language for each extracted element, supports language-specific text processing (e.g., CJK character handling), and enables filtering by language. Integrates with OCR agents to select language-specific models for improved accuracy.","intents":["I need to process documents in multiple languages and identify language per element","I want to filter or separate content by language in multilingual documents","I need language-specific OCR models for non-Latin scripts (Chinese, Arabic, etc.)"],"best_for":["global organizations processing multilingual document collections","RAG systems supporting multiple languages","teams building language-aware document processing pipelines"],"limitations":["Language detection is probabilistic; short text segments may be misclassified","CJK character handling requires additional tokenization libraries (e.g., jieba for Chinese)","Language-specific OCR models must be installed separately; adds significant disk space","Mixed-language documents may have inconsistent language detection across elements"],"requires":["Python 3.9+","langdetect or textblob for language detection","Optional: jieba for CJK tokenization","Optional: language-specific OCR models (Paddle OCR supports 80+ languages)"],"input_types":["Extracted text from any document format"],"output_types":["Element metadata with language field","Filtered/separated elements by language"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unstructured-io--unstructured__cap_9","uri":"capability://automation.workflow.configurable.processing.strategy.selection.and.performance.tuning","name":"configurable processing strategy selection and performance tuning","description":"Provides strategy configuration system (FAST, HI_RES, OCR_ONLY) that allows users to trade off speed vs accuracy based on use case. Supports per-document strategy selection, timeout configuration, and resource limits (memory, CPU). Includes metrics collection for performance monitoring and optimization. Enables fine-tuning of partitioner parameters (e.g., OCR language, layout detection thresholds).","intents":["I need to process large document batches with configurable speed/accuracy tradeoff","I want to monitor processing performance and identify bottlenecks","I need to tune partitioner parameters for specific document types"],"best_for":["production document processing pipelines with SLA requirements","teams optimizing cost/performance tradeoffs in cloud environments","data engineers tuning extraction quality for specific document domains"],"limitations":["Strategy selection is document-level; no per-element strategy variation","Performance metrics are basic (processing time, memory); no detailed profiling","Timeout configuration may cause incomplete extraction; no graceful degradation","Parameter tuning requires domain knowledge; no automatic optimization"],"requires":["Python 3.9+","unstructured library with strategy configuration support"],"input_types":["Strategy enum (FAST, HI_RES, OCR_ONLY)","Configuration dict with parameters"],"output_types":["List[Element] with performance metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","unstructured library installed","Format-specific optional dependencies (e.g., pdf2image for PDF, python-docx for DOCX)","pdf2image library for PDF rasterization","unstructured-inference for HI_RES strategy (optional but recommended)","Tesseract or Paddle OCR installed for OCR_ONLY strategy","Minimum 2GB RAM for HI_RES processing","unstructured library with table extraction","Optional: unstructured-inference for layout detection","pdf2image for PDF image extraction"],"failure_modes":["Format detection relies on file extension and magic bytes; ambiguous formats may require explicit strategy specification","Lazy-loading partitioners adds ~50-200ms overhead on first invocation for each format type","Some legacy formats (e.g., RTF, WordPerfect) require external converter dependencies","HI_RES strategy requires unstructured-inference dependency (adds ~500MB model files); slower than FAST by 3-5x","OCR accuracy degrades significantly below 150 DPI; requires image preprocessing for best results","Layout detection may fail on complex multi-column documents or non-standard page orientations","Bounding box merging algorithm can over-aggregate adjacent elements in dense layouts","Table detection relies on layout analysis; tables without clear borders may be missed","Merged cell handling is heuristic-based; complex merging patterns may be incorrectly reconstructed","Multi-line cell content may be split across rows; requires post-processing to reconstruct","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6943532286788296,"quality":0.6,"ecosystem":0.8,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:24.502Z","last_commit":"2026-05-02T21:33:56Z"},"community":{"stars":14617,"forks":1228,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=unstructured-io--unstructured","compare_url":"https://unfragile.ai/compare?artifact=unstructured-io--unstructured"}},"signature":"Cd0xcnHdw6/Y6uDgDdYwZchUcdJnE7GRZUPqUVED4uahHBwW/SAAQRdGo3P8A9hc7SeXPp7pUptApAGvrUsqAA==","signedAt":"2026-06-20T18:25:56.756Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/unstructured-io--unstructured","artifact":"https://unfragile.ai/unstructured-io--unstructured","verify":"https://unfragile.ai/api/v1/verify?slug=unstructured-io--unstructured","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"}}