{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-unstructured","slug":"pypi-unstructured","name":"unstructured","type":"repo","url":"https://pypi.org/project/unstructured/","page_url":"https://unfragile.ai/pypi-unstructured","categories":["model-training"],"tags":["CV","HTML","NLP","PDF","XML","parsing","preprocessing"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-unstructured__cap_0","uri":"capability://data.processing.analysis.multi.format.document.parsing.with.unified.extraction.interface","name":"multi-format document parsing with unified extraction interface","description":"Parses diverse document formats (PDF, HTML, XML, DOCX, images) into a standardized element hierarchy using format-specific parsers (PyPDF2, lxml, python-docx, Pillow) while normalizing output to a common Element abstraction layer. 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Maintains parent-child relationships between elements and supports hierarchy traversal for context-aware processing. Enables downstream systems to understand document structure for improved chunking, summarization, or navigation.","intents":["I need to preserve document structure (sections, subsections) for semantic understanding","I want to extract context by traversing the document hierarchy (parent sections, sibling elements)","I need to maintain reading order and logical flow for downstream NLP tasks"],"best_for":["RAG systems requiring hierarchical document understanding","document summarization pipelines needing structural context","teams building searchable document systems with structure-aware navigation"],"limitations":["Hierarchy reconstruction is heuristic-based; accuracy varies by document format and structure","Complex or non-standard document structures may be misinterpreted","No support for implicit hierarchies (e.g., numbered lists as subsections)","Hierarchy depth is limited by heuristic rules; deeply nested structures may be flattened","Performance overhead for large documents due to hierarchy reconstruction"],"requires":["Python 3.8+","parsed elements with formatting and positional metadata"],"input_types":["extracted elements with type and position information","formatting metadata (font size, indentation, etc.)"],"output_types":["hierarchical element tree with parent-child relationships","hierarchy metadata (depth, section numbering, reading order)","context information for each element (parent section, siblings)"],"categories":["data-processing-analysis","structure-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-unstructured__cap_11","uri":"capability://memory.knowledge.integration.with.embedding.and.vector.storage.systems","name":"integration with embedding and vector storage systems","description":"Provides built-in adapters for popular embedding models (OpenAI, Hugging Face, local models) and vector databases (Pinecone, Weaviate, Chroma) enabling direct integration of parsed and chunked documents into RAG pipelines. 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Preserves table metadata (headers, cell positions, merged cells) and handles complex layouts including nested tables and multi-row headers.","intents":["I need to extract tables from PDFs and convert them to CSV/JSON for analysis","I want to preserve table structure and headers when converting documents to structured data","I need to handle complex tables with merged cells and multi-row headers without manual cleanup"],"best_for":["data analysts extracting tabular data from mixed document sources","ML engineers preparing structured datasets from unstructured documents","teams automating data entry from scanned documents or PDFs"],"limitations":["Accuracy degrades on scanned PDFs without embedded text; requires OCR integration","Merged cells and complex layouts may be flattened or incorrectly parsed","No automatic header detection for tables without explicit header rows","Performance degrades on documents with >100 tables due to sequential processing","Nested tables are typically flattened rather than preserved hierarchically"],"requires":["Python 3.8+","pdfplumber for PDF table extraction","lxml for HTML table parsing","pandas for DataFrame output"],"input_types":["PDF documents with embedded tables","HTML documents with table elements","DOCX files with table objects","images containing tables (with OCR integration)"],"output_types":["CSV format","JSON format","pandas DataFrames","table metadata (headers, cell positions, dimensions)"],"categories":["data-processing-analysis","structured-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-unstructured__cap_3","uri":"capability://image.visual.image.and.visual.element.extraction.with.metadata.preservation","name":"image and visual element extraction with metadata preservation","description":"Extracts images and visual elements from documents while preserving spatial metadata (page number, bounding box coordinates, position in document hierarchy). Supports image format conversion and optional OCR integration for text-in-image extraction. Maintains references between images and surrounding text for context-aware downstream processing.","intents":["I need to extract all images from PDFs and preserve their location in the document","I want to extract text from images (OCR) and link it back to the original document context","I need to identify and separate diagrams, charts, and photos for different processing pipelines"],"best_for":["document digitization pipelines requiring visual asset extraction","multimodal ML systems combining text and image understanding","teams building searchable document archives with image indexing"],"limitations":["OCR requires external service integration (Tesseract, cloud APIs); not built-in","Image quality assessment and filtering not included","No automatic image classification (chart vs photo vs diagram)","Bounding box accuracy depends on underlying parser; may be approximate for some formats","Large images or high-resolution documents may cause memory overhead"],"requires":["Python 3.8+","Pillow for image processing","pdfplumber or PyPDF2 for PDF image extraction","optional: Tesseract or cloud OCR API for text extraction from images"],"input_types":["PDF documents with embedded images","HTML documents with image elements","DOCX files with image objects"],"output_types":["extracted image files (PNG, JPG, TIFF)","image metadata (page number, bounding box, position)","OCR text (if OCR integration enabled)","image-to-text relationships"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-unstructured__cap_4","uri":"capability://data.processing.analysis.document.metadata.extraction.and.enrichment","name":"document metadata extraction and enrichment","description":"Extracts and normalizes document-level metadata (title, author, creation date, language, page count) from document properties and content analysis. 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Supports configurable cleaning strategies (aggressive vs conservative) and maintains element type awareness to apply format-specific cleaning (e.g., preserving code formatting in code blocks).","intents":["I need to clean extracted text (remove extra whitespace, fix encoding issues) without losing semantic content","I want to normalize text for downstream ML models while preserving code blocks and special formatting","I need to handle documents with mixed encodings or corrupted text gracefully"],"best_for":["ML pipelines requiring text preprocessing before embedding or fine-tuning","teams processing documents with encoding issues or corrupted text","RAG systems needing consistent text normalization across diverse sources"],"limitations":["Cleaning strategies are rule-based; no learned normalization from domain-specific data","Aggressive cleaning may remove intentional formatting or special characters","No support for language-specific text normalization (e.g., Unicode normalization forms)","Performance impact on very large documents due to element-by-element processing"],"requires":["Python 3.8+","optional: unicodedata for Unicode normalization"],"input_types":["extracted text elements","element type information (code block, narrative text, etc.)"],"output_types":["cleaned and normalized text","cleaning operation log (for debugging)","element-level cleaning metadata"],"categories":["data-processing-analysis","text-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-unstructured__cap_6","uri":"capability://data.processing.analysis.document.partitioning.with.element.type.classification","name":"document partitioning with element type classification","description":"Classifies extracted elements into semantic types (Title, NarrativeText, Table, Image, Code, Header, Footer, PageBreak) using heuristics based on formatting, position, and content patterns. Enables downstream pipelines to apply type-specific processing (e.g., different chunking for code vs narrative text) and supports custom element type definitions.","intents":["I need to identify different element types (tables, code blocks, headings) for specialized processing","I want to apply different chunking or embedding strategies based on element type","I need to filter or prioritize certain element types (e.g., extract only code blocks from technical documents)"],"best_for":["ML pipelines requiring element-type-aware processing","technical document processing systems (code extraction, API documentation)","multimodal RAG systems needing type-specific retrieval strategies"],"limitations":["Element type classification is heuristic-based; accuracy varies by document format and structure","No machine learning-based classification; cannot adapt to domain-specific element types","Ambiguous elements (e.g., formatted text that could be code or narrative) may be misclassified","Custom element type definitions require code changes; no configuration-based customization"],"requires":["Python 3.8+","parsed document elements with formatting metadata"],"input_types":["extracted elements with formatting information","element position and context metadata"],"output_types":["element type classification (Title, Table, Code, etc.)","confidence scores for classification (if available)","type-specific metadata"],"categories":["data-processing-analysis","classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-unstructured__cap_7","uri":"capability://automation.workflow.batch.document.processing.with.streaming.output","name":"batch document processing with streaming output","description":"Processes multiple documents in batch mode with streaming output to avoid memory overhead on large document collections. Implements configurable parallelization (thread-based or process-based) and supports progress tracking and error handling per document. Enables integration with external storage systems (S3, GCS) for input/output without local file staging.","intents":["I need to process thousands of documents efficiently without loading all into memory","I want to parallelize document processing across multiple cores or machines","I need to integrate document processing with cloud storage (S3, GCS) for scalability"],"best_for":["teams processing large document collections (>1000 documents)","cloud-based document processing pipelines","batch ETL systems requiring efficient resource utilization"],"limitations":["Parallelization overhead may not justify gains for small document batches (<100 documents)","Process-based parallelization requires picklable objects; some parsers may not support this","Cloud storage integration requires separate SDK setup (boto3 for S3, google-cloud-storage for GCS)","Error handling is per-document; partial batch failures may require manual retry logic","No built-in checkpointing; failed batches must be reprocessed from the beginning"],"requires":["Python 3.8+","optional: boto3 for S3 integration, google-cloud-storage for GCS","optional: concurrent.futures for parallelization"],"input_types":["list of document paths or file objects","batch configuration (parallelization strategy, chunk size)"],"output_types":["streaming element objects or chunks","per-document metadata and status","error logs and retry information"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-unstructured__cap_8","uri":"capability://tool.use.integration.custom.parsing.pipeline.composition.with.plugin.architecture","name":"custom parsing pipeline composition with plugin architecture","description":"Provides a plugin-based architecture enabling users to compose custom parsing pipelines by chaining built-in and custom processors. Supports dependency injection for parser configuration and enables middleware-style processing stages (pre-parsing, post-parsing, element transformation). Maintains element lineage through the pipeline for debugging and traceability.","intents":["I need to build a custom parsing pipeline for domain-specific document formats","I want to inject custom preprocessing or postprocessing steps into the parsing workflow","I need to debug parsing issues by tracing element transformations through the pipeline"],"best_for":["teams with specialized document formats requiring custom parsing logic","organizations building proprietary document processing systems","developers extending unstructured for domain-specific use cases"],"limitations":["Plugin API documentation may be limited; requires understanding internal architecture","Custom plugins must handle error cases; no automatic error recovery","Pipeline composition is code-based; no visual pipeline builder or configuration language","Performance debugging requires manual instrumentation; no built-in profiling","Plugin versioning and compatibility management is manual"],"requires":["Python 3.8+","understanding of unstructured element model and parser architecture","optional: knowledge of middleware patterns and dependency injection"],"input_types":["custom parser implementations","processor/transformer functions","pipeline configuration (ordering, dependencies)"],"output_types":["composed parsing pipeline","element lineage and transformation logs","custom element types and metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-unstructured__cap_9","uri":"capability://data.processing.analysis.format.specific.parser.optimization.and.configuration","name":"format-specific parser optimization and configuration","description":"Exposes format-specific parser configuration options (PDF extraction strategy, HTML parsing mode, table detection sensitivity) enabling users to optimize parsing behavior for their document characteristics. Supports multiple parsing backends for the same format (e.g., PyPDF2 vs pdfplumber for PDFs) with automatic fallback on parsing failures.","intents":["I need to optimize PDF parsing for scanned documents vs digital PDFs","I want to configure table detection sensitivity for documents with complex layouts","I need to switch between parsing backends when one fails on specific document types"],"best_for":["teams with specialized document characteristics requiring parser tuning","organizations processing diverse document quality (scanned, digital, mixed)","developers optimizing parsing performance for specific use cases"],"limitations":["Configuration options are parser-specific; no unified configuration interface","Optimal settings vary by document characteristics; no automatic tuning","Fallback logic may mask underlying parsing issues rather than surfacing them","Performance impact of different configurations not documented; requires empirical testing","Some backends may not support all configuration options"],"requires":["Python 3.8+","knowledge of underlying parser capabilities (PyPDF2, pdfplumber, lxml, etc.)","optional: multiple parser backends installed for fallback support"],"input_types":["parser configuration dictionary","format-specific options (PDF extraction mode, table sensitivity, etc.)"],"output_types":["parsed elements with selected parser metadata","fallback information (which backend was used)","parsing performance metrics"],"categories":["data-processing-analysis","configuration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyPDF2 or pdfplumber for PDF parsing","lxml for XML/HTML parsing","python-docx for DOCX support","Pillow for image processing","parsed document elements from unstructured parsing pipeline","parsed elements with formatting and positional metadata","embedding model API key (OpenAI, Hugging Face, etc.) or local model","vector database client library (pinecone, weaviate, chromadb, etc.)","optional: langchain or llama-index for higher-level RAG integration"],"failure_modes":["OCR capabilities limited to basic image text extraction; no advanced vision model integration for complex layouts","Table extraction accuracy varies significantly by format and complexity; nested tables may be flattened incorrectly","Large PDFs (>500MB) may cause memory overhead due to full document loading before parsing","Scanned PDFs without embedded text require external OCR service integration (not built-in)","Chunking strategy is rule-based; no learned boundaries from domain-specific training data","Overlap configuration is manual; no automatic optimization for specific embedding models","Complex nested structures (deeply nested lists, multi-level tables) may not chunk optimally","No built-in support for language-specific semantic boundaries (e.g., sentence-level for CJK languages)","Hierarchy reconstruction is heuristic-based; accuracy varies by document format and structure","Complex or non-standard document structures may be misinterpreted","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","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-unstructured","compare_url":"https://unfragile.ai/compare?artifact=pypi-unstructured"}},"signature":"NdmQLviTQHWOFvTFCFGLliECoaf3CLyJPMpFhVmtcdR3jKFjWggEOTPJRE8m6KpXPAfaaBWgfdVKXuCTU7tiDQ==","signedAt":"2026-06-20T20:01:45.395Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-unstructured","artifact":"https://unfragile.ai/pypi-unstructured","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}