{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Ingests and pre-processes unstructured documents (PDFs, HTML, Word, emails, images) into structured elements for machine learning pipelines, supporting 60+ file types with parsing, chunking, and enrichment.","skillfed_tags":["document-processing","data-preparation","llm-pipeline"],"use_cases":["Extract and structure text from PDFs and scanned documents for ingestion into vector databases or RAG systems","Parse email messages and attachments to prepare them for LLM analysis or archival processing","Convert Word documents, PowerPoint slides, and other office formats into structured text for batch processing","Chunk long documents into semantically meaningful segments for embedding and retrieval workflows","Detect and extract tables from PDFs and images for structured data extraction pipelines"],"what_it_does":"Unstructured is a document processing library that converts raw files in many formats into structured, machine-readable elements. It handles PDFs, Word documents, HTML, emails, images, and dozens of other formats, extracting text and metadata while preserving document structure. The library is designed to prepare unstructured data for downstream machine learning tasks, particularly for large language models.\n\nThe package works by partitioning documents into semantic elements (paragraphs, tables, headers, etc.), then optionally enriching and chunking them for vector databases or other ML workflows. It ships with 24 runtime dependencies including parsing libraries (beautifulsoup4, lxml, html5lib), NLP tools (spacy, langdetect), and numerical computing (numpy, numba). Installation requires system-level dependencies for full format support, though a minimal install handles text and markup formats without them.","worth_installing":"Yes, with conditions. Install if you need to process multiple document formats and can manage the 24 runtime dependencies and system-level tools. The active maintenance, permissive license, and broad format support make it a solid choice for LLM data preparation pipelines. Skip if you only need to parse a single format (use a lighter, format-specific library instead) or if your environment cannot accommodate the dependency footprint."},"id":"unstructured","links":{"html":"https://skillfed.io/packages/unstructured","md":"https://skillfed.io/packages/unstructured.md","pypi":"https://pypi.org/project/unstructured/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-03","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"unstructured","python_support":"supports_current","summary":"A library that prepares raw documents for downstream ML tasks."},"popularity":{"monthly_downloads":5518206,"position":2084,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.25.2"}