Unstructured

Unstructured

Unstructured is the leading open-source library for ingesting and preprocessing unstructured documents for use in LLM applications. It partitions documents into typed elements (Title, NarrativeText, Table, Image, etc.), extracts metadata, and cleans text — making raw files ready for chunking and embedding.

Key Features

• 25+ file formats — PDF, HTML, DOCX, PPTX, XLSX, EML, MSG, MD, RST, TXT, images, and more
• Partition strategies — fast (rule-based), hi_res (layout detection with detectron2), ocr_only
• Element types — Title, NarrativeText, Table, ListItem, Image, Header, Footer, FigureCaption
• Table extraction — HTML table extraction from PDFs with hi_res strategy
• LangChain / LlamaIndex connectors — UnstructuredLoader is a first-class integration in both
• Connectors — S3, GCS, Azure Blob, Confluence, Google Drive, SharePoint, Slack, and more

Quick Start

pip install unstructured[pdf]

from unstructured.partition.pdf import partition_pdf

elements = partition_pdf("research_paper.pdf", strategy="hi_res")
for el in elements[:5]:
    print(el.category, ":", str(el)[:80])

Install via ai-supply

npx ai-supply add unstructured-document-ingestion

Curated mirror of the open-source Unstructured project (Apache-2.0). Install upstream from the repository.