Principle:Unstructured IO Unstructured Document Partitioning
| Knowledge Sources | |
|---|---|
| Domains | Document_Processing, NLP, Information_Extraction |
| Last Updated | 2026-02-12 00:00 GMT |
Overview
A transformation process that converts raw unstructured documents into ordered sequences of typed, structured elements with rich metadata.
Description
Document partitioning is the core operation in unstructured data processing. Given a raw document in any supported format (PDF, DOCX, HTML, PPTX, CSV, etc.), the partition process:
- Detects the document format
- Selects the appropriate parsing strategy
- Extracts content into typed elements (Title, NarrativeText, Table, Image, ListItem, etc.)
- Enriches each element with metadata (page number, coordinates, language, source info)
This principle solves the fundamental challenge of transforming heterogeneous document formats into a uniform, machine-readable representation. The output is a flat list of Element objects that preserve document structure through element types and metadata rather than nested hierarchies.
Usage
Use this principle as the primary entry point for any document processing pipeline. It applies when you need to convert raw documents into structured data for downstream tasks such as RAG (Retrieval-Augmented Generation), search indexing, knowledge extraction, or data migration. The auto-routing capability makes it suitable for pipelines that handle mixed document formats without format-specific preprocessing.
Theoretical Basis
Document partitioning combines multiple techniques depending on the document format and selected strategy:
Digital document parsing: For documents with embedded text (born-digital PDFs, DOCX, HTML), content is extracted using format-specific parsers that understand the document's internal structure. This preserves text fidelity and can extract metadata like fonts, styles, and links.
Layout analysis: For documents requiring spatial understanding (scanned PDFs, images), computer vision models detect document regions by classifying bounding boxes into categories (title, paragraph, table, figure). This transforms pixel data into structured regions.
OCR (Optical Character Recognition): For regions without embedded text, OCR converts image pixels to text. Modern OCR combines neural networks for text detection with language models for character recognition.
Format routing: A dispatcher function maps file types to format-specific partitioners. Each partitioner implements the same interface (returns list[Element]) but uses format-appropriate extraction logic.
Pseudo-code logic:
# Abstract partitioning algorithm
file_type = detect_filetype(document)
partitioner = get_partitioner_for_type(file_type)
elements = partitioner.partition(
document,
strategy=strategy,
languages=languages,
)
# Each element has: type, text, metadata (page, coordinates, etc.)
return elements