Unstructured

Overview

Unstructured is a platform for ingesting and preprocessing unstructured documents into structured data for LLM applications like RAG and business automation. It supports over 64 file types including PDFs, images, HTML, and Word docs, with tools for partitioning, cleaning, extracting, chunking, and staging data. The open-source library handles core ETL functions, while the enterprise platform adds UI, API, source connectors, and scalable pipelines.

Key Features

• Document Partitioning - Extracts structured content and elements from raw unstructured documents like PDFs and images.
• Cleaning - Sanitizes output by removing unwanted content to prepare data for NLP models.
• Extracting - Identifies and isolates specific entities and information from documents.
• Chunking - Divides documents into semantic units based on document format understanding.
• File Type Support - Processes 64+ formats including PDF, HTML, images, Word docs, and more.
• Source Connectors - Ingests data from sources like Box with CLI and Python libraries.
• Enrichments and Embedding - Applies transformations, enrichments, and embeddings for GenAI workflows.

Pricing

Platforms & Requirements

Open-source library runs on Linux, macOS, Windows via Python. Web-based platform accessible via browser with account login. Enterprise features require API keys; no specific hardware minimums listed beyond standard Python environments.

Integrations & Ecosystem

• Box connector
• Vector databases
• Graph databases
• Python CLI
• REST API
• LLM pipelines (RAG)
• Destination connectors (30+)

Alternatives

Reputation

Unstructured is recognized for its robust open-source library and extensive file type support, making it popular for LLM data prep in RAG apps. Users praise the partitioning accuracy and connector integrations, though some note a learning curve for advanced configurations. The enterprise platform receives positive feedback for scalability but criticisms around opaque pricing details.