Unstructured
Enterprise document ingestion & unstructured ETL pipeline for RAG
About Unstructured
Unstructured is the leading enterprise ETL (Extract, Transform, Load) platform engineered to prepare messy, unstructured business documents for Retrieval-Augmented Generation (RAG) and LLM fine-tuning. Over 80% of enterprise data lives in complex formats like scanned PDFs, PowerPoint decks, Word files, HTML tables, and email threads that break standard text scrapers. Unstructured utilizes specialized computer vision and vision-language models to segment documents into structural semantic elements (titles, paragraphs, headers, embedded tables, and image captions) while preserving exact spatial and hierarchical context. Available as an open-source Python library and a high-throughput serverless cloud API, Unstructured integrates directly with LangChain, LlamaIndex, and major vector databases to power mission-critical enterprise knowledge retrieval.
Unstructured processes more than 25 document formats through a multi-stage layout detection pipeline. Its vision models detect complex multi-column layouts, rotated text, complex mathematical notation, and embedded charts. For tabular data, Unstructured extracts full HTML and Markdown table structures, ensuring that financial balance sheets and technical specifications retain exact row-and-column relationships when embedded into vector stores. Unstructured includes automated semantic chunking strategies that respect document boundaries (chunk_by_title), preventing context fragmentation and maximizing retrieval accuracy in downstream RAG applications.
Install the open-source `unstructured` Python package or connect via the Serverless REST API.
Pass PDFs, DOCX, PPTX, or HTML files into the `partition` ingestion pipeline.
The layout detection engine identifies titles, body text, tables, and visual elements.
Apply semantic chunking to group related sections without splitting across logical boundaries.
Export structured JSON chunks directly into vector databases like Pinecone, Weaviate, or Qdrant.
Capabilities & Features
Common Use Cases
pdf-parsing
rag-data-prep
table-extraction
enterprise-search
Frequently Asked Questions
Can Unstructured process scanned PDF documents with OCR?
Yes, Unstructured includes built-in OCR engines (Tesseract and vision-language models) to extract text and tables from scanned receipts, invoices, and legacy PDF scans.
Is Unstructured open source?
Yes, the core `unstructured` ingestion and partitioning library is open-source on GitHub with millions of monthly downloads.
How does Unstructured preserve table structure for LLMs?
Unstructured identifies table boundaries and converts data cells into clean HTML and Markdown tables so that LLMs can understand structured row-column relationships.
Free Plan
Open-source Python library 100% free; Unstructured Serverless API includes 1,000 free processed pages per month.
Paid Plan
Pay-as-you-go pricing at $0.01 per processed page with OCR, table extraction, and enterprise SOC2 compliance.
Pros & Cons
Supports 25+ document file types including scanned PDFs, PPTX, and HTML
Advanced table extraction preserving exact structural row-and-column hierarchies
Open-source core library with complete on-premise execution support
Pre-built native connectors for LangChain, LlamaIndex, Databricks, and S3
Enterprise-grade SOC2 Type II compliance and zero data retention options
Heavy OCR computer vision dependencies require dedicated GPU resources for local batch jobs
Complex document schemas require tuning chunking parameters for optimal RAG retrieval
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