Document ETL platform for parsing and chunking enterprise content.
Converts messy documents — PDFs, Word files, images — into clean data your AI can understand and search through.
Unstructured is the leading open-source platform for converting messy enterprise documents — PDFs, Word files, PowerPoint decks, HTML pages, images, emails — into clean, chunked text ready for embedding and retrieval. It solves the unglamorous but critical problem that most enterprise data isn't neatly formatted text; it's trapped in complex document layouts with tables, headers, footers, multi-column formats, and embedded images.
Unstructured's core library provides a universal partition() function that detects document type, applies the appropriate parser (including OCR for scanned documents), and outputs structured elements: titles, narrative text, tables, list items, and images, each classified by type and position within the document hierarchy. This element-based output is significantly more useful than raw text extraction because it preserves document structure.
The chunking system is Unstructured's second major contribution. Rather than naively splitting text at fixed character counts, Unstructured's chunkers respect document structure — they chunk by section, keep table rows together, and maintain list coherence. This produces chunks that are semantically meaningful units rather than arbitrary text splits, which directly improves retrieval quality in RAG systems.
Unstructured offers three deployment modes: the open-source Python library for local processing, the Unstructured API (hosted service with higher throughput and additional model capabilities), and Unstructured Platform (an enterprise product with connectors, workflow management, and monitoring). The API and Platform use more sophisticated models for table extraction and OCR than the open-source library.
The connector ecosystem is extensive — Unstructured provides source connectors for S3, Google Drive, SharePoint, Confluence, Salesforce, and more, plus destination connectors for Pinecone, Weaviate, Chroma, Elasticsearch, and other vector databases. This creates a document ETL pipeline: source → extract → chunk → embed → load.
The honest assessment: Unstructured is excellent for standard business documents but struggles with highly specialized formats. Complex scientific papers with equations, architectural drawings, or heavily formatted spreadsheets can produce messy output. Table extraction quality varies significantly between the open-source library (basic) and the API (much better). OCR accuracy depends heavily on document scan quality. Despite these limitations, Unstructured handles the 80% of enterprise document types that matter for most RAG applications better than any alternative.
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Unstructured is the most comprehensive document processing pipeline, handling virtually every document format and producing clean, structured output suitable for RAG systems. The open-source library works well for common formats, while the managed API adds OCR, table extraction, and high-volume processing. Users note that output quality varies significantly by document type and complexity — simple text documents work perfectly, while complex PDFs with tables and images require more tuning. The API pricing can be significant at scale.
The partition() function auto-detects document types and applies appropriate parsers for 30+ file formats. Output is structured elements (Title, NarrativeText, Table, ListItem, Image) with metadata including page number, coordinates, and parent hierarchy.
Use Case:
Processing a SharePoint library containing a mix of PDFs, Word docs, and PowerPoint files into a unified, structured representation for a RAG system.
Chunking strategies that respect document hierarchy: by_title chunks at section boundaries, by_page maintains page-level coherence, and table elements are kept intact. Overlap and max-size parameters are configurable.
Use Case:
Chunking a 200-page technical manual so that each chunk represents a complete section or subsection, preserving the logical structure for retrieval.
Extracts tables from PDFs and images as structured data with row/column relationships preserved. The API version uses vision models for more accurate extraction from complex table layouts.
Use Case:
Extracting financial tables from annual reports where accurate column alignment and number extraction are critical for downstream analysis.
Integrated OCR for scanned documents and images using Tesseract (open-source) or cloud OCR services. Supports multi-language OCR with configurable language packs and preprocessing for scan quality improvement.
Use Case:
Processing a backlog of scanned contracts to extract text and make them searchable in a legal document retrieval system.
Pre-built connectors for 20+ data sources (S3, GCS, Azure Blob, SharePoint, Google Drive, Confluence, Slack) and vector database destinations (Pinecone, Weaviate, Chroma, Qdrant, Elasticsearch).
Use Case:
Building an automated pipeline that pulls new documents from Confluence, processes them through Unstructured, and loads chunks into Pinecone nightly.
Extracted elements include rich metadata: source file, page number, element type, coordinates on page, parent section, language detection, and optional regex-based entity extraction for emails, phone numbers, and dates.
Use Case:
Using element metadata to filter retrieval results by page number or section, enabling citations like 'See Section 3.2, page 45' in AI-generated responses.
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View Pricing Options →Enterprise RAG systems that need to process diverse document types from SharePoint, Confluence, Google Drive, and other business sources
Document ETL pipelines that extract, chunk, embed, and load content into vector databases with structure preservation
Legal, financial, or healthcare applications that need to process PDFs with complex tables and maintain extraction accuracy
Organizations building knowledge bases from legacy document collections including scanned papers and archived files
Unstructured works with these platforms and services:
We believe in transparent reviews. Here's what Unstructured doesn't handle well:
The open-source library handles most document types but uses simpler extraction models. The API uses more sophisticated table extraction (vision models), better OCR, and higher-quality element classification. For production RAG systems with complex documents, the API produces noticeably better results.
Yes, through integrated OCR. The open-source version uses Tesseract, and the API uses more advanced OCR models. Quality depends on scan resolution — clean scans at 300+ DPI produce good results. Low-quality scans, handwriting, or unusual fonts degrade accuracy.
Unstructured handles a wider range of document formats (not just PDFs) and provides more deployment flexibility (local, API, enterprise). LlamaParse often produces better results for complex PDFs with tables and figures because it uses LLM-powered extraction. For PDF-heavy workloads, test both; for multi-format document ETL, Unstructured is more comprehensive.
The open-source library processes roughly 1-5 pages per second depending on complexity and whether OCR is needed. The API is faster with parallelization. For large collections (10K+ documents), use the Platform product or batch API with concurrent requests.
It preserves structural elements (headers become Title elements, lists become ListItem elements) but not inline formatting like bold or italic. The output is semantic elements with types, not formatted text. This is by design — the element classification is more useful for RAG than formatting preservation.
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