Unstructured

Unstructured is an industry-standard data processing library that solves a critical problem in AI: extracting and parsing documents (PDFs, emails, Word docs, HTML pages, images) into clean, structured text suitable for AI applications. Raw documents are messy-they contain formatting, tables, images, and structural elements that confuse AI models. Unstructured extracts meaningful content, preserves document structure, and outputs clean text ready for embeddings, language models, or indexing in vector databases. The library has become essential infrastructure for any AI application that needs to ingest documents, making it the most widely used document processing tool in the AI industry.

How It Works

Install the Unstructured library and call it with your document-specify the file path or provide document bytes. Unstructured automatically detects document type (PDF, HTML, image, etc.) and applies the appropriate parser. The library extracts text, tables, images, and metadata, organizing content hierarchically (title, heading, paragraph, table, etc.). Output the results as structured JSON, markdown, or other formats. For complex document processing at scale, use Unstructured's API service or run the open-source library. The library preserves semantic structure-understanding what's a heading versus body text versus a table-rather than just extracting raw text.

Core Features

• •Multi-Format Support: PDFs, Word documents, HTML, emails, images, and more
• •Intelligent Extraction: Preserves document structure and semantic meaning
• •Metadata Extraction: Extract titles, headings, and structural elements
• •Table Handling: Properly parse and structure table data
• •Image Support: Extract and analyze images within documents
• •Multiple Output Formats: JSON, markdown, and other structured formats
• •Scalable Processing: Open-source library or managed API for processing at scale

Who This Is For

Unstructured is essential for any developer building AI applications that need to ingest documents. It's ideal for companies building RAG systems, document search tools, knowledge base creation systems, and content processing pipelines. It's suited for teams implementing AI features that need to consume documents as input, researchers processing document collections, and organizations automating document analysis. It's valuable for any application where AI needs to understand unstructured document content.