Unstructured

Unstructured tackles the messy first step of every RAG pipeline: turning real-world files into clean, structured elements an LLM can use. The open source Python library ingests more than 60 formats, including PDF, DOCX, PPTX, HTML, email, and scanned images, and its partition function auto-detects file type before splitting content into typed elements such as titles, narrative text, and tables. Downstream utilities handle chunking, cleaning, metadata extraction, and staging for vector databases and embedding models. Heavier documents lean on system dependencies like poppler for PDFs and tesseract for OCR, and the stack runs on CPU with Docker images available. The library is released under the Apache 2.0 license, while the company also sells a hosted API and an enterprise pipeline platform for production workloads. Data engineers and LLM application developers typically reach for it when they want one consistent ingestion layer instead of maintaining format-specific parsers.