Overview
Unstructured is an open-source ETL solution focused on converting complex documents (PDFs, scanned images, Word, HTML, etc.) into clean, structured formats for use in language-model pipelines. The project combines parsing, chunking, and enrichment techniques to prepare documents for retrieval and embedding workflows, from prototypes to production-grade systems.
Key Features
- Multi-format parsing: Supports extraction from PDFs, DOCX, HTML, images and more.
- Data cleansing & chunking: Preprocessing modules that produce segments suited for retrieval and generation tasks.
- Production capabilities: Features for partitioning, enrichment, and embedding-ready output for enterprise workflows.
Use Cases
- RAG / QA systems: Convert unstructured documents into vectorized segments for retrieval-augmented generation.
- Document migration: Extract historical document collections into structured forms for indexing and analysis.
- Data engineering pipelines: Serve as an upstream ETL component that connects parsing, cleansing, and downstream embedding/search systems.
Technical Details
- Stack: Python-first tooling with integrations to common embedding and retrieval stacks (Faiss, Milvus, OpenSearch, etc.).
- Extensibility: Modular design for adding custom parsers and processing steps.
- License & ecosystem: Apache-2.0 license suitable for both community and commercial adoption.