Unstructured.io: Document Parsing for RAG

Unstructured.io is a document parsing service that turns messy files — PDFs, Word documents, PowerPoint decks, images, HTML — into clean, structured text and metadata. The output is optimized for chunking, embedding, and retrieval in RAG and agent pipelines.

• Your agent reads documents that are not plain text or Markdown.
• You need to preserve tables, headings, lists, and semantic structure during extraction.
• You are building a knowledge base or document Q&A agent over heterogeneous file types.
• You want to stop writing custom parsers for every new document format.

• Multi-format support. PDF, DOCX, PPTX, XLSX, images, HTML, and more.
• Semantic chunking. Output includes element types like headings, tables, and narrative text.
• Table and image extraction. Preserves structured data and can describe embedded images.
• API and self-hosted options. Cloud API for speed, open-source libraries for control.
• Optimized for embeddings. Chunks are designed to feed vector databases cleanly.

• Cloud pricing per page. Large document collections add up fast.
• Quality varies by format. Scanned PDFs and complex layouts are harder than clean Word docs.
• Not a reasoning layer. It extracts content; what your agent does with it is still up to you.
• Enterprise lock-in. Advanced partitioning and scale features sit behind paid tiers.

• Open-source libraries are free to run locally.
• Cloud API starts around $0.10 per page for basic extraction.
• Enterprise plans with higher volume, SLAs, and custom models are priced by contract.

Agents and RAG systems that ingest documents as a core input. If your knowledge base lives in PDFs, contracts, slides, or scanned reports, Unstructured is usually the cleanest path to embeddings.

• Pinecone / Chroma / Weaviate for storing chunked embeddings.
• LangChain / LlamaIndex document loaders.
• OpenClaw / Hermes knowledge-base agents.