Unstructured.io

Open-source RAG engine for LLM apps with deep document parsing, multi-granularity chunking, hybrid retrieval integrating vector stores (e.g., Elasticsearch), and visual workflow builder.

Apache Arrow is an open-source, self-hosted columnar in-memory data platform for efficient vector data interchange and processing in AI applications. Key features include zero-copy reads, multi-language libraries, and Apache 2.0 license for free use. Used for high-performance data loading in RAG pipelines and ML workflows, offering cost-free scalability vs proprietary formats.

Rust OCR engine for scientific documents, extracting text and mathematical equations to LaTeX, MathML, or plain text. Supports batch processing, content detection for equations/tables/diagrams, confidence scoring, and PDF support. Includes TypeScript client (@ruvector/scipix) and CLI (scipix-cli).

Open-source tool by VectifyAI for pagewise document indexing that converts PDF pages into image representations for downstream multimodal embedding and retrieval. Designed to support late-interaction-based retrieval approaches like ColPali by preserving original document layout and visual structure.

A compound AI system for parsing, chunking, enriching, and storing unstructured documents at scale, trained on 80k+ enterprise documents and delivering up to 6x better accuracy and 5x cost savings compared to alternative systems.

Open-source document parsing framework from IBM with 97.9% accuracy in complex table extraction and excellent text fidelity. Self-hostable solution for converting PDFs, spreadsheets, and scanned images into structured data for RAG pipelines.