RAG y conocimiento
MinerU
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
★ 82,5k estrellasApache-2.0 + terms
Ver la guía →opendataloader-pdf es un proyecto de RAG y conocimiento de código abierto de opendataloader-project (opendataloader-project/opendataloader-pdf en GitHub). Tiene 29.499 estrellas en GitHub y 2.809 bifurcaciones, se publica bajo la licencia Apache-2.0 y se actualizó por última vez en GitHub el 8 de octubre de 2026.
Turn PDFs into AI-ready data: extract Markdown, JSON and tables — and auto-tag for accessibility
Decide what you want to achieve with opendataloader-pdf and check the README on GitHub for requirements before installing.
Clone the repository from GitHub (or take the packaged install route in step 3 if the README offers one).
Follow the project's documented install commands — the Run section below lists the exact ones — and add any required API keys or models.
Work through the README's quickstart example end to end, then adapt it to your own project or data.
Outputs Markdown, JSON and HTML with tables, headings hierarchy and bounding boxes preserved.
Extracts text in the order a human reads it, including multi-column layouts.
Generates tagged PDFs to support PDF/UA and European Accessibility Act (EAA) workflows.
Complex pages can be routed to an AI-backed mode the project positions for top extraction accuracy.
Pluggable OCR engines (RapidOCR, Tesseract, Apple Vision, Nemotron OCR options) handle image-only pages.
Server-side enrichment can extract formulas as LaTeX and describe charts and images.
Get the parser source and documentation.
git clone https://github.com/opendataloader-project/opendataloader-pdf.git
cd opendataloader-pdfInstall from PyPI (a JVM is used under the hood for conversion).
pip install -U opendataloader-pdfRun the CLI on a file or a whole folder to produce structured output.
opendataloader-pdf file1.pdf file2.pdf folder/opendataloader-pdf is maintained by the opendataloader-project organization/account on GitHub, together with open-source contributors, in the public repository opendataloader-project/opendataloader-pdf.
Yes — the code is open source under the Apache-2.0 license. You can use, modify and redistribute it under that license's terms.
The core parser is free and Apache-2.0 with no API key; the optional hybrid/AI enrichment and PDF/UA export features are positioned as server/enterprise options in the README's capability matrix.
Those extract raw text cheaply but lose layout and reading order; OpenDataLoader focuses on structure — order, tables, headings and tags — that downstream AI actually needs.
Clone the repository from GitHub, follow the install commands in the Run section (taken from the project's README), and complete the documented quickstart example before adapting it to your own use case.
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Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.