RAG e conhecimento
opendataloader-pdf
Turn PDFs into AI-ready data: extract Markdown, JSON and tables — and auto-tag for accessibility
★ 29,5 mil estrelasApache-2.0
Ver o guia →Marker é um projeto de RAG e conhecimento de código aberto de datalab-to (datalab-to/marker no GitHub). Tem 40.648 estrelas no GitHub e 2.945 forks, é publicado sob a licença Apache-2.0 e foi atualizado pela última vez no GitHub em 22 de outubro de 2026.
Marker — converts PDFs to Markdown and JSON quickly and accurately, with layout, tables and math intact, Apache-2.0
The base package covers PDF conversion from PyPI.
Documents beyond PDFs need the full dependency set.
On an NVIDIA GPU use vLLM with Docker and the NVIDIA Container Toolkit; on CPU or Apple Silicon install the llama-server binary from llama.cpp.
Layout, tables and math survive as structured Markdown and JSON, not flattened text.
Balanced mode for maximum quality on GPU; fast mode for CPU-friendly conversion with targeted repair of bad blocks.
Layout detection and OCR come from dedicated vision models, with re-OCR of pages whose embedded text is unreliable.
Output is made to feed search, RAG and dataset tooling, not just to be read once.
The README install route from PyPI.
pip install marker-pdfAdds support for documents other than PDFs.
pip install marker-pdf[full]The llama-server binary Marker needs on non-GPU machines.
brew install llama.cppThe datalab-to team, built on the Surya vision models.
Yes — Apache-2.0 licensed.
No — fast mode is optimised for CPU, with llama.cpp as the backend; balanced mode is best on an NVIDIA GPU with vLLM.
Markdown and JSON with layout, tables and math preserved — structured output for search and RAG, not a plain text dump.
pip install marker-pdf (or marker-pdf[full] for non-PDF documents), set up the vLLM or llama.cpp backend, and convert.
Turn PDFs into AI-ready data: extract Markdown, JSON and tables — and auto-tag for accessibility
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Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.