RAG et connaissances
opendataloader-pdf
Turn PDFs into AI-ready data: extract Markdown, JSON and tables — and auto-tag for accessibility
★ 29,5 k étoilesApache-2.0
Voir le guide →Marker est un projet de RAG et connaissances open source, créé par datalab-to (datalab-to/marker sur GitHub). Il compte 40 648 étoiles sur GitHub et 2 945 forks, il est publié sous la licence Apache-2.0 et a été mis à jour pour la dernière fois sur GitHub le 22 octobre 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
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Render and deploy a folder of markdown as a site in one move.
A self-hostable markdown team wiki with a built-in MCP server.
Polyglot document intelligence in Rust — extract text, tables and structured data from 106 formats.
Knowledge-base — 本地优先的知识库桌面应用。markdown 编辑器 + 全文搜索（fts5） + 双向链接 / 知识图谱；多端, publicly avai...
Les autres projets de la catégorie RAG et connaissances avec le plus d'étoiles du catalogue LibreAI — la sélection réaliste d'alternatives si Marker ne vous convient pas.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Projets de la catégorie RAG et connaissances proches dans le catalogue, les plus proches en étoiles de Marker — continuez à explorer de part et d'autre.
Meta's library for efficient similarity search and clustering of dense vectors, in C++ and Python
Open-source engine turning continuous content streams into search, monitoring and alerts
Complete API layer for private AI apps on local models: RAG, skills, tools, MCP and text-to-SQL
PageIndex: vectorless, reasoning-based RAG that indexes documents like a human expert would
Ready-to-run templates for RAG, AI pipelines and enterprise search that stay in sync with live data
Local knowledge-base RAG and agent app built on LangChain with ChatGLM, Qwen and Llama models
Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.