RAG et connaissances
MinerU
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
★ 82,5 k étoilesApache-2.0 + terms
Voir le guide →PaddleOCR est un projet de RAG et connaissances open source, créé par PaddlePaddle (PaddlePaddle/PaddleOCR sur GitHub). Il compte 90 781 étoiles sur GitHub et 11 456 forks, il est publié sous la licence Apache-2.0 et a été mis à jour pour la dernière fois sur GitHub le 16 septembre 2026.
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
Decide what you want from PaddleOCR and check the README and requirements to confirm it fits your setup and Python environment.
Clone the repository from GitHub (github.com/PaddlePaddle/PaddleOCR) or use the documented install channel if you do not need the source.
Follow the README quickstart to install dependencies and set any required configuration or credentials.
Work through the README's first example end to end, then adapt it to your own project or data.
Supports text recognition in over 100 languages for documents and scene images.
PP-Structure and PaddleOCR-VL turn PDFs and images into structured data and Markdown for LLMs.
Scene OCR handles photos and real-world images, not just clean scans.
Includes KIE capabilities for extracting fields from forms and documents.
Part of the PaddlePaddle ecosystem with models, deployment tools and documentation.
Designed as a powerful but lightweight toolkit that runs without a large GPU.
Start by getting the source and entering the project directory.
git clone https://github.com/PaddlePaddle/PaddleOCR.git
cd PaddleOCRInstall the Python package from PyPI (the README's local deployment path).
pip install paddleocrUse the CLI/API on a first document or image after installing.
paddleocr ocr -i ./example.jpg # see README for the exact commandDocuments end up in knowledge bases as well as pipelines: AppFlowy-Web gives projects, wikis and team docs an open-source AI workspace, while Compiiile turns a folder of markdown into a deployed site in one move. Readers working through the RAG & Knowledge shelf also stop at paper-qa, PaperQA2 — high-accuracy RAG for answering questions from scientific papers, with citations you can check.
It is maintained by PaddlePaddle and contributors on GitHub, where the repository github.com/PaddlePaddle/PaddleOCR is the canonical home for code, issues and releases.
Yes — it is open source under the Apache-2.0 license. Read the license in the repository for what is permitted, including any conditions on redistribution or commercial use.
No. PaddleOCR runs locally and needs no API key; optional hosted demos are separate.
Compared with Tesseract: PaddleOCR offers deep-learning models, layout and structure parsing and 100+ languages out of the box, beyond Tesseract's classic OCR engine.
Clone github.com/PaddlePaddle/PaddleOCR, follow the README quickstart to install it, and run the documented first example before adapting it to your own work.
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Turn PDFs into AI-ready data: extract Markdown, JSON and tables — and auto-tag for accessibility
Knowhere turns documents into structured, hierarchy-native chunks ready for agents and RAG.
Ready-to-run templates for RAG, AI pipelines and enterprise search that stay in sync with live data
Polyglot document intelligence in Rust — extract text, tables and structured data from 106 formats.
Open-source engine turning continuous content streams into search, monitoring and alerts
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MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
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Private document chat for attorneys: local LLMs, verifiable citations.
Ready-to-run templates for RAG, AI pipelines and enterprise search that stay in sync with live data
Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.