RAG & Wissen
knowhere
Knowhere turns documents into structured, hierarchy-native chunks ready for agents and RAG.
★ 3,7 Tsd. SterneApache-2.0
Anleitung ansehen →MinerU ist ein Open-Source-Projekt aus der Kategorie RAG & Wissen von opendatalab (opendatalab/MinerU auf GitHub). Es hat 82.521 Sterne auf GitHub und 6.817 Forks, wird unter der Lizenz Apache-2.0 + terms veröffentlicht und wurde zuletzt am 26. Oktober 2026 auf GitHub aktualisiert.
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
The README install route uses uv for a clean virtual environment.
Install the current major line from PyPI into that environment.
Parse a PDF to Markdown at the standard tier, or start the built-in web UI.
Tables, formulas, figures and reading order survive the conversion — not just a text dump.
Page and block locators let an agent re-read exactly the passage it cited, instead of re-parsing the whole file.
The standard tier balances accuracy and speed; the README documents other tiers and runtimes for heavier jobs.
Use it from the command line, drive a local document library from Python, or parse through the browser UI.
The README-preferred install: a Python 3.12 venv, then the 4.x line.
pip install uv
uv venv .mineru --python 3.12
source .mineru/bin/activate
uv pip install -U "mineru>=4.0,<5"The README command: parse a PDF to Markdown at the standard tier.
mineru-kit parse document.pdf -o document.md --tier standardThe built-in browser interface for parsing without the command line.
mineru-kit webuiThe OpenDataLab team, as the document-parsing front end for LLM and agent workflows.
Yes for ordinary use — Apache-2.0 plus additional terms: a separate commercial licence is only required at 100M monthly active users or $20M monthly revenue, and online services built on it must attribute MinerU.
It preserves layout, tables, formulas and reading order, and returns stable page/block locators for citations — a text dump does neither.
No — the README says so plainly: it is a document reader, not a vector database or chat-with-documents app. It feeds RAG systems; it is not one.
Install mineru 4.x with uv into a Python 3.12 environment, then run mineru-kit parse on a PDF.
Knowhere turns documents into structured, hierarchy-native chunks ready for agents and RAG.
Turn PDFs into AI-ready data: extract Markdown, JSON and tables — and auto-tag for accessibility
Marker — converts PDFs to Markdown and JSON quickly and accurately, with layout, tables and math intact, Apache-2.0
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
Ready-to-run templates for RAG, AI pipelines and enterprise search that stay in sync with live data
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
Die anderen Projekte der Kategorie RAG & Wissen mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls MinerU nicht das Richtige ist.
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Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
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Ready-to-run templates for RAG, AI pipelines and enterprise search that stay in sync with live data
Verwandte Projekte der Kategorie RAG & Wissen im Katalog, die MinerU bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
Ready-to-run templates for RAG, AI pipelines and enterprise search that stay in sync with live data
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Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.