RAG и базы знаний
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 тыс. звёздApache-2.0 + terms
Смотреть руководство →knowhere — проект с открытым исходным кодом из категории «RAG и базы знаний» от Ontos-AI (Ontos-AI/knowhere на GitHub). У него 3 681 звезда на GitHub и 338 форков, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 8 октября 2026.
Knowhere turns documents into structured, hierarchy-native chunks ready for agents and RAG.
Decide the first concrete outcome you want from it, so you can pick the right example, template or mode instead of exploring everything at once.
Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.
Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.
Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.
Vision and Text tracks converge into the same chunk and metadata schema, so downstream retrieval is format-independent.
Section nodes preserve document paths, page ranges, summaries, entities and linked assets instead of disconnected chunks.
Agents choose how to search, traverse, read and cite using outline, filter, exact/fuzzy search and reading tools — also exposed over MCP.
Page-derived entities and keywords connect related documents across a namespace.
Results retain source documents, section paths, page numbers and rendered visual evidence.
The pipeline processes long-form PDFs of hundreds of pages and routes technical atlases through a layout-aware parser.
Get the source so you can inspect, configure and run it locally.
git clone https://github.com/Ontos-AI/knowhere.git
cd knowhereInstall uv and Docker, sync the workspace, copy the env examples and add at least one LLM provider key.
uv sync --all-packages
cp apps/api/.env.example apps/api/.env
cp apps/worker/.env.example apps/worker/.envStart the local stack with the provided script, or run the API and worker apps directly.
./deploy/local-dev/start-dev.sh # see README for detailsIt is maintained by Ontos-AI on GitHub (Ontos-AI/knowhere), where the source, issues and releases live.
Yes — it is free and open source under Apache-2.0, which permits use, modification and commercial redistribution, with licence/notice retention.
Yes — parsing and retrieval use LLM/vision providers, and the README lists keys such as DS_KEY, ALI_API_KEYS, GPT_API_KEY or GLM_API_KEY (at least one required).
Compared with plain chunk-and-embed pipelines such as LlamaIndex parsing alone, knowhere's distinguishing focus is: knowhere turns documents into structured, hierarchy-native chunks ready for agents and RAG. Pick it when that focus matches your need; the alternative may suit you better if its broader or different scope is the priority.
Clone https://github.com/Ontos-AI/knowhere (or install its published package), follow the README quick start to install and configure it, then run the smallest documented example — the Run section on this page mirrors those steps.
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
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Другие проекты из категории «RAG и базы знаний» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если knowhere вам не подходит.
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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
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Соседние проекты из категории «RAG и базы знаний» в каталоге, ближайшие по числу звёзд к knowhere — продолжайте изучать полку по обе стороны от него.
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