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Context7
Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors
★ 62.6k スターMIT
ガイドを見る →textbook-to-noteは、drpwchenが公開しているオープンソースのRAG・知識ベースプロジェクトです（GitHub: drpwchen/textbook-to-note）。GitHubスターは104、フォークは25で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年8月26日です。
Self-hosted rag & knowledge: turn your own pdf textbooks into an ai-searchable knowl.
Fetch the source code onto your machine.
Set up the Python environment the project needs.
Copy the sample config or env file and adjust it.
Start the app from the source directory.
Built around claude code for a focused workflow.
Built around note taking for a focused workflow.
Built around obsidian for a focused workflow.
Built around ocr for a focused workflow.
Inspect, fork, and modify every line of the code.
Run it on your own machine — no account lock-in.
Get the source on your machine.
git clone https://github.com/drpwchen/textbook-to-note.gitSet up Python dependencies.
cd textbook-to-note && pip install -r requirements.txtLaunch the application.
cd textbook-to-note && python main.pyIt turn your own PDF textbooks into an AI-searchable knowledge base and structured, fully-cited notes — figures included. Local-first, token-frugal, giving you a working starting point you can run and adapt.
Yes — the code is open-source, so you can use and modify it without a license fee.
Clone the GitHub repository and follow the README; the typical flow is install dependencies, configure, and run.
It is primarily written in Python, so you will need that toolchain to build or extend it.
The GitHub repository is the hub: open issues for bugs and check the README and discussions for guidance.
Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors
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LibreAIカタログに掲載されたRAG・知識ベースプロジェクトの中で最もスターの多いもの — textbook-to-noteが合わなかった場合に検討したい、現実的な候補リストです。
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カタログの中でtextbook-to-noteとスター数が近いRAG・知識ベースプロジェクト — 前後の棚もそのまま見て回れます。
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