// rag · VectifyAI/OpenKB

OpenKB

OpenKBは、VectifyAI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: VectifyAI/OpenKB）。GitHubスターは4,759、フォークは495で、Apache-2.0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7月22日です。

OpenKB compiles your documents into a persistent, interlinked wiki knowledge base — no vector DB

★ 4.8k スター⑂ 495 フォークApache-2.0更新 2026年7月

GitHubで見る ↗Official site ↗

▦ ビジュアル — このプロジェクトのREADMEには埋め込み可能なスクリーンショットがないため、
ここではプレースホルダーを表示しています。下のガイド自体は完全な内容です。

// プロジェクトの基本情報

プロジェクト基本情報

リポジトリ: VectifyAI/OpenKB
GitHubスター: 4,759
フォーク: 495
ライセンス: Apache-2.0
GitHubでの最終更新: 2026年7月22日
カテゴリ: RAG・知識ベース
掲載タイプ: 完全なインストールガイド
公式サイト: Official site

// 仕組み

仕組み

Gather your documents
Collect the papers, docs, or URLs you want compiled into one knowledge base.
Install and initialize
Install OpenKB from PyPI, create a folder, and run the init command, choosing your LLM model.
Add documents
Add files, a whole directory, or a URL; OpenKB compiles them into wiki pages with cross-links.
Query and chat
Ask questions with cited answers, chat interactively, or generate outputs like skills and slide decks.

// 機能

選ばれる理由

Compiled wiki, not chunk RAG

Documents become summary, concept, and entity pages with cross-references that accumulate over time; contradictions get flagged.

Vectorless long-document retrieval

PageIndex tree indexing handles long PDFs by reasoning over a hierarchical index — no vector database required.

Broad format support

PDF, Word, Markdown, PowerPoint, HTML, Excel, CSV, text, and URLs, with native handling of figures, tables, and images.

Query and chat with sessions

One-off cited answers or multi-turn chat over the wiki, with persisted sessions you can resume.

Skill Factory and generators

Distill redistributable agent skills from the wiki, visualize an interactive knowledge graph, or generate single-file HTML slide decks.

Obsidian-compatible and OKF-ready

The wiki is plain Markdown with wikilinks that opens in Obsidian, and pages follow Google's Open Knowledge Format specification.

// 自分で実行

自分で実行してみる

Clone the repository
Clone for a source install, or simply install from PyPI in the next step.
```
git clone https://github.com/VectifyAI/OpenKB.git
cd OpenKB
```
Install OpenKB
Install the package (or `pip install -e .` from the clone for development).
```
pip install openkb
```
Initialize and query
Create a knowledge base, add a document, and ask your first question (set your LLM API key in .env first).
```
openkb init
openkb add paper.pdf
openkb query "What are the main findings?"
```

// 活用例

作れるもの

Turn a folder of research papers into a searchable, cross-linked wiki
Maintain a team knowledge base that stays current as new documents are added
Ask cited questions over very long PDFs that exceed model context windows
Distill a book's worth of material into a portable agent skill

// よくある質問

よくある質問

Who maintains OpenKB?

VectifyAI, the team behind PageIndex; OpenKB is powered by PageIndex's open-source vectorless retrieval.

Is OpenKB free?

Yes, it is Apache-2.0 licensed. You pay only for the LLM API usage of the provider you configure (OpenAI, Claude, Gemini, and others via LiteLLM); some subscription providers can authenticate via OAuth instead of a key.

Does it need a paid API key?

It needs an LLM — usually your own API key set in .env. PageIndex runs locally by default; the optional PageIndex Cloud features need a separate key.

How does it compare to traditional RAG tools?

RAG rediscovers knowledge from chunks on every query and nothing accumulates. OpenKB compiles knowledge once into a persistent wiki with cross-references already built, then keeps it current as documents are added.

How do I get started?

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 — OpenKB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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MinerU★ 82.5k
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms

// さらに見る rag

さらに見る： RAG・知識ベース

カタログの中でOpenKB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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OpenKBを入手する

無料でオープン — ソースコードを入手して、リポジトリにスターを付け、作り始めましょう。

GitHubで見る ↗← RAG・知識ベースのツールをさらに見る