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**Drop documents in. Get an auto-maintained, cross-linked wiki out.**
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<h3align="center">LLM-Powered Wiki Knowledge Base</h3>
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[Getting Started](#getting-started) · [How It Works](#how-it-works) · [Commands](#commands) · [Configuration](#configuration)
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<palign="center"><i>Scale to long documents ◦ Reasoning-based retrieval ◦ Native multimodality support ◦ No Vector DB</i></p>
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</div>
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---
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# 📑 Introduction to OpenKB
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Andrej Karpathy [described](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) a workflow where LLMs compile raw documents into a structured, interlinked markdown wiki — summaries, concept pages, cross-references — all maintained automatically. Knowledge compounds over time instead of being re-derived on every query.
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**OpenKB** is an open-source CLI that implements this workflow, powered by [PageIndex](https://github.com/VectifyAI/PageIndex) for long document understanding and [markitdown](https://github.com/microsoft/markitdown) for broad format support.
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**OpenKB**(Open Knowledge Base) is an open-source CLI that implements this workflow, powered by [PageIndex](https://github.com/VectifyAI/PageIndex) for long document understanding and [markitdown](https://github.com/microsoft/markitdown) for broad format support.
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### Why not just RAG?
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RAG rediscovers knowledge from scratch on every query. Nothing accumulates. OpenKB compiles knowledge once into a persistent wiki, then keeps it current. Cross-references already exist. Contradictions are flagged. Synthesis reflects everything consumed.
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## Features
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###Features
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-**Any format** — PDF, Word, PowerPoint, Excel, HTML, Markdown, and more via markitdown
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-**Any format** — PDF, Word, PowerPoint, Excel, HTML, Markdown, text, CSV, and more via markitdown
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-**Long documents** — Books and reports that exceed LLM context windows are handled via [PageIndex](https://github.com/VectifyAI/PageIndex) tree indexing
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-**Auto wiki** — LLM generates summaries, concept pages, and cross-links. You curate sources; the LLM does the rest
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-**Query** — Ask questions against your wiki. The LLM navigates your compiled knowledge to answer
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-**Lint** — Health checks find contradictions, gaps, orphans, and stale content
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-**Watch mode** — Drop files into `raw/`, wiki updates automatically
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-**Obsidian compatible** — Wiki is plain `.md` files with `[[wikilinks]]`. Open in Obsidian for graph view and browsing
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##Getting Started
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#🚀 Getting Started
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### Install
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@@ -67,7 +71,7 @@ OPENAI_API_KEY=sk-...
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OpenKB uses [LiteLLM](https://docs.litellm.ai/docs/providers) — any provider works. Set the model during `okb init` or edit `.okb/config.yaml`.
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##How It Works
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#🧩 How It Works
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```
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raw/ You drop files here
@@ -116,7 +120,9 @@ When you add a document, the LLM:
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A single source might touch 10-15 wiki pages. Knowledge accumulates — each document enriches the existing wiki rather than sitting in isolation.
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## Commands
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# 📦 Usage
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### Commands
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| Command | Description |
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|---|---|
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|`okb query "question" --save`| Ask and save the answer to `wiki/explorations/`|
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|`okb watch`| Watch `raw/` and auto-compile new files |
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|`okb lint`| Run structural + knowledge health checks |
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|`okb lint --fix`| Auto-fix what it can |
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<!--| `okb lint --fix` | Auto-fix what it can |-->
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|`okb list`| List indexed documents and concepts |
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|`okb status`| Show knowledge base stats |
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## Configuration
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###Configuration
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Generated by `okb init`, stored in `.okb/config.yaml`:
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At runtime, the LLM reads `AGENTS.md` from disk — your edits take effect immediately.
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## Using with Obsidian
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### Using with Obsidian
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OpenKB's wiki is a directory of Markdown files with `[[wikilinks]]` — Obsidian renders it natively.
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3. Use graph view to see knowledge connections
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4. Use Obsidian Web Clipper to add web articles to `raw/`
Inspired by [Andrej Karpathy's LLM Wiki pattern](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f). Powered by [PageIndex](https://pageindex.ai/).
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