arturseo-geo / llm-knowledge-base

What is llm-knowledge-base?

This repo delivers a schema standard for LLM-compiled personal knowledge bases, turning raw files like PDFs, articles, and code into a structured Markdown wiki via simple LLM prompts. It solves the gap in workflows like Karpathy's wiki-building idea by adding a spaced repetition learning layer for flashcards, gap tracking, and Socratic quizzes—all without vector databases or heavy infrastructure. Users get a local-first setup with Obsidian integration, where LLMs handle compilation, querying, and incremental updates based on the schema spec.

Why is it gaining traction?

It stands out as an llm knowledge base open source alternative to RAG stacks, outperforming on transparency, editability, and zero-setup cost for 50-2,000 documents—think compiled indexes over black-box embeddings. The hook is the schema standardisation (reminiscent of avro schema github or json schema github actions) that any LLM can follow, plus contamination mitigation and a finetune path to domain-specific models. Developers dig the workflow: drop files, prompt once, then query or review learning queues effortlessly.

Who should use this?

Researchers curating domain wikis on AI alignment or GEO data, solo devs building llm knowledge base rag hybrids without DB overhead, or Obsidian power-users wanting automated flashcards over manual spaced repetition plugins. Ideal for teams doing llm knowledge base construction with Claude or similar, where mastery via gap tracking beats passive retrieval.

Verdict

Promising schema standard for llm knowledge base management at small scale, with solid docs and examples despite 14 stars and 1.0% credibility score signaling early maturity—no tests or broad adoption yet. Try it if you're prototyping personal wikis; contribute to push it toward a true github schema registry contender.

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