ArticleSimon SpätiTutorial
Users with sensitive knowledge bases often avoid uploading raw notes to hosted AI platforms. This implementation illustrates how data engineering tooling, local vector storage, and in-browser WASM engines can provide private, cost-effective RAG capabilities over personal Markdown archives.
By focusing strictly on the retrieval phase rather than text generation, the setup avoids LLM hallucinations while surfacing forgotten or non-obvious cross-links across thousands of notes. Architecturally, pairing DuckDB for local ingestion with MotherDuck and DuckDB-WASM for client-side querying demonstrates a highly efficient, serverless design for personal knowledge graphs.
Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.
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