Under the hood.
Technical deep-dives into how note.md indexes PDFs, retrieves passages, and runs local language models — entirely on your Mac.
The local AI architecture behind note.md
How on-device indexing, retrieval, and inference fit together to power semantic search, Matrix extraction, and Evidence Scan — without sending a single byte off your Mac.
Four pipelines, one local index.
Each pipeline is described step-by-step. The technical details — chunk sizes, models, ranking math — are included so you can reason about quality, not just trust a black box.
Source indexing: turning PDFs into a local knowledge index
Every imported PDF is extracted, chunked, embedded, and full-text indexed locally. Here is the pipeline that makes search, Matrix, and Evidence Scan possible.
Hybrid semantic search: meaning + keywords, fused
Vector similarity and BM25 keyword matching run side by side, then merge into a single ranked list. The same index, with two different shaping strategies for humans and LLMs.
Matrix extraction: filling research tables with local AI
A row-by-row local LLM pipeline that fills research matrices with structured, verifiable extractions — schema-enforced output, per-cell failure isolation, and authoritative user edits.
Evidence Scan: finding support and contradictions for any claim
Type /scan on a sentence in your article. note.md evaluates it against your indexed literature and inserts typed citations — supports, contradicts, nuanced, or silent.
Local. Private. Yours.
No document, embedding, claim, or model output leaves your Mac. Download note.md and run the pipelines yourself.
Free on the App Store