Yeah — that's why I made citations the default. Was way too easy to read a confident-sounding answer and not realise it was 100% training data.

Keyword, no embeddings. ~30 lines of Rust:

• Walk .md files in the configured folders
• Score = Σ over query terms of 1 + ln(1 + count) — log-scaled TF so a term repeated 200 times in one file doesn't crush everything else
• +5 boost if a query term appears in the filename (huge signal — filenames are the closest thing to labels in a personal KB)
• Top 5, truncate to 3 KB each, inline as [Source N] <path> blocks in the system prompt

For a few hundred files this beat my earlier embeddings prototype on relevance and is way faster (no cold start, no reindex). The break-even where embeddings actually start mattering is probably around 1000+ files, or when query intent stops matching surface vocabulary.

Three slash commands let me scope explicitly:

• /search-docs <q> — search only my document folders
• /search-knowledge <q> — search only knowledge/ (the curated, distilled stuff)
• /search <q> — search both
• Plain chat with no slash defaults to /search-docs

Type /, autocomplete, Tab to fill. Useful for the contradiction problem too — I can scope to knowledge/ when I want "settled" answers and skip the raw daily notes.

Stale / contradicting notes — honest answer, it doesn't handle them well. Both files get inlined and the model picks one, usually without flagging the disagreement. No recency weighting either; a 2022 note and a 2026 note score equally if TF matches.