qmd-search

qmd Search

Search a local markdown knowledge base semantically with qmd. Five modes — BM25 keywords, vector similarity, hybrid (expansion + rerank), literal native-script grep, and a fused find — all running on-device. The key advantage over Obsidian's built-in search: it matches meaning, finds notes that share no words with the query, and works across languages (e.g. a Russian query retrieves English notes).

When to use which mode

• hybrid (query) — default. A real question or fuzzy intent ("how do I stop overengineering"). Best quality; first run downloads reranker/expansion models (~one-time slow).
• vector (vsearch) — fast concept lookup ("notes about embodied computing").
• BM25 (search) — an exact keyword, name, or filename. Instant, no model.
• grep (-m grep) — literal fixed-string ripgrep over the .md files. The audit path for proper nouns, transliterations, exact phrases, Russian stems/inflections, and absence checks. Bypasses the index; matches only the exact script/spelling you type.

Bilingual / proper-name rule (do not skip)

This vault is bilingual (English/Russian). The embedding model is decent for concepts but weak for proper nouns / specific entities, and BM25 only matches the script you type. So:

Never conclude "it's not in the vault" after one English semantic query. For names, people, pets, places, foreign terms, or bilingual topics:

1. Search semantically first (query / vsearch).
2. Generate likely native-script spellings/stems and try them, e.g. Ziggy → Зигги/Зиги, dog/pet → собак, пёс, щенок, питомц, животн. Use stems (собак catches собака/собаку/собаки), not just the nominative.
3. Run a literal pass before concluding absence: qmd-search.sh -m grep -n 20 "Зигги".
4. Use literal hits to disambiguate close names (e.g. Зигги the pet vs. Зигмунд Freud).
5. If everything fails, say "I didn't find it with these queries: …" and list the terms tried — not "it's not in the vault." Raise -n to ~20 for absence checks.