# 40 — Concordance view

`Ctrl+B Shift+L` opens a project-wide concordance:
every distinct word in your manuscript, ranked by
occurrence count, with up to three keyword-in-context
(KWIC) samples per row.

```
┌─ Concordance — project-wide ─────────────────────────┐
│ 1,247 distinct · 19,432 tokens · 84 paragraphs       │
│ filter: walk│  sort: count   (3 shown)               │
│ #     word                       count   variants    │
│   1   walked                       18    (walk, walking)
│ ▶ 2   walking                      11                │
│   3   walk                          7                │
│                                                      │
│ samples for "walked"  (18× total)                    │
│   aerin/chapter-1:l3   …Anna «walked» away into…     │
│   aerin/chapter-2:l8   The boy «walked» a hundred…   │
│   khaal/chapter-1:l1   He «walked» as if the stones… │
│                                                      │
│ ↑↓ navigate · type to filter · Ctrl+S sort · Esc     │
└──────────────────────────────────────────────────────┘
```

## What the pipeline does for you

When you open the modal, inkhaven:

1. Walks every Paragraph node in the project's
   hierarchy.
2. Strips the leading typst heading line so the
   title doesn't inflate counts.
3. Tokenises with `unicode-segmentation`'s UAX-#29
   word segmenter — Cyrillic / Latin / Greek /
   Devanagari boundaries handled identically.
4. Drops single-character tokens, pure-digit runs,
   and stop-words.  (Stop-words reuse the
   `editor.style_warnings.repeated_phrases.<lang>_stop_words`
   list — same multilingual setup as the repeated-
   phrase detector, no second list to tune.)
5. Stems each surviving token with the project's
   Snowball algorithm.  `walk`/`walked`/`walking`
   all key on a single stem; Russian `сказал`/
   `сказала`/`сказали` collapse the same way.
   Disable via
   `editor.style_warnings.repeated_phrases.use_stemming
   = false`.
6. Groups by stem-key, counts, and captures the
   first three encountered occurrences as KWIC
   samples (32-char half-width, `«match»` wrapping
   for visual contrast in monospace output).
7. Sorts by count descending; ties broken by
   headword ascending.

## Interactions

Inside the modal:

| Chord                     | Action                                            |
|---------------------------|---------------------------------------------------|
| Any printable character   | Append to filter (substring match on headword + variants) |
| `Backspace` / `Delete`    | Edit the filter buffer                            |
| `Ctrl+S`                  | Toggle sort: count ↔ alphabetical                 |
| `↑` `↓` / `PgUp` / `PgDn` | Navigate                                           |
| `Home` / `End`            | Jump to first / last visible row                  |
| `Esc`                     | Close                                              |

Plain `s` types into the filter — it doesn't toggle
sort.  Use `Ctrl+S` so headwords starting with `s`
stay typeable.

## Filter semantics

The filter substring-matches against the headword
**and** every kept variant.  So typing `walk`
surfaces the entry whose headword is `walked` (the
most-common surface form for that stem) but whose
variants list includes `walking` and `walk`.

Type more characters to narrow further — `walke`
narrows the same entry down further; `walker` would
land on a different stem entirely.

## Memory + performance

- Max 3 samples + 5 variants per stem, so even a
  Tolstoy-scale corpus stays bounded.
- Build completes well under a second on a 100k-word
  manuscript; the work runs synchronously on the UI
  thread.
- Filter + sort changes are instant after the
  initial build — `visible` is a cached `Vec<usize>`
  into `data.entries`.

## Use cases

- **Auditing your prose's lexical fingerprint**.
  The top of the list is your *real* voice — not
  the voice you imagine.  Surprises are common.
- **Spotting overworked vocabulary**.  When `glanced`
  shows up 47 times across the book, it's time to
  swap in `peered` / `looked up` / `caught sight of`.
- **Locating a character or place mention**.  Type
  the name in the filter; KWIC samples show every
  place it appears with breadcrumb-style slug paths
  (`aerin/chapter-2:l8`) — faster than full-text
  search if you already know the word.

## See also

- [`03-the-editor.md`](03-the-editor.md) — inline
  filter-word + repeated-phrase overlays (related
  style-checking layer that's always-on while you
  edit).
- [`16-similar-paragraphs.md`](16-similar-paragraphs.md)
  — vector-similarity picker (semantic version of
  "find me where I wrote about X").

## 1.2.11 additions

- **Enter jumps to the source paragraph.**  Pressing
  Enter on a concordance row closes the modal and
  opens the source paragraph at the first sample's
  editor line.  The heading offset is computed
  against the live editor body so the cursor lands
  on the right textarea row (the index is built
  over heading-stripped bodies; the editor shows the
  raw paragraph with the `= title` line).
- **System books excluded from the corpus.**  System
  books (Prompts, Characters, Places, Lore, Help,
  Notes, Artefacts, Typst, Scripts) are skipped at
  index-build time.  Two reasons: their content is
  metadata, not prose — counting them dilutes the
  lexical signal; and their bodies aren't always
  reachable through the on-disk path (prompts-editor
  saves to bdslib only), so an Enter-jump to one
  would fail.  Filtering them out at index time
  eliminates the broken nav case.

## 1.2.12 additions

- **`inkhaven export-concordance` CLI.**  Same
  data the `Ctrl+B Shift+L` modal shows, written
  to disk for use in spreadsheets / analysis
  pipelines.  Two formats:

  ```
  inkhaven export-concordance --output stems.csv
  inkhaven export-concordance --format json --output stems.json
  ```

  CSV is one row per stem with
  `headword,stem,count,variants,sample_paths`
  columns.  JSON is the structured form including
  KWIC snippets, line numbers, full variants list,
  project-wide totals.  Optional `--min-count
  <N>` flag drops long-tail single-occurrence stems
  below the threshold from the export — useful for
  filtering noise on big manuscripts.  Same
  multilingual Snowball-stemmed plumbing as the
  modal; same system-book exclusion.
