62 lines
3.3 KiB
Markdown
62 lines
3.3 KiB
Markdown
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# halkyon_learn_curator
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Turns dictionary downloads into the dictionary files a Halkyon Learn server
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loads. It reads each source with an adapter of its own and writes one format
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for all of them, so a new source or language needs a new adapter, never a
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change to the server or the app. It doesn't depend on Serverpod or the server.
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## Making a dictionary file
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JMdict in English, with the JLPT levels on top:
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1. Download `jmdict-eng-<version>.json.tgz` from
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[jmdict-simplified's releases](https://github.com/scriptin/jmdict-simplified/releases)
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and unpack it.
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2. Download `original_data/n1.csv` … `n5.csv` from
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[yomitan-jlpt-vocab](https://github.com/stephenmk/yomitan-jlpt-vocab) into a
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folder.
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3. Convert, in this package:
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```sh
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dart run halkyon_learn_curator:curate jmdict \
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--input jmdict-eng-3.6.2.json --jlpt jlpt/ --output jmdict-en.jsonl.gz
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```
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The whole of JMdict (about 219,000 entries) takes some 20 seconds and
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300 MB of memory: entries are read one at a time.
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Put the file into the server's `dictionaries` folder. The server loads it on
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its next start, and again whenever the file changes (see the server's README).
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## The format (`halkyon-dictionary`, version 1)
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[JSON Lines](https://jsonlines.org/), gzipped when the name ends in `.gz`.
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The **first line** says what the file holds:
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| Field | |
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|---|---|
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| `format`, `formatVersion` | `"halkyon-dictionary"`, `1` |
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| `source`, `sourceVersion` | The source's name (`jmdict`) and version (`3.6.2 (2026-10-05)`). An instance holds one version of a source for each meaning language. |
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| `language`, `glossLanguage` | BCP 47 tags: the words' language (`ja`) and the meanings' (`en`). |
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| `license` | The data's licence, an SPDX identifier (`CC-BY-SA-4.0`). |
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| `credits` | Everything it is drawn from: `name`, `by`, `license`, `url`. The app lists them where it credits its data sources. |
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**Every further line** is an entry:
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| Field | |
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|---|---|
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| `id` | Its id in the source; with the source and meaning language, it names the entry for good. |
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| `headword`, `reading` | How the word is shown, and how it is read; no `reading` when the headword is its own (a word written in kana). |
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| `labels` | What it can be picked by: `common`, `jlpt:n5` … `jlpt:n1` (later e.g. `hsk:3`, `topik:1`). |
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| `rank` | Its order among the source's entries, lower first; JMdict: N5 to N1 in their lists' order, then other common words, then the rest. |
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| `keys` | What a search matches, lower case: written forms, readings in their own script and romanised, meanings. Everything specific to a language happens here, so the server's search knows no language. |
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| `entry` | The entry itself, as cards take it: `forms` and `readings` (`text`, `common`, the source's `tags`; for a reading the `forms` it belongs to), `senses` (`glosses`; the source's codes in `pos`, `misc`, `field`, `dialect`; `info`; the `forms` and `readings` a sense is limited to). Lists are left out when empty. |
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## Adding a source
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Write an adapter in `lib/src/sources/` that turns the source into the lines
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above, with its credits and a sample of real data in `test/fixtures/` (and
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where that comes from in `test/fixtures/README.md`); give it a command in
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`bin/curate.dart`. Check the source's licence first, and that the instance may
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pass its data on to the people using it.
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