removing not needed files
This commit is contained in:
lila
parent
597083e1fd
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88b16a1ed7
15 changed files with 80 additions and 555 deletions
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@ -1,16 +0,0 @@
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export const LANG_MAP: Record<string, string> = {
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english: "en",
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italian: "it",
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german: "de",
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french: "fr",
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spanish: "es",
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};
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export const POS_MAP: Record<string, string> = {
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nouns: "noun",
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verbs: "verb",
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adverbs: "adverb",
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adjectives: "adjective",
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};
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export const ALL_LANGUAGES = ["en", "de", "it", "es", "fr"];
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@ -1,43 +0,0 @@
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export function buildSystemPrompt(
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sourceLanguage: string,
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pos: string,
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targetLanguages: string[],
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): string {
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return `You are a multilingual dictionary engine. Output ONLY a JSON object. No markdown, no explanations.
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For each ${sourceLanguage} ${pos} provided, generate 1-2 distinct senses.
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CEFR difficulty mapping:
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- A1/A2 → easy
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- B1/B2 → medium
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- C1/C2 → hard
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Each sense must have:
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- sense: student-friendly definition, max 15 words
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- example: natural sentence using the word
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- difficulty_level: easy, medium, or hard
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- translations: object with keys ${targetLanguages.join(", ")}; each value is an array of {word, gender} where gender MUST be masculine, feminine, or neuter. Use null ONLY if the language has no grammatical gender for that word.
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Output format: JSON object where keys are the input words, values are arrays of sense objects.
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Example for ["house"]:
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{
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"house": [
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{
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"sense": "A building for human habitation.",
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"example": "They bought a house in the city.",
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"difficulty_level": "easy",
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"translations": {
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"de": [{"word": "Haus", "gender": "neuter"}],
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"it": [{"word": "casa", "gender": "feminine"}],
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"es": [{"word": "casa", "gender": "feminine"}],
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"fr": [{"word": "maison", "gender": "feminine"}]
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}
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}
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]
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}
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/no-think
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`;
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}
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@ -4,11 +4,6 @@
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"private": true,
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"type": "module",
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"scripts": {
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"db:reset": "tsx db/reset.ts",
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"extract": "tsx stage-1-extract/scripts/extract.ts",
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"reverse-link": "tsx stage-2-reverse-link/scripts/reverse-link.ts",
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"db:import": "tsx db/import.ts",
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"db:init": "tsx db/init.ts",
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"test": "vitest run",
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"test:watch": "vitest",
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"pipeline:run": "tsx --env-file .env pipeline.ts"
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0
data-pipeline/pipeline.ts
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data-pipeline/pipeline.ts
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data-pipeline/source-data/french/adjectives
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data-pipeline/source-data/french/adjectives
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grand
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petit
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bon
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mauvais
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beau
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nouveau
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vieux
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jeune
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heureux
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triste
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fort
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faible
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rapide
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lent
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chaud
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froid
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facile
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difficile
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propre
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sale
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20
data-pipeline/source-data/german/verbs
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data-pipeline/source-data/german/verbs
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sein
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haben
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werden
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können
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müssen
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sagen
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machen
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geben
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kommen
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gehen
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wissen
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sehen
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lassen
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stehen
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finden
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bleiben
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liegen
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heißen
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denken
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nehmen
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20
data-pipeline/source-data/italian/adverbs
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data-pipeline/source-data/italian/adverbs
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bene
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male
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sempre
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mai
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spesso
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raramente
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oggi
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domani
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ieri
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qui
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lì
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molto
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poco
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troppo
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abbastanza
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velocemente
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lentamente
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insieme
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forse
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davvero
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data-pipeline/source-data/spanish/nouns
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data-pipeline/source-data/spanish/nouns
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mesa
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silla
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coche
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perro
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gato
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ventana
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puerta
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calle
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plaza
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mercado
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parque
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río
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montaña
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playa
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sol
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luna
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estrella
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cielo
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tierra
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árbol
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import fs from "fs";
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import readline from "readline";
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/**
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* Creates a line-by-line reader stream for a given file path.
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*/
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export function createLineReader(sourcePath: string): readline.Interface {
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const fileStream = fs.createReadStream(sourcePath, "utf-8");
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return readline.createInterface({ input: fileStream, crlfDelay: Infinity });
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}
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import fs from "fs";
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import type { Wordlist } from "./scanning-source-files.js";
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/**
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* Takes a list of scanned datasets and creates their output folders if missing.
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*/
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export function ensureOutputFolders(wordlists: Wordlist[]): void {
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for (const wordlist of wordlists) {
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if (!fs.existsSync(wordlist.outputDir)) {
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fs.mkdirSync(wordlist.outputDir, { recursive: true });
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console.log(
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`📁 Created target folder: worddata/${wordlist.language}/${wordlist.pos}`,
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);
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}
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}
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console.log(
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"✅ All required output directories have been verified and created successfully.",
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);
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}
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import path from "path";
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export function getWordFilePath(word: string, outputDir: string): string {
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return path.join(outputDir, `${word}.json`);
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}
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import fs from "fs";
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import path from "path";
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// Define a simple shape for what a discovered dataset looks like
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export interface Wordlist {
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language: string;
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pos: string;
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sourcePath: string;
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outputDir: string;
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}
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/**
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* Scans the source-data directory to find all available word lists.
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*/
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export function scanSourceData(baseDir: string): Wordlist[] {
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const sourceBaseDir = path.join(baseDir, "source-data");
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const discoveredWordlists: Wordlist[] = [];
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// Safety check: if there's no source-data folder, return an empty array
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if (!fs.existsSync(sourceBaseDir)) {
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return discoveredWordlists;
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}
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// 1. Read the language directories (e.g., ['english'])
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const languages = fs.readdirSync(sourceBaseDir);
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for (const lang of languages) {
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const langFolderPath = path.join(sourceBaseDir, lang);
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// Make sure it's a directory, not a stray file
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if (!fs.statSync(langFolderPath).isDirectory()) continue;
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// 2. Read the files inside the language folder (e.g., ['nouns'])
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const posFiles = fs.readdirSync(langFolderPath);
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for (const pos of posFiles) {
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const fullSourcePath = path.join(langFolderPath, pos);
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// Make sure it's a file (like your extensionless "nouns" file)
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if (!fs.statSync(fullSourcePath).isFile()) continue;
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// 3. Package everything into a flat item and add it to our array
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discoveredWordlists.push({
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language: lang,
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pos: pos,
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sourcePath: fullSourcePath,
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outputDir: path.join(baseDir, "worddata", lang, pos),
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});
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}
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}
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// show summary
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console.log(
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`✅ Scan complete! Found ${discoveredWordlists.length} wordlist(s):`,
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);
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for (const list of discoveredWordlists) {
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console.log(` • ${list.language.toUpperCase()} (${list.pos})`);
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}
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return discoveredWordlists;
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}
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import fs from "fs";
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/**
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* Writes data as formatted JSON to a file path.
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* Safely catches and re-throws file system errors.
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*/
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export function writeJsonFile(filePath: string, data: unknown): void {
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const tempPath = `${filePath}.tmp`;
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fs.writeFileSync(tempPath, JSON.stringify(data, null, 2), "utf-8");
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fs.renameSync(tempPath, filePath);
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}
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# english nouns
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## step 1a
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get freqency source list + verify lemmatization + confirm language/POS tagging
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example:
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house
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bank
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asdf
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## step 1b
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transform list into JSON (headword, language, POS)
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example:
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```json
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{
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"headword": "house",
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"language": "en",
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"pos": "noun"
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},
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{
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"headword": "bank",
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"language": "en",
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"pos": "noun"
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},
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{
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"headword": "asdf",
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"language": "en",
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"pos": "noun"
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}
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```
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---
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## step 2a
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check existence in kaikki (eg. is there a english noun "asdf" in kaikki, if not put it separate list)
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example output:
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```json
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{
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"headword": "house",
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"language": "en",
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"pos": "noun"
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},
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{
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"headword": "bank",
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"language": "en",
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"pos": "noun"
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}
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```
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miss list (triage queue, not trash! contains junk, slipped inflections, and real Kaikki gaps, gets handled separately, never auto-drop!)
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```json
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{ "headword": "asdf", "language": "en", "pos": "noun" }
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```
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## step 2b
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for each sense, extract: glosses, translations (with gender), examples
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id will be generated via: `headword:lang:pos:sense_index`
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words without glosses will be dropped! (put in special list)
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example output:
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```json
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{
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"id": "house:en:noun:0",
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"headword": "house",
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"language": "en",
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"pos": "noun",
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"sense": ["A building for human habitation."],
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"examples": ["They bought a house in the city."],
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"translations": {
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"de": [{ "word": "Haus", "gender": "neuter" }],
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"it": [{ "word": "casa", "gender": "feminine" }],
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"es": [{ "word": "casa", "gender": "feminine" }],
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"fr": [{ "word": "maison", "gender": "feminine" }]
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}
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},
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{
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"id": "house:en:noun:1",
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"headword": "house",
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"language": "en",
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"pos": "noun",
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"sense": ["A noble family or lineage."],
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"examples": ["The House of Tudor ruled England."],
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"translations": {
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"de": [
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{ "word": "Adelsgeschlecht", "gender": "neuter" },
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{ "word": "Haus", "gender": "neuter" }
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]
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}
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},
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{
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"id": "bank:en:noun:0",
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"headword": "bank",
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"language": "en",
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"pos": "noun",
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"sense": ["An institution where one can place and borrow money."],
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"examples": ["She deposited her paycheck at the bank."],
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"translations": {
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"de": [{ "word": "Bank", "gender": "feminine" }],
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"it": [{ "word": "banca", "gender": "feminine" }],
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"es": [{ "word": "banco", "gender": "masculine" }],
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"fr": [{ "word": "banque", "gender": "feminine" }]
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}
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},
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{
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"id": "bank:en:noun:1",
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"headword": "bank",
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"language": "en",
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"pos": "noun",
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"sense": ["The land alongside a river or lake."],
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"examples": ["They picnicked on the bank of the river."],
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"translations": {
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"de": [{ "word": "Ufer", "gender": "neuter" }],
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"it": [{ "word": "riva", "gender": "feminine" }],
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"es": [{ "word": "orilla", "gender": "feminine" }],
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"fr": [{ "word": "rive", "gender": "feminine" }]
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}
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},
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{
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"id": "bank:en:noun:2",
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"headword": "bank",
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"language": "en",
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"pos": "noun",
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"sense": ["A collection or store of something held in reserve."],
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"examples": ["The hospital keeps a blood bank."],
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"translations": {
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"de": [{ "word": "Bank", "gender": "feminine" }]
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}
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}
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```
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## step 2c
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fill gaps Kaikki left (LLM, 3 models, generate-then-vote)
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takes 2b's partial cards. 3 different-family models (qwen, llama, gemma) generate, then vote.
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separate focused sub-passes, one field at a time — never combined:
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- **translations** → per missing language, generated with the gloss as context. generate → vote. no agreement → gap stays (cloud audit later)
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- **examples** → for senses with no Kaikki example. generate → verify-vote ("is this a valid example of the gloss?"), since freeform sentences never exact-match. no agreement → no example (card still valid) no tiebreak: no agreement leaves the gap, never escalates to more models. vote records stored in pipeline.db for the cloud audit.
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- **invariant**: the gloss is never LLM-generated — translations are filled, examples generated, difficulty graded, but the gloss must always be Kaikki's (no gloss → sense dropped in 2b)
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example input:
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```json
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{
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"id": "harbor:en:noun:0",
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"headword": "harbor",
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"language": "en",
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"pos": "noun",
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"glosses": ["A sheltered area of water where ships can dock safely."],
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"examples": [],
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"translations": { "de": [{ "word": "Hafen", "gender": "masculine" }] }
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}
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```
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next substage is the translation generation:
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```json
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{
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"id": "harbor:en:noun:0",
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"headword": "harbor",
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"language": "en",
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"pos": "noun",
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"glosses": ["A sheltered area of water where ships can dock safely."],
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"examples": [],
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"translations": {
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"de": [{ "word": "Hafen", "gender": "masculine" }],
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"it": [{ "word": "porto", "gender": null }],
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"es": [{ "word": "puerto", "gender": null }],
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"fr": [{ "word": "port", "gender": null }]
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}
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}
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```
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next substage is the gender modification (by looking up kaikki for it/es/fr or corresponding wiktionary, fill only nulls, not touching existing genders):
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**runs only after all genders are present**
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```json
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{
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"id": "harbor:en:noun:0",
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"headword": "harbor",
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"language": "en",
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"pos": "noun",
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"glosses": ["A sheltered area of water where ships can dock safely."],
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"examples": [],
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"translations": {
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"de": [{ "word": "Hafen", "gender": "masculine" }],
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"it": [{ "word": "porto", "gender": "masculine" }],
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"es": [{ "word": "puerto", "gender": "masculine" }],
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"fr": [{ "word": "port", "gender": "masculine" }]
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}
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}
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```
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next substage is the example generation:
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```json
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{
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"id": "harbor:en:noun:0",
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"headword": "harbor",
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"language": "en",
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"pos": "noun",
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"glosses": ["A sheltered area of water where ships can dock safely."],
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"examples": ["The fishing boats returned to the harbor at dusk."],
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"translations": {
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"de": [{ "word": "Hafen", "gender": "masculine" }],
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"it": [{ "word": "porto", "gender": "masculine" }],
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"es": [{ "word": "puerto", "gender": "masculine" }],
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"fr": [{ "word": "port", "gender": "masculine" }]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## step 3
|
||||
|
||||
adding difficulty level
|
||||
|
||||
CEFR is mapped to three buckets:
|
||||
A1/A2 → easy
|
||||
B1/B2 → intermediate
|
||||
C1/C2 → hard
|
||||
|
||||
depending on number of senses per headword:
|
||||
|
||||
- One sense → derive difficulty from the CEFRLex distribution (first CEFR level crossing a frequency threshold, mapped to a bucket, not the peak), for the four covered languages (en/de/es/fr). Deterministic, no LLM
|
||||
- Multiple senses → the three local LLMs grade each sense (easy/intermediate/hard) from the gloss. CEFRLex not involved.
|
||||
- No CEFRLex entry at all (Italian, or single-sense word that's missing) → LLMs grade from gloss
|
||||
|
||||
to get coverage (not quality), 3 local llms are going to be used: gemma, qwen and llama
|
||||
|
||||
on the llm votes:
|
||||
|
||||
- **Majority agrees (3-0 or 2-1)** → ship the majority value.
|
||||
- **3-way split (all three differ)** → no consensus → exclude the card to the review queue. Not shipped.
|
||||
|
||||
example output:
|
||||
|
||||
```json
|
||||
{
|
||||
"id": "house:en:noun:0",
|
||||
"headword": "house",
|
||||
"language": "en",
|
||||
"pos": "noun",
|
||||
"difficulty_level": "easy",
|
||||
"glosses": ["A building for human habitation."],
|
||||
"examples": ["They bought a house in the city."],
|
||||
"translations": {
|
||||
"de": [{ "word": "Haus", "gender": "neuter" }],
|
||||
"it": [{ "word": "casa", "gender": "feminine" }],
|
||||
"es": [{ "word": "casa", "gender": "feminine" }],
|
||||
"fr": [{ "word": "maison", "gender": "feminine" }]
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "house:en:noun:1",
|
||||
"headword": "house",
|
||||
"language": "en",
|
||||
"pos": "noun",
|
||||
"difficulty_level": "hard",
|
||||
"glosses": ["A noble family or lineage."],
|
||||
"examples": ["The House of Tudor ruled England."],
|
||||
"translations": {
|
||||
"de": [
|
||||
{ "word": "Adelsgeschlecht", "gender": "neuter" },
|
||||
{ "word": "Haus", "gender": "neuter" }
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**important**: `difficulty_level` is mandatory on every shipped card (the user sets difficulty before a game)
|
||||
|
||||
This is coverage, not final quality: three local models make every shipped card's difficulty present and plausible, but not guaranteed correct
|
||||
|
||||
no cefr list: needs to be graded by online llms (see notes)
|
||||
|
||||
---
|
||||
|
||||
## coverage report (trial deliverable)
|
||||
|
||||
- not a data stage => reads the finished cards and summarises them
|
||||
- the trial (english + italian nouns, local models) runs steps 1a→3 then emits a report
|
||||
|
||||
### pipeline health — did it run?
|
||||
|
||||
- input lemmas → output cards (ratio; multi-sense makes cards > lemmas)
|
||||
- dropped per stage + why: 2a misses (junk/inflection/gap), 2b no-gloss drops, 3 no-consensus exclusions
|
||||
- parse failures / errors
|
||||
|
||||
### coverage — how complete?
|
||||
|
||||
- translation completeness: all 4 langs vs gaps, per language
|
||||
- examples: from Kaikki vs LLM-generated vs none
|
||||
- gender: translations still null after Wiktionary fill
|
||||
- difficulty distribution per bucket (lopsided = broken CEFRLex threshold or bad grading)
|
||||
|
||||
### local good enough? — model agreement
|
||||
|
||||
- difficulty votes: 3-0 / 2-1 / 3-way split rates (high 3-way = locals can't do it → need cloud)
|
||||
- translation gap-fill: agreement rate, gaps left unfilled
|
||||
- → go/no-go on local-for-launch
|
||||
|
||||
### rent vs API? — workload volume
|
||||
|
||||
- total LLM calls across 2c + 3 (× per-token rate = API cost)
|
||||
- cards needing LLM vs handled deterministically (english: high deterministic; italian: ~0, no CEFRLex)
|
||||
- per-language call counts → scale english (low) and italian (high) to estimate the middle three
|
||||
|
||||
### review backlog — what's deferred
|
||||
|
||||
- miss-list size + composition, per language
|
||||
- no-consensus exclusions (cloud-audit queue size)
|
||||
- unfilled translation gaps (also cloud-audit work)
|
||||
|
||||
**framing**: english = optimistic floor (rich Kaikki, CEFRLex exists, models strongest).
|
||||
italian = pessimistic ceiling (no CEFRLex, thinner Kaikki). all five languages sit between.
|
||||
read to decide, not to archive.
|
||||
|
||||
---
|
||||
|
||||
Notes:
|
||||
|
||||
- the miss list needs to get verified/re-worked later on
|
||||
- the kaikki data contains ipas and links to audio files, add them later if needed, they are not needed now
|
||||
- use online llms to set the difficulty(cefr) of the not shipped words
|
||||
- add plurals from kaikki/wiktionary
|
||||
- postgres sync to prod db
|
||||
|
|
@ -1,41 +0,0 @@
|
|||
# trial run — implementation roadmap (pure vertical)
|
||||
|
||||
goal: english + italian nouns through the full pipeline on local models, emit
|
||||
coverage report. build a thin end-to-end slice first, then widen each pass.
|
||||
|
||||
## phase 0 — foundation
|
||||
|
||||
- confirm @lila/shared types (lang codes, POS) exist and match
|
||||
- package.json deps for what phase 1 needs
|
||||
- one hardcoded test word to carry through the slice (e.g. "house")
|
||||
|
||||
## phase 1 — thin vertical slice (ONE word, 1a→3, crudest possible)
|
||||
|
||||
goal: prove a single card can travel the whole pipeline and come out the end.
|
||||
allowed to be ugly — hardcode, skip voting, one model, fake CEFRLex.
|
||||
|
||||
- 1a: one lemma record, by hand or trivial read
|
||||
- 2a: look it up in Kaikki, confirm it exists
|
||||
- 2b: extract its senses → card(s) with gloss
|
||||
- 2c: fill one missing translation with ONE local model, no voting
|
||||
- 3: assign a difficulty crudely (even hardcoded "easy")
|
||||
- OUT: one finished card. the pipeline has a shape.
|
||||
|
||||
## phase 2 — widen: real deterministic front (1a–2b, all words)
|
||||
|
||||
- 1a: real frequency list, verified
|
||||
- 2a: real existence gate + miss-list triage
|
||||
- 2b: real sense extraction, gender from tags, gloss-required drop
|
||||
- still JSON output. inspect cards by hand.
|
||||
|
||||
## phase 3 — widen: real LLM back (2c + 3)
|
||||
|
||||
- storage seam: JSON → SQLite, schema, resumability
|
||||
- 2c: real generate→vote, three families, all sub-passes
|
||||
- 3: real CEFRLex single-sense + 3-model vote multi-sense + exclude-on-split
|
||||
|
||||
## phase 4 — coverage report + runs
|
||||
|
||||
- emit report
|
||||
- run english, then italian
|
||||
- decide: local good enough? rent vs API?
|
||||
Loading…
Add table
Add a link
Reference in a new issue