# Vocabulary Trainer — Roadmap: Data Pipeline & Schema Migration

> **Objective:** Replace the OpenWordNet/kaikki data source with a
> Gemini-powered pipeline that generates high-quality vocabulary data,
> backed by a normalized Postgres schema.
>
> **Author:** [Your Name]
> **Date:** July 2026
> **Companion doc:** `docs/schema-design.md`

---

## How to Read This Document

This roadmap has three zoom levels:

- **Part 1 — Overview:** The phases at a glance. Read this to
  understand the full scope in 30 seconds.
- **Part 2 — Phase Breakdown:** Goals, tasks, dependencies, and
  acceptance criteria per phase. Read this to plan your week.
- **Part 3 — Detailed Tasks:** Step-by-step instructions within each
  phase. Read this when you sit down to code.

---

# Part 1 — High-Level Overview

```
Phase 1  Schema ✅
         New Drizzle schema (words, senses, translations) with
         relations, constraints, and indexes. Committed.

Phase 2  Preparation
         Get wordlists, set up tooling, finalize the Gemini prompt.

Phase 3  Data Pipeline
         Build the Gemini → validate → SQLite pipeline.
         Produce a clean dataset of ~1000 nouns × 5 languages.

Phase 4  Migration & Import
         Generate and apply the Drizzle migration.
         Write the SQLite → Postgres import script.

Phase 5  App Integration
         Rewrite the game and distractor queries against the new schema.
         Update the exercise-generation logic.
         Test the full game flow in dev.

Phase 6  Production Deploy
         Run the Drizzle migration on prod.
         Import the dataset.
         Verify the live app works end-to-end.

Phase 7  Extend POS
         Run the pipeline for verbs, adjectives, adverbs.
         No schema changes needed — new wordlists + adjusted prompts.

Phase 8  Future Features (out of scope for now)
         Inflection tables, conjugation/declension exercises,
         gender exercises, spaced-repetition scheduling.
```

**Dependency chain:**

```
Phase 1 ✅ → Phase 2 → Phase 3 → Phase 4 → Phase 5 → Phase 6 → Phase 7
                                                                    │
                                                                    ▼
                                                                Phase 8
```

---

# Part 2 — Phase Breakdown

---

## Phase 1: Schema ✅ COMPLETE

**Goal:** New normalized schema exists in Drizzle with all tables,
constraints, indexes, and relations.

**Completed tasks:**

- [x] `words` table: headword, language_code, pos, UNIQUE, CHECKs, index
- [x] `senses` table: word_id FK (cascade), sense_index, difficulty,
      definitions TEXT[], examples TEXT[], UNIQUE, CHECK, index
- [x] `translations` table: sense_id FK (cascade), target_language_code,
      translation, gender (nullable), difficulty, UNIQUE, CHECKs, index
- [x] Relations: words→senses (many), senses→word (one) + translations
      (many), translations→sense (one)
- [x] `NOUN_GENDERS` constant added to `@lila/shared`
- [x] `DIFFICULTY_LEVELS` updated: "intermediate" → "medium"
- [x] Old tables (`vocabulary_entries`, `entry_translations`) untouched
- [x] Auth and lobby tables untouched
- [x] Build passes, committed

---

## Phase 2: Preparation

**Goal:** Have everything you need before writing pipeline code.

**Tasks:**

- [x] Acquire frequency-based noun lists for all 5 languages
- [x] Clean and format the lists (one word per line, UTF-8)
- [x] Set up local Postgres (Docker or native)
- [ ] Set up a SQLite database file for staging
- [ ] Write and test the Gemini prompt with 5 sample words
- [ ] Refine the prompt until the JSON output matches the contract
      defined in `docs/schema-design.md` §6.3

**Dependencies:** Phase 1 complete.

**Acceptance criteria:**

- You have 5 wordlist files in `data-pipeline/source-data/` (one per
  language).
- A Gemini call with 5 German nouns returns valid JSON matching the
  contract, including definitions, examples, translations with gender,
  and difficulty levels.
- Local Postgres is running and reachable from your app.

---

## Phase 3: Data Pipeline

**Goal:** A repeatable script that takes a wordlist, calls Gemini in
batches of 20, validates the output, and writes clean rows to SQLite.

**Tasks:**

- [ ] Write the validation module (see schema-design §6.4)
- [ ] Write the pipeline script: - Read wordlist file - Split into batches of 20 - Call Gemini API per batch - Parse JSON response - Validate each entry - Write valid entries to SQLite - Log invalid entries to a rejection file
- [ ] Create the SQLite schema (mirrors the Postgres schema)
- [ ] Run the pipeline for all 5 languages (nouns only)
- [ ] Review the rejection log, fix prompt issues, re-run failed batches
- [ ] Spot-check 50 random entries for correctness

**Dependencies:** Phase 2 complete.

**Acceptance criteria:**

- SQLite database contains ~1000 nouns × 5 languages with senses and
  translations.
- Rejection rate is below 10%.
- Spot-checked entries have correct definitions, plausible examples,
  correct genders, and reasonable difficulty levels.
- The pipeline is re-runnable (idempotent or with duplicate handling).

---

## Phase 4: Migration & Import

**Goal:** The new schema exists in Postgres and the SQLite data is
imported.

**Tasks:**

- [ ] Generate the Drizzle migration (`npx drizzle-kit generate`)
- [ ] Inspect the generated SQL file — verify it creates the right
      tables, constraints, and indexes
- [ ] Apply the migration to local Postgres (`npx drizzle-kit migrate`)
- [ ] Write the import script (SQLite → Postgres): - Read all rows from SQLite - Insert into Postgres in dependency order:
      words → senses → translations - Use batch inserts (not row-by-row) - Wrap in transactions (per batch of 20 words) - Handle duplicates gracefully (skip or upsert)
- [ ] Run the import script
- [ ] Verify row counts match between SQLite and Postgres
- [ ] Run 3–5 manual SQL queries against Postgres to sanity-check
      the data

**Dependencies:** Phase 3 complete (SQLite has data).

**Acceptance criteria:**

- `npx drizzle-kit migrate` runs without errors on local Postgres.
- Import script completes without errors.
- Row counts in Postgres match SQLite (±rejection count).
- Manual query: "Give me 5 random German nouns with Spanish
  translations at easy difficulty" returns sensible results.

---

## Phase 5: App Integration

**Goal:** The running app uses the new schema. Game rounds and
distractors work correctly for all language pairs.

**Tasks:**

- [ ] Rewrite `getGameTerms` query: - JOIN words → senses → translations - Filter: source language, pos, sense difficulty (ceiling),
      target language, translation difficulty (exact) - ORDER BY RANDOM(), LIMIT rounds
- [ ] Rewrite `getDistractors` query: - Same JOINs and filters - Exclude: `sense_id != current`, `translation != correct` - ORDER BY RANDOM(), LIMIT 3
- [ ] Update the exercise-generation logic: - Pick one random definition from the `definitions` array - Pick one random example from the `examples` array - Assemble the 4 answer options (1 correct + 3 distractors) - Shuffle the options
- [ ] Remove or deprecate old schema references
      (old `vocabulary_entries`, `entry_translations` tables)
- [ ] Test manually: - German → Spanish, nouns, easy, 10 rounds - Spanish → German, nouns, medium, 10 rounds - English → French, nouns, hard, 10 rounds - Italian → German, nouns, easy, 10 rounds - Verify: no duplicate answers, no same-sense synonyms as
      distractors, definitions and examples are in the source
      language, genders display correctly
- [ ] Test edge cases: - A difficulty/pos/language combo with very few words
      (does the app handle < 4 available words gracefully?) - A word with multiple senses (does the correct sense appear?)

**Dependencies:** Phase 4 complete (Postgres has data, schema exists).

**Acceptance criteria:**

- Full game flow works in dev for at least 4 different language pairs.
- No same-sense synonyms appear as distractors.
- Definitions and examples are in the correct language.
- Gender is displayed where applicable.
- No console errors or unhandled query failures.

---

## Phase 6: Production Deploy

**Goal:** The live app runs on the new schema with the new data.

**Tasks:**

- [ ] Back up the production database
- [ ] Run the Drizzle migration on prod
- [ ] Run the import script against prod Postgres
- [ ] Verify row counts on prod
- [ ] Test the live app: - Play 2 full games on the deployed app - Check different language pairs and difficulties
- [ ] Monitor for errors (server logs, browser console) for 24h
- [ ] Remove old tables from prod (after confirming everything works)

**Dependencies:** Phase 5 complete (dev is fully working).

**Acceptance criteria:**

- Live app serves game rounds from the new schema.
- No errors in server logs for 24 hours.
- Old tables are dropped (or scheduled for removal).
- Rollback plan exists: the prod backup can be restored if needed.

---

## Phase 7: Extend POS

**Goal:** Verbs, adjectives, and adverbs are in the database and
usable in the app.

**Tasks:**

- [ ] Acquire frequency lists for verbs, adjectives, adverbs
      (all 5 languages)
- [ ] Adjust the Gemini prompt per POS: - Verbs: may need different metadata (transitivity, etc.) - Adjectives: may need base form info - Adverbs: typically simpler metadata
- [ ] Run the pipeline for each POS
- [ ] Validate, import to Postgres (dev → prod)
- [ ] Test game flow with verbs, adjectives, adverbs
- [ ] Verify the POS filter in the app UI works for all types

**Dependencies:** Phase 6 complete.

**Acceptance criteria:**

- All 4 POS types are playable in the app.
- Pipeline is repeatable for future data additions.

---

## Phase 8: Future Features (out of scope)

Listed here for visibility. Not planned, not estimated.

- [ ] `inflection_forms` table + conjugation exercises (verbs)
- [ ] Adjective declension exercises (der grüne Mann, grüner Mann…)
- [ ] Gender exercises (pick the correct article)
- [ ] Spaced-repetition scheduling (track which words the user knows)
- [ ] User accounts and progress persistence
- [ ] Additional languages (if ever)

---

# Part 3 — Detailed Task Breakdown

---

## Phase 2: Preparation — Detailed

### 2.1 Acquire wordlists

- Search for frequency lists. Good starting points:
  - "Leipzig Corpora Collection" (academic, per-language)
  - Wiktionary frequency lists
  - GitHub repos: search "german noun frequency list",
    "spanish noun frequency list", etc.
  - Tatoeba sentence counts as a proxy for word frequency
- Target: ~1000 nouns per language, sorted by frequency.
- Format: plain text, one word per line, UTF-8, no headers.
- Save to `data-pipeline/source-data/{language}/nouns/`.
- Clean the lists: remove duplicates, remove words with spaces
  (multi-word expressions), remove proper nouns if desired.

### 2.2 Set up local Postgres

- Option A (Docker):
  ```
  docker run --name vocab-dev \
    -e POSTGRES_USER=dev \
    -e POSTGRES_PASSWORD=dev \
    -e POSTGRES_DB=vocab \
    -p 5432:5432 \
    -d postgres:16
  ```
- Option B (native install): install Postgres, create a `vocab`
  database.
- Update your `.env` / `.env.local` with the connection string.
- Verify: connect with `psql` or a GUI client (TablePlus, DBeaver,
  pgAdmin). Run `SELECT 1;`.

### 2.3 Write and test the Gemini prompt

- Start with 5 German nouns.
- The prompt should specify:
  - The exact JSON structure expected (copy from schema-design §6.3)
  - That definitions and examples must be in the word's language
  - That translations are needed for all 4 other supported languages
  - That gender must be provided for de/fr/es/it, null for en
  - That difficulty must be one of: easy, medium, hard
  - That multiple senses should be included for polysemous words
- Call the API. Inspect the JSON. Common issues to fix:
  - Gemini wraps the JSON in markdown code fences → strip them
  - Gemini returns "intermediate" instead of "medium" → normalize
  - Gemini omits a language → re-prompt or reject
  - Gemini returns gender "common" for German → reject
- Iterate until 3 consecutive batches of 20 return clean JSON.

---

## Phase 3: Data Pipeline — Detailed

### 3.1 Create the SQLite schema

- Create a file `data-pipeline/schema.sql` or define it in your
  pipeline script.
- Tables mirror the Postgres schema:

  ```sql
  CREATE TABLE words (
    id TEXT PRIMARY KEY,
    headword TEXT NOT NULL,
    language_code TEXT NOT NULL,
    pos TEXT NOT NULL,
    created_at TEXT DEFAULT (datetime('now')),
    UNIQUE(headword, language_code, pos)
  );

  CREATE TABLE senses (
    id TEXT PRIMARY KEY,
    word_id TEXT NOT NULL REFERENCES words(id),
    sense_index INTEGER NOT NULL DEFAULT 0,
    difficulty TEXT NOT NULL,
    definitions TEXT NOT NULL DEFAULT '[]',  -- JSON array as text
    examples TEXT NOT NULL DEFAULT '[]',     -- JSON array as text
    created_at TEXT DEFAULT (datetime('now')),
    UNIQUE(word_id, sense_index)
  );

  CREATE TABLE translations (
    id TEXT PRIMARY KEY,
    sense_id TEXT NOT NULL REFERENCES senses(id),
    target_language_code TEXT NOT NULL,
    translation TEXT NOT NULL,
    gender TEXT,
    difficulty TEXT NOT NULL,
    created_at TEXT DEFAULT (datetime('now')),
    UNIQUE(sense_id, target_language_code, translation)
  );
  ```

- Note: SQLite has no native TEXT[]. Store arrays as JSON text.
  The import script will parse them into Postgres TEXT[] on import.

### 3.2 Write the validation module

- Create `data-pipeline/validate.ts`.
- Input: one parsed Gemini entry (the JSON object for one word).
- Checks (return a list of errors, empty = valid):
  - `headword` is a non-empty string
  - `language` is in ['en','de','it','fr','es']
  - `pos` is in ['noun','verb','adjective','adverb']
  - `senses` is a non-empty array
  - Each sense has:
    - `sense_index` is a non-negative integer
    - `difficulty` is in ['easy','medium','hard']
    - `definitions` is a non-empty array of non-empty strings
    - `examples` is a non-empty array of non-empty strings
    - `translations` is a non-empty array
    - Each translation has:
      - `target_language` is in the supported list
      - `target_language` != the word's own language
      - `word` is a non-empty string
      - `gender` is valid for the target language
        (de: masculine/feminine/neuter/null,
        fr/es/it: masculine/feminine/null,
        en: null)
      - `difficulty` is in ['easy','medium','hard']
- Output: `{ valid: boolean, errors: string[] }`

### 3.3 Write the pipeline script

- Create `data-pipeline/run.ts`.
- Pseudocode:
  ```
  for each language in [de, en, es, fr, it]:
    read wordlist file → array of words
    split into batches of 20
    for each batch:
      call Gemini API with the batch
      parse JSON response (strip markdown fences if present)
      for each entry in response:
        validate(entry)
        if valid:
          generate UUIDs for word, senses, translations
          INSERT into SQLite (words, senses, translations)
        else:
          append to rejection log
      log progress: "Batch 12/50 done. 238 words imported, 2 rejected."
      sleep 1s (rate limiting)
  ```
- Use `better-sqlite3` for SQLite access (synchronous, simple).
- Generate UUIDs with `crypto.randomUUID()`.
- Store definitions/examples as JSON strings in SQLite
  (`JSON.stringify(arr)`).

### 3.4 Run and review

- Run the pipeline for all 5 languages.
- Check the rejection log. If rejection rate > 10%, fix the prompt
  and re-run failed batches.
- Spot-check: open the SQLite DB, run:
  ```sql
  SELECT w.headword, s.definitions, t.translation, t.gender
  FROM words w
  JOIN senses s ON s.word_id = w.id
  JOIN translations t ON t.sense_id = s.id
  WHERE w.language_code = 'de' AND w.pos = 'noun'
  ORDER BY RANDOM()
  LIMIT 20;
  ```
- Read the definitions. Are they in German? Do they make sense?
  Are the genders correct? Are the difficulties reasonable?

---

## Phase 4: Migration & Import — Detailed

### 4.1 Generate and inspect the migration

- Run: `npx drizzle-kit generate`
- Open the generated SQL file in `packages/db/drizzle/`.
- Read it. Verify:
  - Three CREATE TABLE statements (words, senses, translations)
  - CHECK constraints match your schema
  - UNIQUE constraints are present
  - Three CREATE INDEX statements
  - Foreign keys reference the correct tables with ON DELETE CASCADE
- If something looks wrong, fix the schema file and regenerate.

### 4.2 Apply the migration locally

- Run: `npx drizzle-kit migrate`
- Connect to local Postgres and verify:
  ```sql
  \dt                          -- list tables
  \d words                     -- describe words table
  \d senses                    -- describe senses table
  \d translations              -- describe translations table
  ```

### 4.3 Write the import script

- Create `data-pipeline/import-to-postgres.ts`.
- Pseudocode:

  ```
  open SQLite database (read-only)
  connect to Postgres via Drizzle

  read all words from SQLite
  for each batch of 100 words:
    begin transaction
    INSERT words into Postgres
    for each word:
      read its senses from SQLite
      INSERT senses into Postgres
      for each sense:
        read its translations from SQLite
        parse definitions/examples from JSON string → TEXT[]
        INSERT translations into Postgres
    commit transaction
    log progress

  log final counts: words, senses, translations
  ```

- Parse `definitions` and `examples` from JSON strings (SQLite)
  into actual arrays (Postgres TEXT[]).
- Use `ON CONFLICT DO NOTHING` to handle re-runs gracefully.

### 4.4 Run and verify

- Run the import script.
- Compare counts:

  ```sql
  -- In SQLite
  SELECT COUNT(*) FROM words;
  SELECT COUNT(*) FROM senses;
  SELECT COUNT(*) FROM translations;

  -- In Postgres
  SELECT COUNT(*) FROM words;
  SELECT COUNT(*) FROM senses;
  SELECT COUNT(*) FROM translations;
  ```

- Counts should match (minus any rows that failed validation).
- Run the game query manually in Postgres:
  ```sql
  SELECT w.headword, s.definitions, s.examples,
         t.translation, t.gender
  FROM words w
  JOIN senses s ON s.word_id = w.id
  JOIN translations t ON t.sense_id = s.id
  WHERE w.language_code = 'de'
    AND w.pos = 'noun'
    AND s.difficulty IN ('easy', 'medium')
    AND t.target_language_code = 'es'
    AND t.difficulty = 'medium'
  ORDER BY RANDOM()
  LIMIT 5;
  ```
- Verify the results make sense.

---

## Phase 5: App Integration — Detailed

### 5.1 Rewrite getGameTerms

- Open `packages/db/src/models/termModel.ts`.
- Replace the old query (2-table join on `vocabulary_entries` +
  `entry_translations`) with the new 3-table join
  (words → senses → translations).
- Parameters: sourceLanguage, targetLanguage, pos, difficulty, rounds.
- Difficulty filter:
  - `senses.difficulty IN (all levels up to and including selected)`
    — ceiling logic
  - `translations.difficulty = selected` — exact match
- Return: word_id, headword, sense_id, definitions, examples,
  translation, gender.

### 5.2 Rewrite getDistractors

- Same 3-table join.
- Additional filters:
  - `t.sense_id != :currentSenseId`
  - `t.translation != :correctAnswer`
- LIMIT 3.
- If fewer than 3 distractors are found (small data pool), handle
  gracefully: reduce the number of options or log a warning.

### 5.3 Update exercise generation

- In the function that assembles a game round:
  - Pick one random definition:
    `definitions[Math.floor(Math.random() * definitions.length)]`
  - Pick one random example:
    `examples[Math.floor(Math.random() * examples.length)]`
  - Combine 1 correct translation + 3 distractors.
  - Shuffle the 4 options (Fisher-Yates or similar).
  - Attach gender to each option for display.

### 5.4 Test matrix

Run through this matrix manually in the dev app:

| Source | Target | POS  | Difficulty | Rounds | Pass? |
| ------ | ------ | ---- | ---------- | ------ | ----- |
| de     | es     | noun | easy       | 10     |       |
| es     | de     | noun | medium     | 10     |       |
| en     | fr     | noun | hard       | 10     |       |
| it     | de     | noun | easy       | 10     |       |
| fr     | en     | noun | medium     | 10     |       |

For each: verify definitions are in the source language, translations
are in the target language, genders are shown, no same-sense synonyms
appear as distractors, no duplicate options.

---

## Phase 6: Production Deploy — Detailed

### 6.1 Pre-deploy checklist

- [ ] All Phase 5 acceptance criteria pass
- [ ] `git status` is clean, all changes committed
- [ ] The Drizzle migration file is committed to the repo
- [ ] You know your prod database connection string
- [ ] You have a backup method for prod (pg_dump, hosting provider
      snapshot, etc.)

### 6.2 Deploy

- Back up prod:
  ```
  pg_dump -h <host> -U <user> -d <db> > backup_$(date +%Y%m%d).sql
  ```
- Run migration on prod:
  ```
  DATABASE_URL=<prod-url> npx drizzle-kit migrate
  ```
- Run import script against prod:
  ```
  DATABASE_URL=<prod-url> npx tsx data-pipeline/import-to-postgres.ts
  ```
- Verify counts on prod.

### 6.3 Post-deploy verification

- Open the live app. Play 2 full games with different settings.
- Check server logs for errors.
- Wait 24h. Check logs again.
- If everything is clean, drop old tables:
  ```sql
  DROP TABLE IF EXISTS entry_translations;
  DROP TABLE IF EXISTS vocabulary_entries;
  ```
  (Or keep them for another week if you want a safety net.)

### 6.4 Rollback plan

If something goes wrong:

- Restore the backup:
  ```
  psql -h <host> -U <user> -d <db> < backup_YYYYMMDD.sql
  ```
- Revert the code to the previous commit.
- Redeploy.

---

## Phase 7: Extend POS — Detailed

### 7.1 Per POS

Repeat the Phase 3 pipeline for each new POS:

- Acquire wordlists (verbs, adjectives, adverbs) for all 5 languages.
- Adjust the Gemini prompt:
  - Verbs: ask for transitivity, common prepositions, or other
    verb-specific metadata if needed for future exercises.
  - Adjectives: ask for the base form. Note that adjective metadata
    differs from noun metadata (no gender on the adjective itself
    in the same way — gender applies to the noun it modifies).
  - Adverbs: typically simpler. May not need gender at all.
- Run pipeline → validate → SQLite → import → Postgres.
- Test in the app with the POS filter set to the new type.

### 7.2 No schema changes

The `pos` column already supports all four types. The `gender`
column is nullable and simply won't be populated for adverbs or
English words. No migration needed.

---

# Risks & Mitigations

| Risk                                                   | Likelihood        | Impact                    | Mitigation                                                                                        |
| ------------------------------------------------------ | ----------------- | ------------------------- | ------------------------------------------------------------------------------------------------- |
| Gemini returns malformed JSON                          | Medium            | Pipeline stalls           | Strip markdown fences, wrap parsing in try/catch, log and skip bad batches                        |
| Gemini produces wrong genders or difficulties          | Medium            | Bad exercise data         | Validation module rejects invalid entries. Spot-check 50+ entries per language                    |
| Too few words at a given difficulty/pos/language combo | Medium            | Game can't fill 4 options | Graceful fallback: reduce options or show a "not enough words" message. Log which combos are thin |
| SQLite → Postgres import fails mid-way                 | Low               | Partial data in Postgres  | Transactions per batch. Re-run with ON CONFLICT DO NOTHING                                        |
| ORDER BY RANDOM() gets slow at scale                   | Low (not at 500k) | Slow game load            | Add a TODO comment. Optimize with random() pre-filter when needed                                 |
| Prod migration breaks the live app                     | Low               | Downtime                  | Backup before migrating. Rollback plan documented. Deploy during low-traffic hours                |

---

# File / Folder Structure (new files)

```
project/
├── data-pipeline/
│   ├── source-data/
│   │   ├── german/nouns/         ← wordlist files
│   │   ├── english/nouns/
│   │   ├── spanish/nouns/
│   │   ├── french/nouns/
│   │   └── italian/nouns/
│   ├── rejections/               ← invalid Gemini entries (for review)
│   ├── staging.db                ← SQLite staging database
│   ├── schema.sql                ← SQLite schema definition
│   ├── validate.ts               ← validation module
│   ├── run.ts                    ← main pipeline script
│   └── import-to-postgres.ts     ← SQLite → Postgres import
├── packages/
│   ├── db/
│   │   └── src/db/schema.ts      ← Drizzle schema (updated ✅)
│   └── shared/
│       └── src/constants.ts      ← NOUN_GENDERS added ✅, "medium" ✅
├── documentation/
│   └── pipeline/
│       ├── design-doc.md         ← schema design doc (updated ✅)
│       └── roadmap.md            ← this document (updated ✅)
└── ...
```