704 lines
25 KiB
Markdown
704 lines
25 KiB
Markdown
# 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 0 Preparation
|
||
Get wordlists, set up tooling, finalize the Gemini prompt.
|
||
|
||
Phase 1 Data Pipeline
|
||
Build the Gemini → validate → SQLite pipeline.
|
||
Produce a clean dataset of ~1000 nouns × 5 languages.
|
||
|
||
Phase 2 Schema & Migration
|
||
Create the new Drizzle schema (words, senses, translations).
|
||
Write the SQLite → Postgres import script.
|
||
Migrate the local dev database.
|
||
|
||
Phase 3 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 4 Production Deploy
|
||
Run the Drizzle migration on prod.
|
||
Import the dataset.
|
||
Verify the live app works end-to-end.
|
||
|
||
Phase 5 Extend POS
|
||
Run the pipeline for verbs, adjectives, adverbs.
|
||
No schema changes needed — new wordlists + adjusted prompts.
|
||
|
||
Phase 6 Future Features (out of scope for now)
|
||
Inflection tables, conjugation/declension exercises,
|
||
gender exercises, spaced-repetition scheduling.
|
||
```
|
||
|
||
**Dependency chain:**
|
||
|
||
```
|
||
Phase 0 → Phase 1 → Phase 2 → Phase 3 → Phase 4 → Phase 5
|
||
│
|
||
▼
|
||
Phase 6
|
||
```
|
||
|
||
Each phase depends on the previous one being complete. Do not skip
|
||
ahead.
|
||
|
||
---
|
||
|
||
# Part 2 — Phase Breakdown
|
||
|
||
---
|
||
|
||
## Phase 0: Preparation
|
||
|
||
**Goal:** Have everything you need before writing pipeline code.
|
||
|
||
**Tasks:**
|
||
|
||
- [ ] Acquire frequency-based noun lists for all 5 languages
|
||
- [ ] Clean and format the lists (one word per line, UTF-8)
|
||
- [ ] 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:** None.
|
||
|
||
**Acceptance criteria:**
|
||
|
||
- You have 5 wordlist files in `data/wordlists/` (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 1: 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 0 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 2: Schema & Migration
|
||
|
||
**Goal:** The new normalized schema exists in Drizzle, the local
|
||
Postgres database is migrated, and the SQLite data is imported.
|
||
|
||
**Tasks:**
|
||
|
||
- [ ] Write the new Drizzle schema file: - `words` table (headword, language_code, pos) - `senses` table (word_id FK, sense_index, difficulty,
|
||
cefr_level, definitions TEXT[], examples TEXT[]) - `translations` table (sense_id FK, target_language_code,
|
||
translation, gender, difficulty) - All CHECK constraints, UNIQUE constraints, indexes
|
||
- [ ] 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 1 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 3: 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 2 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 4: 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 3 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 5: 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 4 complete.
|
||
|
||
**Acceptance criteria:**
|
||
|
||
- All 4 POS types are playable in the app.
|
||
- Pipeline is repeatable for future data additions.
|
||
|
||
---
|
||
|
||
## Phase 6: 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 0: Preparation — Detailed
|
||
|
||
### 0.1 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/wordlists/nouns_de.txt`, `nouns_es.txt`, etc.
|
||
- Clean the lists: remove duplicates, remove words with spaces
|
||
(multi-word expressions), remove proper nouns if desired.
|
||
|
||
### 0.2 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;`.
|
||
|
||
### 0.3 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 CEFR level must be one of: A1, A2, B1, B2, C1, C2
|
||
- 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 1: Data Pipeline — Detailed
|
||
|
||
### 1.1 Create the SQLite schema
|
||
|
||
- Create a file `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,
|
||
cefr_level TEXT,
|
||
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.
|
||
|
||
### 1.2 Write the validation module
|
||
|
||
- Create `pipeline/validate.ts` (or .js).
|
||
- 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']
|
||
- `cefr_level` is in ['A1','A2','B1','B2','C1','C2'] or null
|
||
- CEFR ↔ difficulty mapping is consistent
|
||
- `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[] }`
|
||
|
||
### 1.3 Write the pipeline script
|
||
|
||
- Create `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 (data/rejections/{lang}_{pos}.jsonl)
|
||
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)`).
|
||
|
||
### 1.4 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 2: Schema & Migration — Detailed
|
||
|
||
### 2.1 Write the Drizzle schema
|
||
|
||
- Create or update `src/db/schema.ts`.
|
||
- Define the three tables using Drizzle's `pgTable` builder.
|
||
- Include all columns, types, constraints, and indexes as specified
|
||
in `docs/schema-design.md` §3.
|
||
- Keep the old tables in the file for now (commented out or in a
|
||
separate file) so the app doesn't break during development.
|
||
|
||
### 2.2 Generate and inspect the migration
|
||
|
||
- Run: `npx drizzle-kit generate`
|
||
- Open the generated SQL file in `drizzle/` (or wherever your
|
||
config puts it).
|
||
- Read it. Verify:
|
||
- Three CREATE TABLE statements
|
||
- 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.
|
||
|
||
### 2.3 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
|
||
```
|
||
|
||
### 2.4 Write the import script
|
||
|
||
- Create `scripts/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.
|
||
|
||
### 2.5 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 3: App Integration — Detailed
|
||
|
||
### 3.1 Rewrite getGameTerms
|
||
|
||
- Open the file containing `getGameTerms`.
|
||
- 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.
|
||
|
||
### 3.2 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.
|
||
|
||
### 3.3 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.
|
||
|
||
### 3.4 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 4: Production Deploy — Detailed
|
||
|
||
### 4.1 Pre-deploy checklist
|
||
|
||
- [ ] All Phase 3 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.)
|
||
|
||
### 4.2 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 scripts/import-to-postgres.ts
|
||
```
|
||
- Verify counts on prod.
|
||
|
||
### 4.3 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.)
|
||
|
||
### 4.4 Rollback plan
|
||
|
||
If something goes wrong:
|
||
|
||
- Restore the backup:
|
||
```
|
||
psql -h <host> -U <user> -d <db> < backup_20260722.sql
|
||
```
|
||
- Revert the code to the previous commit.
|
||
- Redeploy.
|
||
|
||
---
|
||
|
||
## Phase 5: Extend POS — Detailed
|
||
|
||
### 5.1 Per POS
|
||
|
||
Repeat the Phase 1 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.
|
||
|
||
### 5.2 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/
|
||
├── docs/
|
||
│ ├── schema-design.md ← companion design doc
|
||
│ └── roadmap.md ← this document
|
||
├── data/
|
||
│ ├── wordlists/
|
||
│ │ ├── nouns_de.txt
|
||
│ │ ├── nouns_en.txt
|
||
│ │ ├── nouns_es.txt
|
||
│ │ ├── nouns_fr.txt
|
||
│ │ └── nouns_it.txt
|
||
│ ├── rejections/ ← invalid Gemini entries (for review)
|
||
│ └── staging.db ← SQLite staging database
|
||
├── pipeline/
|
||
│ ├── run.ts ← main pipeline script
|
||
│ ├── validate.ts ← validation module
|
||
│ ├── prompt.ts ← Gemini prompt template
|
||
│ └── schema.sql ← SQLite schema definition
|
||
├── scripts/
|
||
│ └── import-to-postgres.ts ← SQLite → Postgres import
|
||
├── src/
|
||
│ └── db/
|
||
│ └── schema.ts ← Drizzle schema (updated)
|
||
└── drizzle/ ← generated migration files
|
||
```
|