da9cdbfa1b
The pipeline docs still described pipeline.ts as pseudocode and the validation module as unwritten. Both have been implemented and run. - CLAUDE.md: replace the "no executable pipeline yet" description with the actual module flow, plus the two invariants worth preserving (resumability via headword diffing, raw responses saved before parsing) - DATA_PIPELINE.md: mark the seven implemented modules, add a module responsibility map and the CLI flag table, drop the resolved warning about hardcoded prompt values - roadmap.md: check off phase 3 tasks 3.2/3.3/3.4, record where the build diverged from the plan, note that validate.ts is stricter than its own spec - STATUS.md: phase 3 is data work now, not code work Also records two open issues: the systemic difficulty-ordering rejection cause, and the hard-tier shortfall pending a full run. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
224 lines
14 KiB
Markdown
224 lines
14 KiB
Markdown
# Lila Data Pipeline
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> How vocabulary data is generated and gets into PostgreSQL.
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> Last updated: 2026-08-20 · Branch: `refactor/gemini-only-pipeline`
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**Authoritative detail lives in two companion docs:**
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| Doc | What's in it |
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| ------------------------------------------------ | ------------------------------------------------------------------------------ |
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| [pipeline/design-doc.md](pipeline/design-doc.md) | Schema design, difficulty model, query patterns, Gemini JSON contract, indexes |
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| [pipeline/roadmap.md](pipeline/roadmap.md) | Phase-by-phase plan with task checklists and acceptance criteria |
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This file is the orientation layer: what the pipeline is, what exists on disk today, and what is not built yet.
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The previous local-LLM pipeline (llama.cpp, adapter pattern, 10-model evaluation, CEFR voter ensemble, Kaikki gender lookup) has been removed from the codebase. Its documentation is preserved under `archive/` and describes `utils/` and `config/` modules that **no longer exist**:
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- [archive/data-pipeline-local-llm.md](archive/data-pipeline-local-llm.md) — the old pipeline stages and file layout
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- [archive/llm-setup-local.md](archive/llm-setup-local.md) — llama.cpp / cloud provider configuration
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- [archive/model-strategy-cefr-voters.md](archive/model-strategy-cefr-voters.md) — the multi-model voter architecture for sense-disambiguated CEFR assignment
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---
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## What changed, and why
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The old pipeline ran small local models and needed a deterministic Kaikki Wiktionary lookup to patch grammatical gender, because local models hallucinated it. The rewrite drops local inference entirely in favour of the Gemini API: one provider, no adapter layer, gender produced directly by the model and enforced by validation instead of by a second data source.
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The data model changed with it. The live `vocabulary_entries` / `entry_translations` tables (one row per word sense, populated from Kaikki) are replaced by `words` → `senses` → `translations`, where translations hang off a **sense**, not off a flat entry. That is the whole point of the rewrite: a quiz question can now be tied to one specific meaning of a word.
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---
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## Flow
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```
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source-data/{lang}/{pos} frequency wordlists, one word per line, UTF-8
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│
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▼
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Gemini API batches of 20 words, one language at a time
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│
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▼
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validation per-entry; invalid entries → rejection log, not the DB
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│
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▼
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db/staging.db SQLite staging (words, senses, translations)
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│
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▼
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import script SQLite → PostgreSQL via Drizzle, transaction per batch
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│
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▼
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PostgreSQL (dev :5432, then prod)
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```
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Each language is processed independently so definitions and examples are written **in that language** — a German word gets a German definition, not a translation of an English one. Only the translations cross language boundaries.
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The app always reads from PostgreSQL. SQLite exists purely as a staging file so re-runs, prompt tweaks, and spot-checks never touch a real database.
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---
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## What exists on disk today
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The pipeline is implemented and running. Every module below is executable code with
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co-located unit tests in `data-pipeline/tests/`.
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| Path | State |
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| --------------------------------------------- | --------------------------------------------------------------------- |
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| `data-pipeline/source-data/{lang}/{pos}` | ✅ Noun lists for `de`, `en`, `es`, `fr`, `it` |
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| `data-pipeline/prompt` | ✅ Templated Gemini prompt with `{{PLACEHOLDER}}` substitutions |
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| `data-pipeline/sourceLists.ts` | ✅ Wordlist discovery, trim/dedup normalization |
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| `data-pipeline/promptTemplate.ts` | ✅ Placeholder rendering; throws on any unreplaced `{{...}}` |
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| `data-pipeline/gemini.ts` | ✅ Structured-output API client with retry/backoff |
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| `data-pipeline/validate.ts` | ✅ Per-entry validation (design-doc §6.4) |
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| `data-pipeline/staging.ts` | ✅ SQLite writes, one transaction per word |
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| `data-pipeline/pipeline.ts` | ✅ Orchestrator with CLI flags, resumable |
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| `data-pipeline/db/schema.sql` | ✅ SQLite staging schema |
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| `data-pipeline/db/staging.db` | ✅ Populated — gitignored |
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| `data-pipeline/responses/` | ✅ Raw Gemini responses, one JSON per batch — gitignored |
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| `data-pipeline/rejections/{lang}-{pos}.jsonl` | ✅ Rejection log, one JSON per failed entry — gitignored |
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| SQLite → PostgreSQL import script | ❌ Not written (Phase 4) |
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| `data-pipeline/kaikki-source-files/` | ⚠️ 5.7 GB of leftover JSONL from the old pipeline; nothing reads them |
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Directory naming follows the language/POS codes used in `packages/shared/src/constants.ts` (`de/noun`, not `german/nouns`) so no name mapping is needed anywhere in the pipeline.
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## Module responsibilities
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```
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sourceLists.ts discoverSourceLists() → [{ sourceLanguage, pos, words, filePath }]
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normalizeWords(): trim, drop empties, dedup preserving order
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promptTemplate.ts renderPrompt(): substitutes SOURCE_LANGUAGE_NAME/_CODE, POS,
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TARGET_LANGUAGE_CODES, TARGET_LANGUAGE_UNION, INPUT_WORDS
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gemini.ts buildEntriesResponseSchema(): OpenAPI schema with enums narrowed
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to this batch's source/POS/target languages
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generateContent(): 5 attempts, retries 429/500/503, honours the
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API's own retryDelay; rejects non-STOP finishReason
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validate.ts validateEntry() → "valid" | "empty" | "invalid"
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staging.ts openStaging(), getStagedHeadwords(), stageEntry(), countStagedRows()
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pipeline.ts orchestration, batching, rate-limit delay, rejection logging
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```
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Two properties worth knowing:
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- **Resumability is free.** `getStagedHeadwords()` diffs the input list against what is
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already in `staging.db`, so an interrupted run picks up exactly where it stopped and
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never re-spends quota on a staged word.
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- **Raw responses are saved before parsing.** Validation-rule changes can be replayed
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against `responses/` without calling the API again.
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`"empty"` is a distinct outcome from `"invalid"`: the contract says a word that is not a
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valid noun in that language comes back with `"senses": []`. Those are _skipped_ and
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counted separately, not written to the rejection log.
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---
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## Staging schema
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`data-pipeline/db/schema.sql` mirrors the PostgreSQL schema with two SQLite concessions: IDs are `TEXT` (`crypto.randomUUID()`), and `definitions` / `examples` are JSON-encoded strings because SQLite has no array type. The import script parses them back into PostgreSQL `TEXT[]`.
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```
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words id, headword, language_code, pos UNIQUE(headword, language_code, pos)
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senses id, word_id→words, sense_index, UNIQUE(word_id, sense_index)
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difficulty, definitions, examples
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translations id, sense_id→senses, target_language_code, UNIQUE(sense_id, target_language_code, translation)
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translation, gender, difficulty
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```
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`difficulty` is `easy | medium | hard` on both `senses` and `translations`, and they mean different things — sense difficulty is "is this _meaning_ appropriate for the level", translation difficulty is "is this _word_ an acceptable answer". design-doc §4 explains how queries use sense difficulty as a ceiling and translation difficulty as the target.
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---
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## The prompt
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`data-pipeline/prompt` is the working prompt, checked in as a plain text file and edited by hand. It is fully templated: `promptTemplate.ts` substitutes source language (name and code), POS, target languages, and the word batch, then fails loudly if any `{{PLACEHOLDER}}` survives rendering — so a typo in a placeholder name can never silently reach the API.
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What it enforces, beyond the JSON shape in design-doc §6.3:
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- Raw JSON only — no markdown fences, comments, or trailing commas; one object per input word, in input order.
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- Definitions and examples in the **source** language.
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- Gender required for `de` (m/f/n) and `it`/`es`/`fr` (m/f); always `null` for `en`.
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- German translation nouns capitalized; Romance-language nouns lowercase unless proper nouns.
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- Base dictionary form, no articles or determiners.
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- 1–3 senses per word, most words 1; skip rare, archaic, and technical senses.
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- Up to 2 translations per target language per sense, only genuine synonyms or difficulty variants.
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- A translation's difficulty may never be lower than its sense's difficulty, and a sense's
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difficulty should equal the easiest translation difficulty in that sense.
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- A word that isn't a valid noun in that language comes back with `"senses": []`.
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The copy-paste bugs that existed in the pre-templating draft (hardcoded `"en"` in rules 2–3,
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a contradictory target list in rule 15, "valid English noun" in rule 31) are fixed — those
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values are now placeholders.
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Beyond the prompt, `gemini.ts` constrains the output with a **response schema** sent on every
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request, with enums narrowed to that batch's source language, POS, and target languages. The
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shape of the JSON is therefore enforced by the API, and `validate.ts` is left to enforce the
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things a schema cannot express: cross-field difficulty ordering, gender rules per target
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language, sequential `sense_index`, translation coverage and caps, and that the headword was
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actually in the input batch.
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Validation is the safety net, not the prompt — every entry is checked before it reaches SQLite, and rejects go to a log for review rather than silently disappearing. Target reject rate is under 10%.
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> **Known systemic rejection cause (open).** Effectively all current rejections are
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> `translation difficulty lower than sense difficulty`. The model tends to tag a sense
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> `medium` while correctly tagging some of its translations `easy`. Since the prompt already
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> defines sense difficulty as the easiest translation difficulty in the sense, this value is
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> derivable and the entry is otherwise good — normalizing it in `validate.ts` instead of
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> rejecting would recover these words. Not yet implemented.
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---
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## Running it
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```bash
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docker compose up -d pipeline-database # dedicated PostgreSQL on :5433
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pnpm --filter @lila/pipeline pipeline:run # tsx --env-file .env pipeline.ts
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pnpm --filter @lila/pipeline test
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```
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`pipeline.ts` takes CLI flags (pass them after `--`, which the script strips itself):
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| Flag | Default | Meaning |
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| --------------- | ------- | -------------------------------------------------------- |
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| `--langs` | all | Comma-separated source languages, e.g. `de,es` |
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| `--pos` | `noun` | Part of speech to process |
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| `--batch-size` | `20` | Words per Gemini request |
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| `--max-batches` | all | Cap batches per list — useful for smoke tests |
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| `--delay-ms` | `6000` | Pause between requests, for rate limiting |
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| `--dry-run` | `false` | Render prompts and print batches without calling the API |
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```bash
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# smoke test: one German batch, no API calls
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pnpm --filter @lila/pipeline pipeline:run -- --langs de --max-batches 1 --dry-run
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```
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Because runs are resumable, interrupting with Ctrl-C is safe — already-staged words are
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skipped on the next run.
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The pipeline reads `.env` from the repo root: `GEMINI_API_KEY`, plus `PIPELINE_POSTGRES_USER` / `PIPELINE_POSTGRES_PASSWORD` / `PIPELINE_POSTGRES_DB` / `PIPELINE_DATABASE_URL`. The pipeline database is deliberately separate from the app database (`:5432`) so pipeline work can never damage dev data.
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---
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## Phase status
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Full breakdown in [pipeline/roadmap.md](pipeline/roadmap.md).
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| Phase | State |
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| ------------------------------------------------- | -------------------------------------------------------------- |
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| 1 — Drizzle schema (words/senses/translations) | ✅ Complete |
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| 2 — Preparation (wordlists, DBs, prompt) | ✅ Complete |
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| 3 — Build the pipeline → SQLite | 🔄 **Current.** Code complete; full 5-language run in progress |
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| 4 — Migration & SQLite → PostgreSQL import | ⬜ Not started |
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| 5 — App integration (`getGameTerms`, distractors) | ⬜ Not started |
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| 6 — Production deploy | ⬜ Not started |
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| 7 — Extend to verbs, adjectives, adverbs | ⬜ Not started — new wordlists + prompt only, no schema change |
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All Phase 3 code is written and unit-tested. What remains is the data work: finish the
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5-language run, resolve the systemic rejection cause noted above, and hand-check 50 entries.
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Target for Phase 3: the full deduped noun lists (~1,550–1,750 unique words per language after
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dedup) × 5 languages in staging, reject rate under 10%, 50 entries spot-checked by hand.
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**Known risk for Phase 5 — the `hard` tier is nearly empty.** Generated difficulty skews
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heavily easy, with `hard` translations well under 1% of the corpus. The design-doc §5.1 game
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query filters translation difficulty as an _exact_ match, so a "hard" game currently has far
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too few rows to fill one round plus distractors. This needs prompt calibration before import,
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not after.
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Phase 5 is where this becomes visible in the app: `packages/db/src/models/termModel.ts` still queries `vocabulary_entries`/`entry_translations` and must be rewritten against the sense-based schema. Until then, production runs on the old data.
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