nl-processing 0.3.0

Natural language processing playground

nl_processing

Dutch language processing toolkit powered by LangChain + OpenAI.

Installation

pip install nl_processing

Modules

Module	Class	Description	Docs
extract_text_from_image	ImageTextExtractor	Extract Dutch text from images via Vision API	docs
extract_words_from_text	WordExtractor	Extract and normalize words from markdown text	docs
translate_text	TextTranslator	Translate text (NL → RU) with markdown preservation	docs
translate_word	WordTranslator	Batch-translate words (NL → RU)	docs
database	DatabaseService	Remote source-of-truth persistence for words, translations, and exercise scores	docs
sampling	WordSampler	Weighted word sampling with adversarial distractors	docs
database_cache	DatabaseCacheService	Local-first cache for vocabulary practice (planned)	docs

Each module's docs/ folder contains a product brief, PRD, and architecture doc.

Recommended: database_cache for Interactive Practice

Status: database_cache is currently in the design/documentation phase. The implementation is planned.

database_cache is a local-first cache module that accelerates the vocabulary practice loop. It sits in front of the remote database module and keeps a durable local snapshot of translated words and exercise statistics using SQLite. All reads are served entirely from local storage (sub-200ms).

Why Use It

• Interactive practice sessions — word retrieval and score-aware sampling without network latency.
• Offline score recording — exercise outcomes are written locally first and synced later.
• Stale-while-revalidate — a stale cache is served immediately while a background refresh happens.
• Safe sync — pending local writes use idempotent event IDs, so retries never double-apply scores.
• Exercise-aware — initialized with specific exercise_types, mirrors the remote per-exercise-type tables.

How It Works

1. Initialized with user_id, language pair, exercise_types, and cache_ttl.
2. On init(): opens/creates local SQLite, returns CacheStatus, starts background refresh if stale.
3. get_words() and get_word_pairs_with_scores() — read from local cache only.
4. record_exercise_result() — updates local score and appends to a durable outbox.
5. flush() — replays pending events to remote database using idempotent event IDs.
6. refresh() — fetches a fresh snapshot from remote, atomically swaps, reapplies pending local events.

Planned API

from nl_processing.database_cache.service import DatabaseCacheService
from nl_processing.core.models import Language, PartOfSpeech, Word
from datetime import timedelta

cache = DatabaseCacheService(
    user_id="alex",
    source_language=Language.NL,
    target_language=Language.RU,
    exercise_types=["nl_to_ru", "multiple_choice"],
    cache_ttl=timedelta(minutes=30),
)

status = await cache.init()
pairs = await cache.get_words(word_type=PartOfSpeech.NOUN, limit=10, random=True)
scored = await cache.get_word_pairs_with_scores()

await cache.record_exercise_result(
    source_word=Word(normalized_form="fiets", word_type=PartOfSpeech.NOUN, language=Language.NL),
    exercise_type="nl_to_ru",
    delta=-1,
)

await cache.flush()

Relationship to Other Modules

• database — authoritative remote store; database_cache consumes its export/sync APIs.
• sampling — should use database_cache as its hot-path data source for score-aware sampling.
• CachedDatabaseService (legacy) — superseded by database_cache; retained for backward compatibility.

Development

uv sync                # install dependencies
make check             # full lint + test pipeline
uv run pytest tests/unit   # unit tests only (free, no API key)

See docs/ENV_VARS.md for required environment variables and NEON.md for database setup.

Contributing

See docs/REALEASE_WORKFLOW.md for the release process and publishing considerations.