Publish Arabic and right-to-left documentation

Each Nibleaf language has its own page tree and direction. Arabic reader chrome renders right to left while inline code and code blocks remain left to right. Publish only real translations and connect the pages that are actual counterparts.

Add the Arabic language

Open the page-tree language controls, add Arabic with the correct language code and rtl direction, and decide which language is the default. An Arabic page may use a different slug and location from its English counterpart.

Do not create empty translated copies just to make the trees match. Missing, non-indexable, and external-canonical pages should not be advertised as a reciprocal language cluster.

Connect translations

Link each translated page to its actual counterpart. Nibleaf uses that explicit relationship when generating hreflang; matching slugs are not required.

English: /getting-started
Arabic:  /البدء

The initial HTML should agree on lang, dir, page title, description, Open Graph locale, canonical, language alternates, and structured-data language.

Test mixed technical text

Use a sentence containing Arabic prose, a command, a path, a query string, and a version number:

شغّل الأمر docker compose up -d، ثم افتح /docs?lang=ar وتأكد من الإصدار v0.1.1.

Check the line in Visual, Markdown, Preview, and the published reader. Copy the command into a plain-text field to verify its logical order. Also check focus order, previous/next arrows, breadcrumbs, the table of contents, nested sidebars, font weights, mobile menus, long code blocks, and heading spacing.

Verify Arabic search

Arabic projects index exact normalized fields and lower-weight morphology fields. Both remove diacritics and tatweel, fold common alef forms, and map alef maqsura to ya. The morphology lane then applies a conservative Lucene Light10-style affix stemmer. Exact normalized tokens remain available and rank separately, so stemming can add recall without replacing the original spelling signal.

This is not semantic retrieval, triliteral-root extraction, or general lemmatization. Short tokens, code, mixed Arabic/Latin identifiers, and protected ambiguous words are excluded from morphology processing. Exact phrases remain the strongest signal; lexical, morphology, and bounded fuzzy lanes fill in additional results.

Test words with and without diacritics, alef variants, tatweel, common prefixes and suffixes, singular and plural forms, exact technical phrases, and product terms written in Arabic and Latin script. Include negative queries that must not match. Use real page vocabulary, not synthetic keyword lists.