add-nikkud

Add Nikkud

The reverse of strip-nikkud. Vocalisation requires a real model — a regex can't do this — so we call out to one of two trained models.

When to invoke

• "Add nikud to this"
• "Vocalise this Hebrew sentence"
• "Run nakdan on…"
• "Put vowels on this paragraph"
• Preparing unvocalized text for reading aloud, study, or display

Choosing a backend

Backend	When	Tradeoffs
Dicta nakdan (default)	Always available; user has internet; user wants best quality	Online; depends on Dicta's free public API; supports modern + rabbinic + poetry genres
unikud (Python, morrisalp/unikud)	User explicitly wants offline, on-device, or air-gapped	First run downloads a CANINE model (~500 MB); modern Hebrew only; lower accuracy than Dicta

Default to Dicta unless the user says "offline", "local", "no network", or has clearly asked for unikud.

Path A — Dicta nakdan (default)

Public JSON API behind nakdanpro.dicta.org.il. No key.

curl -sS -X POST https://nakdan-2-0.loadbalancer.dicta.org.il/api \
  -H 'Content-Type: application/json' \
  -d '{
    "task": "nakdan",
    "data": "<UNVOCALIZED HEBREW>",
    "genre": "modern",
    "addmorph": false,
    "keepqq": false,
    "nodageshdefmem": false,
    "patachma": false,
    "keepmetagim": true
  }'

Response shape

The response is a JSON array, one element per token (word or separator):

[
  {"word": "שלום", "sep": false, "options": ["שָׁלוֹם", "שְׁלוֹם", "שִׁלּוּם", ...], "fconfident": true},
  {"word": " ",    "sep": true,  "options": []},
  ...
]

Reconstruction rule: for each element,

• if sep == true, append word verbatim
• if sep == false, append options[0] (the highest-ranked vocalisation)

Concatenate to get the vocalised string.

Genre parameter

• modern — modern Hebrew (default)
• rabbinic — Talmudic / halakhic / responsa text
• poetry — piyut, biblical poetry style

If the user names a genre, use it. If the source is recognisably Talmudic or halakhic (Aramaic words, sugya structure), prefer rabbinic. For Tanakh, poetry often outperforms modern for prophetic books.

Other flags

• keepmetagim: true — preserve meteg / ga'ya marks already present
• keepqq: true — keep existing qamats qatan distinctions
• addmorph: true — also return per-word morphological analysis (rarely needed; bigger response)

Path B — unikud (offline)

pip install unikud

from unikud.framework import Unikud
u = Unikud()  # downloads model on first call; cached locally
print(u.vocalize("שלום עולם"))

First run will pull the model from Hugging Face (~500 MB) and cache it under ~/.cache/huggingface/. Subsequent runs are offline and fast.

If pip install fails or the model download fails, fall back to Path A and tell the user why.

Procedure

1. Detect input. If the input already contains nikkud (any code point in U+05B0–U+05C7 excluding punctuation), confirm with the user before re-vocalising — they may have meant strip-nikkud first, or they may want to refresh the existing pointing.

2. Choose backend. Default Path A. Switch to Path B only on explicit offline request.

3. Pick genre (Path A only) based on the text or the user's hint.

4. Call the API / library. For long text, chunk into paragraphs and concatenate — Dicta handles a few KB per request comfortably.

5. Render. Return the vocalised text in a fenced code block. If the user wants to see alternative vocalisations for ambiguous words (Path A only), expose options[1:] for the words where fconfident is false.

Caveats

• Vocalisation is inherently ambiguous in Hebrew (homographs distinguished only by context). Even the best models will misvocalise occasionally — flag low-confidence tokens (fconfident: false) when the user is using the output for something high-stakes.
• The Dicta endpoint is undocumented and unversioned. Treat it as best-effort and fall back to unikud (or report failure) if it 404s or schema changes.
• Don't run add-nikkud on text fetched from Sefaria — Sefaria text is already vocalised by traditional sources (Masoretic for Tanakh, etc.) and re-vocalising would be both wasteful and likely worse than the source.