நாலாயிர திவ்ய பிரபந்தம் — Naalaayira Divya Prabandham

A Next.js 15 web app for all 4000 pasurams of the twelve Azhwars, plus Ramanuja Nootrandhadi (108), in Tamil script, with:

• English translation and a word-by-word glossary for about 3,450 verses
• Tamil life stories of every Azhwar (src/content/azhwars.ts)
• A full reader at /read for all 4000 pasurams. It has adjustable text size, an English on/off toggle, "continue reading", and a progress count of verses read. Progress is stored in the browser.
• Downloadable PDFs: one per prabandham (Tamil + English) and the complete book (Tamil)
• Infographics for the whole collection: verses per Azhwar and prabandham, Divya Desams, avatars, the twelve names, Thiruvaymozhi panns, sangu and chakram mentions
• A LangGraph agent on every pasuram page that uses a Groq-hosted model, with a key each visitor brings (BYOK), to write a Tamil பொழிப்புரை, பதவுரை and விளக்கவுரை, and to produce emotion and theme meters, story context, characters, places, a short lesson and a quiz

Run

npm install
npm run dev                  # http://localhost:3000

AI commentary: bring your own Groq key

On any pasuram page, open 🔑 Groq API key (BYOK), paste a key from https://console.groq.com/keys, and click சரிபார்த்துச் சேமி (verify and save). The app checks the key against Groq's model list and loads the models into a picker. The default model is openai/gpt-oss-120b.

• The key is stored only in that browser's localStorage.
• It is sent to this app's /api/agent route in the x-groq-api-key header, used for that one request, and never written to disk or logged.
• Optionally, set GROQ_API_KEY (and GROQ_MODEL) in .env.local to give visitors who have no key of their own a server-side fallback.

Without a key, the agent skips the model step and shows only the word-level analysis.

The agent (src/lib/agent/graph.ts)

START → retrieve → analyze ─┬─(API key)→ interpret → review ─┬─(issues, < 2 tries)→ interpret
                            └─(no key)──────────────→ compose ←┘
                                                        ↓
                                                       END

Node	Job
retrieve	Loads the verse, the verses before and after it, the prabandham and the Azhwar
analyze	Deterministic: grammar profile from the glossary, proper names, places, avatars, symbols
interpret	Calls Groq: strict JSON-schema output on gpt-oss models, JSON mode on others. The response is validated with the Zod schema
review	Checks the output is in Tamil and every field is filled. If not, it sends the issues back to interpret
compose	Merges everything into the infographic payload

/api/agent streams each step as NDJSON so the UI can show the pipeline as it runs. Results are cached in data/ai-cache/.

Data (data/)

scripts/build_data.py builds the dataset from:

• UHH-Tamilex/NalayiratTivviyapPirapantam: TEI XML with the text in ISO-15919 (converted to Tamil script), English translation and glossary
• raghuyennamalli/NDP_Pann_Prediction: Thiruvaymozhi 6–10, Ramanuja Nootrandhadi, and the Thiruvaymozhi panns

git clone https://github.com/UHH-Tamilex/NalayiratTivviyapPirapantam /tmp/ndp
# download the two xlsx files from NDP_Pann_Prediction/Data
python3 scripts/build_data.py --tei /tmp/ndp --pann <with_pann.xlsx> --nopann <without_pann.xlsx> --out data

Numbering follows the standard 1–4000 scheme. The two Thirumadals are split into couplets (78 + 148). Ramanuja Nootrandhadi is numbered 4001–4108. Places, avatars and symbols are detected by keyword matching, so their counts are approximate.

Neither source repository states a license. Check with the authors before you publish this data.

PDFs (public/pdf/)

Most PDF libraries misplace Tamil vowel signs (ெ, ே, ை), so the PDFs are printed from the reader pages with headless Chrome instead. The print stylesheet adds a cover page, page numbers and A4 layout. To regenerate them after changing the data or the layout:

NEXT_DIST_DIR=.next-pdf npm run build
NEXT_DIST_DIR=.next-pdf npx next start -p 3123 &
node scripts/build_pdfs.mjs --base http://localhost:3123          # all 26 files
node scripts/build_pdfs.mjs --base http://localhost:3123 --only thiruppavai

The script looks for Chrome in the usual locations, or in CHROME_PATH if you set it. The separate NEXT_DIST_DIR lets this production server run alongside npm run dev. Every reader page also has a print button (அச்சிடு), so visitors can save any section as a PDF from their browser.