Voidly Atlas's semantic search at /v1/atlas/search used all-MiniLM-L6-v2, an English-only sentence-transformer. That model was trained on the same general-purpose web corpora as the rest of the sentence-transformers family, but it embeds non-English queries into a near-random region of the 384-d space — so a journalist in Tehran typing اینترنت ایران or in Moscow typing блокировка интернета got mostly noise.

We added a parallel multilingual index using paraphrase-multilingual-MiniLM-L12-v2 — same dimensionality (384), same sentence-transformers library, but trained on the LaBSE parallel corpus across 50+ languages. We re-embedded all 2,696 incidents into a separate SQLite table (incident_embeddings_multilingual) so the legacy English-only endpoint stays byte-identical.

New endpoint

GET|POST /v1/atlas/search/multilingual?q=... — same response shape as the English-only endpoint, plus a detected_language field from a cheap script-based heuristic (Unicode block prefixes for Arabic / Persian / Cyrillic / CJK / Hangul / Hiragana / Hebrew / Devanagari / Thai / Burmese / Ethiopic). The heuristic distinguishes scripts but won't separate Spanish vs French vs Italian inside Latin.

Test queries

• AR إيران الإنترنت → top-3 Iran incidents, similarity 0.71–0.73
• FA اینترنت ایران → top-3 Iran incidents, similarity 0.69–0.70
• RU блокировка интернета Россия → top-3 Russia incidents, similarity 0.76–0.79
• CN 中国互联网封锁 → top-3 China disruption rows, similarity ≈ 0.74
• ES censura internet → Mauritius / South Sudan / Turkey censorship rows, similarity 0.74–0.77
• EN baseline Iran internet blocking → top-3 Iran, similarity 0.72–0.73

Latency

• English-only baseline: ~40 ms (cached query encode + dot product over 2,637 rows)
• Multilingual: 73–89 ms (+33-49 ms overhead — L12 encoder vs L6, ~3x the parameters)
• Both well under the 200 ms p95 target documented in /v1/atlas/search/info

Honest caveats

• Incident descriptions are mostly English — the corpus itself is monolingual. Cross-language matching relies entirely on the encoder mapping non-English queries into the same shared embedding space; we are not retrieving Arabic-language documents because none exist.
• Multilingual sentence-transformers are typically 5-10pp lower precision than per-language or English-only equivalents on monolingual benchmarks. If you know your query is English, use /v1/atlas/search for best precision.
• Script-based language detection is a heuristic. Persian-specific letters (ژپچگیک) flip an Arabic match to fa, but otherwise we report the dominant script. Pure-ASCII Latin queries always report as en; the multilingual model still embeds them correctly, the detection field is just a hint.
• Templated IODA disruption descriptions (e.g. "Internet connectivity disruption in Iran") cluster very tightly in embedding space. Country/type filters (?country=IR&incident_type=censorship) remain strongly recommended.

How it's rebuilt

• Weekly cron 5 5 * * 0 runs /opt/voidly-ai/scripts/rebuild-incident-embeddings-multilingual.py via the ML venv (sentence-transformers + torch-cpu).
• The encoder run takes ~30 s on CPU for the full 2,696-incident corpus (batch=32).
• Service auto-reloads embeddings every 5 min via _maybe_reload_multilingual() — no restart needed.

This is an accessibility ship, not a precision claim. Journalists and researchers outside the West can now ask Voidly Atlas questions in their own language and get back the same permalink-backed incident IDs we hand to English speakers.