Indian document sets — government forms, college notices, WhatsApp exports — often mix Hindi, English and transliterated Hindi-in-Roman-script on the same page. A standard embedding model, benchmarked mostly on clean monolingual English text, handles this unevenly.
What to actually check
- Does the embedding model have documented multilingual support, and specifically Hindi — not just a claim of '100+ languages' with no per-language evaluation.
- Test retrieval with a query in one language against documents in the other; cross-lingual retrieval quality varies a lot between models.
- Transliterated text (Hindi words spelled in Roman script) is the hardest case — test it explicitly rather than assuming a multilingual model handles it like proper Devanagari.
A practical fallback
If cross-lingual retrieval quality is poor, translating queries and documents into one common language before embedding, using a small dedicated translation step, is a reliable workaround.
See embeddings models for RAG: how to choose.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
