engineering · 1 min read
Multilingual RAG: Building Knowledge Bases That Cross Languages
Global customers ask in their language; your docs are in English. Multilingual RAG bridges the gap. Here is how to ship it without doubling your indexing bill.
BABhogar AI TeamProduct & Engineering
Global enterprises serve customers in dozens of languages. Maintaining a separate knowledge base per language is impractical. Multilingual RAG lets one corpus serve them all - done right.
Why it matters
The naive answer is "embed everything with a multilingual model". The better answer is per-language query rewriting plus multilingual embeddings, which together close the cross-language recall gap most systems suffer from.
How Bhogar AI approaches it
Bhogar AI ships a multilingual KB profile: multilingual embeddings, per-language query rewriting, language detection on every query, and answers in the user's language by default with the option to cite original-language chunks.
- Multilingual embedding models (BGE-M3, Cohere multilingual)
- Per-language query rewriting
- Auto-translation of cited chunks into the answer language
- Per-language quality metrics in evals
- Coverage across 100+ languages including low-resource
What you get
Global support deployments report 80-90%+ answer parity across major languages and a single KB to maintain instead of one per locale.