Handle multilingual RAG by using multilingual embedding models like Qwen3, BGE-M3, or jina-embeddings-v3 that create a unified semantic space across 100+ languages, enabling users to query and retrieve documents in their native language.
Multilingual RAG relies on embedding models specifically trained to map text from different languages into a shared vector space, where semantically similar content has similar vectors regardless of language. Leading models like Qwen3 Embedding (supports 100+ languages), jina-embeddings-v3 (32 languages, 8192-token context), and BGE-M3 (multilingual with dense, sparse, and hybrid retrieval) all create unified semantic spaces. Queries in any supported language retrieve relevant documents across all indexed languages, enabling a global user base to interact with the system naturally. For optimal performance, combine multilingual embeddings with cross-encoder reranking to refine retrieval accuracy.
Qwen3 Embedding: 100+ languages, 32K context, Matryoshka dimension control, instruction prompting for domain tuning
jina-embeddings-v3: 32 languages, 8K context, task-specific LoRA adapters for query-document retrieval
BGE-M3: Multilingual with dense + sparse hybrid retrieval, fine-tuned for cross-lingual tasks