RAGFlow supports cross-language queries through multilingual embedding models and language-agnostic retrieval components. The document parsing engine (DeepDoc) is language-agnostic—OCR, TSR, and DLR work on any language's text, script, and document layouts—so you can ingest documents in multiple languages into a single knowledge base. Semantic chunking similarly works across languages by identifying sentence and paragraph boundaries regardless of language. The key to cross-language retrieval is multilingual embeddings: models like OpenAI's text-embedding-3-large, or open-source models (mBERT, XLM-RoBERTa, multilingual-e5) represent concepts across languages in a shared semantic space, enabling similarity search between queries in one language and documents in another. For example, query in English ("climate change impacts") could retrieve French documents ("impacts du changement climatique") because multilingual embeddings understand semantic equivalence. BM25 keyword search is language-specific (keywords in English won't match French documents), so cross-language retrieval relies primarily on vector similarity. Alternatively, query translation can be applied—translate the user's query to each document language before retrieval if you prefer keyword-focused search. RAGFlow's configurable embedding model support makes multilingual retrieval straightforward: select a multilingual embedding model in the UI, and queries in any language will find relevant documents in any language in your knowledge base. Knowledge graph construction (if enabled) is language-agnostic because it focuses on relationships rather than vocabulary, further supporting cross-language understanding. The final language consideration is LLM generation—your chosen LLM must support the target language; models like GPT-4 support 100+ languages. Overall, RAGFlow's architecture makes cross-language RAG natural through multilingual embeddings and language-agnostic document processing, enabling global knowledge bases without language silos.
Related Resources: Building RAG Applications | Chunking Strategies for RAG