RAGFlow supports multilingual document processing through intelligent parsing and configurable embedding models. The document parsing engine (DeepDoc) can extract text from PDFs in multiple languages—the OCR and layout recognition components are language-agnostic, identifying text structure regardless of script. Once text is extracted, semantic chunking preserves sentence and paragraph boundaries in any language, respecting natural language structure. RAGFlow’s embedding layer supports multilingual embeddings: you can configure models like OpenAI’s multilingual embeddings, open-source models (mBERT, XLM-RoBERTa), or multilingual variants of specialized embeddings. The hybrid search layer applies BM25 and vector similarity identically across languages, so keyword matching and semantic search work for any language in your knowledge base. For cross-language queries (e.g., query in English, document in Spanish), you can use multilingual embeddings that represent concepts across languages in a shared semantic space, enabling retrieval across language boundaries. Query translation is also possible—translate the user’s query to the document language before retrieval if preferred. Knowledge graph construction (if enabled) works across languages because it focuses on entity relationships rather than specific vocabulary. RAGFlow’s re-ranking layer is agnostic to language, scoring relevance based on semantic relationships. A practical limitation is that your chosen LLM must understand the target language for generation; if using OpenAI’s GPT-4, it speaks 100+ languages, while some open-source LLMs may have limited multilingual support. Overall, RAGFlow’s architecture makes multilingual RAG straightforward by decoupling language-agnostic retrieval from language-specific generation.
Related Resources: RAG Pipeline with Milvus | Improving Chunking for RAG