Building an Enterprise RAG System for Non-English Documents: A Turkish/Multilingual Case Study

Building an Enterprise RAG System for Non-English Documents: A Turkish/Multilingual Case Study Retrieval Augmented Generation (RAG) systems are revolutionizing how we interact with information, allowing us to ask complex questions and receive answers grounded in a vast sea of documents. While much of the focus has been on English language applications, the real power of RAG lies in its ability to unlock knowledge hidden within documents in any language. This article dives into the challenges and solutions of building a production-ready RAG system specifically for non-English documents, using Turkish as our primary example but with insights applicable to many other languages. The Challenge: Beyond Vanilla RAG The basic RAG pipeline is deceptively simple: Chunking: Divide your documents into manageable pieces. Embedding: Convert each chunk into a vector representation. Indexing: Store these vectors in a vector database. Retrieval: Based on a user query, find the most relevant chunks in t