Building an Enterprise RAG System for Non-English Documents

{ "title": "Building an Enterprise RAG System for Non-English Documents: A Deep Dive into Turkish/Multilingual RAG", "body_markdown": "# Building an Enterprise RAG System for Non-English Documents: A Deep Dive into Turkish/Multilingual RAG\n\nRetrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for building knowledge-intensive applications. It allows Large Language Models (LLMs) to access and incorporate external knowledge, significantly improving their accuracy and reducing hallucinations. While many resources focus on RAG for English documents, implementing it for other languages, especially morphologically rich ones like Turkish, presents unique challenges. This article delves into our experience building a production-ready RAG system for Turkish and multilingual documents, highlighting the techniques we employed, the challenges we overcame, and the impressive results we achieved. We'll specifically focus on morphological preprocessing, sentence-boundary chunking,