DeepRAG: Building a Custom Hindi Embedding Model for Retrieval Augmented Generation from Scratch

TL;DR

DeepRAG tackles Hindi retrieval challenges in RAG by building a complete Hindi embedding pipeline from scratch. It introduces a Hindi-specific SentencePiece tokenizer, a Hindi-optimized transformer with unique attention and pooling, and a multi-stage contrastive training regime, achieving substantial gains over multilingual baselines. The results show roughly 23–24% higher retrieval precision and 13% gains in intrinsic semantic similarity tasks, validating language-specific design choices. The work demonstrates practical end-to-end Hindi RAG with LangChain integration and offers a roadmap for extending to other Indic languages.

Abstract

In this paper, I present our work on DeepRAG, a specialized embedding model we built specifically for Hindi language in RAG systems. While LLMs have gotten really good at generating text, their performance in retrieval tasks still depends heavily on having quality embeddings - something that's been lacking for Hindi despite being one of the world's most spoken languages. We tackled this by creating embeddings from the ground up rather than just fine-tuning existing models. Our process involved collecting diverse Hindi texts (over 2.7M samples), training a custom SentencePiece tokenizer that actually understands Hindi morphology, designing transformer architecture with Hindi-specific attention mechanisms, and optimizing with contrastive learning. Results were honestly better than I expected - we saw a 23% improvement in retrieval precision compared to the multilingual models everyone's been using. The paper details our methodology, which I think could help others working with low-resource languages where the one-size-fits-all multilingual models fall short. We've also integrated our embeddings with LangChain to build complete Hindi RAG systems, which might be useful for practitioners. While there's still tons more to explore, I believe this work addresses a critical gap for Hindi NLP and demonstrates why language-specific approaches matter.