Integration of Large Language Models (LLMs) and RAG for Nepali Dataset

Introduction

The integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) has revolutionized the field of Natural Language Processing (NLP), especially for underrepresented languages like Nepali. This approach combines the generative power of LLMs with the contextual precision of retrieval systems, making it a perfect solution for creating applications like NagarGPT a chatbot that provides Nepali users with accurate, contextually relevant responses.

This blog delves into the tools, methodologies, and architecture behind this integration, showcasing how it bridges the gap in Nepali NLP and opens doors to innovative applications.

    Why Integrate LLMs and RAG?

-Challenge of Nepali NLP: 

Limited resources, datasets, and support for Nepali language processing.  

- Benefits of Integration:  

  - LLMs generate natural, fluent responses.  

  - RAG retrieves context-specific information from external knowledge bases.  

  - Together, they create responses that are accurate and comprehensive.

Architecture Overview

The system is built around two main components—Retriever and Generator—seamlessly integrated to process Nepali datasets.

Architecture

1. User Query:

-  The user inputs a query in Nepali.  

2.Retriever:  

   - Fetches the most relevant documents from a pre-defined knowledge base using semantic embeddings.  

   - Utilizes tools like Dense Vector Search and FAISS for high-speed retrieval.  

3. Generator:  

   - Processes the query and retrieved context to generate fluent and contextually relevant responses.  

   - Fine-tuned models like T5 or BART generate responses in Nepali.  

4. Response Output:

 Delivers the final answer back to the user in Nepali.  

Tools and Methodologies Used

1. Data Collection:  

   - Sources: Government websites, Nepali news portals, and administrative documents.  

   - Preprocessed to remove noise and normalize the text for NLP tasks.  

2. Embedding Models:  

   - Multilingual models such as XLM-Roberta or mBERT for creating dense vector representations of      Nepali queries and documents.  

3. Retrieval System:  

   - Dense Vector Search for identifying relevant documents using embeddings.  

   - Optimized with vector databases like Pinecone or FAISS for scalability.  

4. Generative Model:  

   - Transformer-based models fine-tuned on the Nepali dataset for sequence-to-sequence generation.  

5. Evaluation Metrics:  

   - Retrieval Metrics: Precision, Recall.  

   - Generation Metrics: BLEU, ROUGE, METEOR. 

Conclusion

The integration of LLMs and RAG for Nepali datasets is not just a technological innovation but a step toward linguistic inclusivity. It empowers Nepali speakers to access and interact with AI-driven systems seamlessly, making information retrieval more accessible and efficient.  

As we continue to refine and scale this approach, the future holds immense possibilities for Nepali NLP applications—from personalized chatbots to educational platforms and beyond.  

Thank you for reading! Feel free to share your thoughts, feedback, or ideas in the comments below. Together, let’s make AI more inclusive!