Bridging Language Diversity: A Retrieval Augmented Generationbased Framework for Information Extraction for South Indian Language Documents

Abstract

Extraction of information from the corpus is an complicated task considering the information overload, format diversity, multilingually, and multimodal behavior of the information. The proposed work mainly targets accurate information extraction from South Indian Language documents, such as Malayalam, Kannada, Telugu, and Tamil, using the Retrieved Augmented Generation [RAG] model. The main problem associated with low-resource languages such as these is their dense morphology and agglutinative behavior. The goal of this research work is to incorporate RAG with language models to avoid hallucinated responses and successfully perform relevant information extraction from multilingual documents. In this work, RAG will be incorporated with leading FM models and versions of Gemini, Lamma, and DeepSeek. Implementation achieved using the Google Gemini API, Qdrant vector database, advanced Multilingual embedding and efficient prompt engineering. The application allows users to upload a corpus in their specific language and ask any question; the model will retrieve the accurate answer based on the query. The implementation details are publicly available and can be verified through the [link-https://github.com/siv3sh/Multilingual_document_Reader. git] GitHub repository. Each model varies in performance in terms of Latency, long-term dependencies, supporting tokens and accuracy in multilingual completions.