Article
DOCUMENT RETRIEVAL SYSTEM USING RAG TEXT GENERATION
The exponential growth of digital content across domains such as research, legal, and corporate knowledge bases has created an urgent need for systems capable of accurate, context-aware document retrieval. Traditional keyword-based search engines are often insufficient, as they cannot capture semantic nuances, understand context, or synthesize information from multiple documents. This paper presents a Document Retrieval System using Retrieval-Augmented Generation (RAG), which combines retrieval-based embeddings with transformer-based generative models to provide highquality responses to user queries. The proposed system first retrieves the most relevant documents from a large corpus using dense vector representations and semantic search. Subsequently, a generative language model synthesizes coherent, context-rich answers by integrating information from these documents. Experimental results demonstrate that the RAG-based system significantly outperforms traditional retrieval models in precision, recall, and user satisfaction metrics.
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