2026-05-04T19:54:08.399Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:11520 2025-08-30T13:30:00Z 1

Enhancing Retrieval - Augmented Generation with Graph-Based Retrieval and Generative Modeling 2025 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/11520 https://portal.sinteza.singidunum.ac.rs/paper/1010 D. Vujić A. NJeguš Н. Бачанин Џакула This paper presents the design and implementation of a robust RetrievalAugmented Generation (RAG) system that integrates advanced retrieval, ranking, and generative techniques to address knowledge-intensive tasks. The system combines dense retrieval using ChromaDB, metadata-driven keyword extraction with YAKE and KMedoids algorithm for clustering keywords, graph-based retrieval leveraging PageRank, and cross-encoder re-ranking to deliver precise and contextually relevant results. These retrieval outputs are synthesized into high-quality conversational responses using Hugging Face models and Google API. A modular pipeline ensures scalability, seamlessly integrating various retrieval and generative components. Evaluation results demonstrate high retrieval precision, improved recall through graph-based methods, and enhanced response quality through structured prompt engineering. This work highlights the effectiveness of combining diverse techniques in RAG systems, offering a foundation for scalable, reliable, and context-aware applications in domains such as customer support, education, and research. conferenceObject 3 9 https://doi.org/10.15308/Sinteza-2025-3-9 Book of Proceedings of Sinteza 2025 - International Scientific Conference on Information Technology, Computer Science, and Data Science