Efficient in-Domain Research Query Resolution using Retrieval Augmented Generation with Ollama

Abstract— Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing the performance of Large Language Models (LLMs) by grounding their outputs on external knowledge. This paper presents a domain-specific RAG pipeline integrating Ollama with LangChain, FAISS, and Hugging Face embeddings to process and query a custom corpus of 100 research papers. By leveraging FAISS for efficient similarity search and Hugging Face models for semantic embeddings, the system enables precise and context-aware retrieval of academic knowledge. The results demonstrate improved accuracy, contextual relevance, and reduced hallucinations compared to traditional LLM usage, making the framework suitable for research assistance and literature review automation.