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.
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Introduction
The exponential growth of scientific publications has created challenges in efficiently accessing and synthesizing knowledge across domains [1]. Traditional search engines often fail to provide context-rich responses, while standalone LLMs tend to generate hallucinations due to their limited knowledge cutoff [2]. Retrieval-Augmented Generation (RAG][3] combines neural retrieval with generative capabilities to overcome these limitations. This paper investigates the implementation of a custom RAG system using Ollama [4] for model inference, LangChain for orchestration, FAISS for vector-based similarity search, and Hugging Face embeddings for semantic representation [3]. The system is applied to a dataset of 100 academic research papers to demonstrate its effectiveness in research assistance.
Conclusion
This study demonstrates the potential of combining Ollama with LangChain, FAISS, and Hugging Face embeddings to build an effective Retrieval-Augmented Generation framework for academic research assistance. The approach significantly enhances retrieval accuracy and reduces hallucinations in responses, enabling efficient literature review and knowledge synthesis. Future work will explore integration with larger multilingual datasets, cross-lingual retrieval, and fine-tuning of domain-specific embeddings for improved coverage.