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August 19, 2025Open Access

A Review on Rag-Based Student Assistant Chatbot Using Lang Chain

Key Points

RAG-based chatbots enhance academic information retrieval, improving student access to timely resources and data.
Implementation of RAG with langchain enables intelligent interactions via chatbots, streamlining academic query resolutions.
Assessment of existing literature highlights the efficacy of vector databases and LLMs in delivering contextually relevant responses.
This innovative approach suggests significant advancements in educational technology, allowing institutions to better support students' needs.

Abstract

In the evolving landscape of educational technology, students face ongoing challenges in accessing timely and accurate academic information. Traditional query resolution systems-ranging from FAQs to administrative desks-are often inefficient. This review paper explores the application of Retrieval-Augmented Generation (RAG) and the LangChain framework to build intelligent, responsive, and domain-specific chatbots for academic institutions. Through the integration of a vector database, retriever modules, and large language models (LLMs), RAG-based systems ensure contextual relevance and data-grounded responses. This paper surveys existing literature, evaluates methodologies, and highlights the significance, implementation strategies, and expected outcomes of such systems in the educational sector. Keywords: Retrieval-Augmented Generation (RAG), LangChain, Vector Database, LLM

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