stdClass Object
(
    [id] => 17339
    [paper_index] => 202508-02-023698
    [title] =>  A REVIEW ON RAG-BASED STUDENT ASSISTANT CHATBOT USING LANG CHAIN 
    [description] => 
    [author] => Dharshan S, Puneeth KS, Sanjay G J, Vivek V, Dr Anitha DB
    [googlescholar] => 
    [doi] => https://doi.org/10.36713/epra23698
    [year] => 2025
    [month] => August
    [volume] => 10
    [issue] => 8
    [file] => fm/jpanel/upload/2025/August/202508-02-023698.pdf
    [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
    [doj] => 2025-08-19
    [hit] => 
    [status] => 
    [award_status] => P
    [orderr] => 19
    [journal_id] => 2
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Research & Development (IJRD)
    [short_code] => IJSR
    [eissn] =>  2455-7838 (Online)
    [pissn] => - -
    [home_page_wrapper] => images/products_image/2-n.png
)
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