Hybrid RAG-LLM System for Student Data Retrieval and Summarisation
DOI:
https://doi.org/10.15662/IJEETR.2026.0802407Keywords:
Retrieval-Augmented Generation (RAG), LangChain, Ollama, MiniLM Embeddings, FAISS Vector Database, Large Language Models, Student Information RetrievalAbstract
Schools bring out a lot of academic records as reports of the students, their end-semester marks and course-specific materials. In some cases, certain information in such documents can only be accessed through manual searches. This may be tedious and less efficient, especially when dealing with multiple students’ records. Thus, the current study suggests the Student Information Retrieval and Summarization System with Large Language Models and Retrieval-Augmented Generation [1, 2]. The data utilized in this study will include the academic records of CMMS students of approximately 50 students in the same grade. Student ID, name of students, the name of the branch, name of semester, name of subject, name of subject code, grade, and student SGPA are some of the information contained in the dataset. The system begins by removing the text in the PDF file followed by pre-processing the data.
A document chunking means that the text is further broken down into smaller fragments. This approach enhances precision of the information retrieved. Next, the chunks are converted to a vector embedding using the embedding model. This model helps in understanding the meaning of the text. Storing these vectors in a FAISS Vector Database [4,14] enables the process to be more efficient in retrieving the relevant information. When the user makes a query in natural language, the system isolates the most suitable bits of the document which has information regarding the query made by the user. These context fragments are then fed to a Large Language Model coupled with LangChain and trained on Ollama [11,12] which forms just the right answers to what the user is asking. Therefore, the system suggested enhances efficiency and precision of recalling the information with the responses generated. The findings depict that the suggested system is more efficient in retrieving more relevant and summarized responses than the traditional system of Information retrieval using keywords. Besides, the suggested system will reduce the time in which the information concerning the students will be retrieved. The proposed system thus enhances the usability of the information retrieval system. In addition, the proposed system enables the user to interrelate with the information with a natural language query. Hence, the proposed system increases the usability of the information system.
This excerpt shows the possibility of applying LLMs and vector databases to create smart information systems in the educational profession. The proposed framework can be extended to additional applications in information management in the education domain.
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