# Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model
## 1 Introduction  
The problem of an inconsistent process of searching for and finding materials during study serves as the basis for this investigation. The process of collecting materials, which involves different sources of information from different sources, often encounters obstacles because there is no central database for all materials. The success and efficiency of a student's learning can be measured by their ability to access the materials they need quickly and accurately. If a student cannot easily access the material they need, their motivation and perceived quality of education can be affected. Implementing a system of centralized storage and retrieval creates a clear structure that allows quick access to key resources, defines key learning areas, and better guides students to the materials they need. With such a planned approach, students can expect better coordination and organization of materials as well as greater transparency in terms of deadlines, content, and assignments. To achieve optimal organization of materials, it is also necessary to understand the goals and needs of students so that a digital tool and system can be created that supports the learning process quickly and with quality. The main objective of this work is therefore to develop a software support that integrates all teaching materials and ensures that they are easily and quickly accessible to users. The process involves the implementation and customization of an API (application programming interface) for an existing LLM/chatbot (large language model/chatbot) model [1] that aims to provide precise answers to questions based on a comprehensive database of educational materials. Different types of educationalmaterials, such as scripts, presentations, and articles, can provide different information to users. To achieve this, knowledge, the correct application of text processing algorithms, and an understanding of the semantics and context within the materials are essential to create an efficient and user-friendly search system. Defining the functions of the program and integrating them into an advanced LLM/chatbot model and understanding the basic needs of the users are crucial for effective communication between users and the program. In response to the complex demands of modern learning, many authors have addressed the integration of AI (artificial intelligence) tools into various learning systems. However, this is an area that is rapidly evolving technologically, almost daily. Therefore, only recent work was considered in the literature review, and a few examples are highlighted here. A collection of open educational approaches influenced by both the Open Educational Resources (OER) movement and the digital collaboration practices can assist educators in navigating, and potentially excelling, in an era marked by the rapid advancement of AI [2]. A support system was developed for an education center by using a chatbot that provides quick answers and is accessible at any time via a web platform. Technologies such as the Dialogflow platform were used for the user interface component and the dialog management component while Firebase was used for the database component in the architecture of the chatbot [3]. By examining the impact of ChatGPT (Chat Generative Pre-trained Transformer) and large language model (LLM) services on learning and instruction in higher education and mapping the system's capabilities, 13 AI-based implications for students' learning in higher education have been defined [4]. The use of ChatGPT to improve students' programming skills was also tested. This included tasks such as generating code from problem descriptions, creating pseudocode for algorithms based on text, and debugging code [5]. In another study, the educational and cultural environment of computer use in general and the additional effects of ChatGPT on this, particularly in e-learning, were worked out.