# Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model
## 5 Model Testing and Evaluation
### Model Testing  
The program, with a user interface created with the Tkinter package, contains a field for entering the user's query and four buttons (Figure 10).  
By clicking the 'answer' button or the 'RETURN' key on the keyboard, the program searches the local database and uses the implemented GPT model to generate a response to the query. The program is not case-sensitive or syntax-sensitive but recognizes similar words. After clicking the button, the entered query is cleared for easier subsequent input. Based on the line of code (Figure 11):  
context = \"\
\".join( conversation history[-3:]) # Taking the last three queries as context (can be adjusted as needed)  
The program retains the context of the conversation, and the user can ask a question that relates to the previously answered question. The conversation depth in the code is set to 3 (the program remembers the context of the last 3 responses) but can be increased at the expense of response speed. If GPT cannot generate a meaningful answer or there is no information in the database, it returns 'I do not know' To delete the saved conversation that the program uses as context for its next response, the 'New Conversation' button is used (Figures 12-14).  
Figure 11: Context and history of the conversation.  
Figure 12: Answer to the question ”What is a matrix?”, disregarding syntax.  
Figure 10: Program interface.  
This study deliberately focuses on the essential functionalities of the program to search for educational material and emphasizes the simplicity and accessibility of this information. It is important to note that the focus was not on an in-depth examination of the user interface design (UI) or user experience (UX) aspects. Instead, the main focus was on the functionality of the algorithm and how it effectively performs its tasks. The primary user interfaces used in the study are purely for demonstrating functionality, while future development should necessarily focus on creating a user-friendly interface to enable wider use by educators and students, as emphasized in the study.  
Although not explicitly mentioned in the paper, there is a need to raise awareness of the importance of ethical considerations, including data protection, information security, and potential bias in educational materials or the model itself. The bias of the model results from the bias in the data on which the model is trained. Since the OpenAI model API is used, pre-trained parameters (weights) configured during training are imported. The database with the training materials is not the database on which the model was trained. It is the database from which the model reads the data in a previously learned way, only with different information. In the developed application, a special emphasis is placed on security. Emphasis is placed on regularly updating all packages and libraries to avoid potential security vulnerabilities. The need to implement cryptography to ensure the involiability of information is emphasized and the importance of security measures within the system itself is highlighted. These sections of the paper clearly show how seriously security issues related to the developed application are being addressed. It is important to point out that this is a local application that draws on materials that the user enters into the local database. This local nature of the application provides users with an additional layer of control over their own data, reducing the risk of unauthorized access or misuse. All of these measures have been carefully thought through to ensure responsible and ethical use of the system in the context of education.