# Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model
## 3 Modeling a Hybrid Model
### Model Functionality and Structure  
A comprehensive understanding of how the chatbot works and how it is structured is crucial for assessing its efficiency and capabilities. From integration with other platforms and user interface design to data analysis and processing, each segment is critical to the final product. The software interface should be designed in such a way that it directly presents users with methods to use. Intuitivelyness reduces the need for detailed instructions and allows users to interact with the application right from the start. This feature is essential because, regardless of the technical capabilities, the usefulness of the application will be compromised if users are reluctant to engage with it. In the digital age, when everything is fast, users expect quick responses to their requests. The application must be tuned to guarantee prompt responses to user requests. Longer waiting times can cause the user to lose concentration or become restless. Certain sensitive data, such as API keys, are embedded directly in the source code. Such an approach can reveal vulnerabilities to security threats. Protection protocols such as external variables or cryptography should be implemented to ensure the invulnerability of this information. In addition, constant updates of all packages and libraries are essential to circumvent potential security flaws. The new hybrid model of the chatbot integrates important modules, including the aforementioned front-end user interface (UI), the back office, the knowledge-based module (database), and the e-learning module (chatbot). The front-end module focuses on creating an intuitive interface and an easily navigable user interface that allows users to interact with the chatbot across different devices. The back-office module, on the other hand, manages the operations in the background, orchestrates the business logic, and stores the data in the database while monitoring the interactions with the users. The knowledge-based module serves as a specialized repository for information about users, educational objects, and other relevant data, managing and maintaining the chatbot's knowledge base. This module is crucial for providing accurate answers to students' questions. Its architecture has been carefully designed to allow flexibility and adaptability. The e-learning bot module recognizes students' queries and provides accurate answers using natural language processing and domain ontology. As the core of the model, this module evaluates students' queries and provides relevant answers based on the knowledge-based module. The second integrated model is an automated framework that uses machine learning to train the chatbot to improve its ability to recognize patterns in data and increase accuracy. In addition, natural language processing enables the chatbot to understand and interpret human language. The schematic of the new model illustrates how these modules work together to provide a high-quality user experience (Figure 1). The user-driven process begins and concludes with the user's interaction with the system. Facilitated through a user interface (UI), the user's responses or requests are transmitted to the UI, initiating a chain of interactions. The UI interfaces with a database housing  
Figure 1: The principle of the hybrid e-learning AI-driven chatbot model. educational materials, linking seamlessly to a chatbot equipped with a comprehensive set of elements. This includes a reasoning system with three key components: the \"Text\" component handling input data preprocessing, the \"Algorithm\" serving as the computational engine for deriving conclusions, and the \"Operations Manager\" orchestrating tasks and managing overall system performance. Additionally, the chatbot incorporates information management to organize received and produced information, an H/C interaction supervisor ensuring appropriate responses, and a performance monitor to assess and enhance the chatbot's functionality.