## 3 Hypothesis and Functional Specification
### Data Preparation  
As the study outlines, the data preparation process is characterized by a systematic and rigorous approach to data acquisition from various sources, particularly on web pages and PDF documents. This method ensures the reliability and quality of the data. The data is initially stored in plain text format to ensure uniform processing. Once acquired, the raw data undergoes a comprehensive refinement process to eliminate extraneous and potentially distracting content. This cleaning stage includes removing non-essential elements such as headers, footers, personal names, and redundant information that may be repeated across multiple pages. Irrelevant or repetitive data, such as headers, footers, and hyperlinks that do not contribute to informational value, is deleted. Text alignment and spacing are ensured for readability. Furthermore, the removal of any personal information is conducted to ensure privacy, and the simplification of complex or redundant tables is achieved through the reorganization of data into smaller, semantically meaningful chunks. A vector representation is then assigned to each chunk for efficient retrieval based on semantic similarity, ensuring that essential information remains accessible in a more user-friendly format.  
Following the initial cleanup, the text is organized into meaningful paragraphs, which enhances coherence and readability. Unnecessary spaces within the text are removed, ensuring a streamlined and concise format. Complex elements such as tables, which may not be directly readable in text format, are converted into a more legible structure that  
Figure 3: Workflow for Query Formation and Contextual Response Generation in the RAG Method.  
retains the essential information. As illustrated in Figure 4, the preparation phase ensures that the data is optimal for further processing and analysis.  
After the data's sanitization, segmentation into smaller, manageable portions is performed. The segmentation is designed to enable finer-grained analysis and to assist in the alignment of the data with specific user queries during the retrieval phase. Each segment is transformed into a vector representation, which encodes the semantic content of the text in a numerical format. The importance of this vectorization is underscored by its role in enabling the system to perform semantic similarity retrieval, whereby user queries are matched with relevant information based on meaning rather than mere keyword similarity.  
Subsequent storage of these vectorized segments occurs in a specialized database designed to support efficient similarity searches. Data is indexed in this format, allowing for the rapid retrieval of contextually pertinent information, which makes it suitable for applications such as natural language processing and recommendation systems. This approach improves retrieval accuracy, and response times are optimized, allowing the system to handle large datasets effectively.  
This systematic preparation enhances the model's ability to retrieve relevant information accurately and quickly, improving the semantic similarity retrieval process's overall performance.