## 3 Hypothesis and Functional Specification
### Solution Architecture  
The architecture proposed in this paper is based on the RAG method combined with the Google Vertex AI Palm-2 model. The system comprises three primary phases: indexing, retrieval, and response creation. During indexing, documents from diverse sources, including PDFs and webpages, are deconstructed into smaller text segments. Subsequently, the samples are saved in a vector database alongside their appropriate embeddings, enabling the system to perform semantic searches. The vector database employs cosine similarity to align incoming searches with the most pertinent literature included within the database. Data preparation is conducted in advance. Data from university sources is processed, cleaned, and stored in a vector database, with each text chunk assigned a fixed vector representation. The preprocessed and indexed data is utilized to respond to inquiries, indicating that the preparation was not conducted on the fly but was completed before query handling. Upon submitting a query, the system searches the vector database for semantically analogous text according to the established cosine similarity level during retrieval. The model utilizes BERT (Bidirectional Encoder Representations from Transformers) as the core retrieval mechanism to search the vector database for semantically analogous text. BERT is pre-trained on a large corpus and fine-tuned for contextual understanding, allowing it to create dense embeddings for user queries and database entries. This architecture supports semantic search by encoding contextual information, helping the model capture nuanced meanings and find semantically relevant results even when exact keyword matches are absent. The model applies a cosine similarity metric to measure the relevance of the query and the text embeddings, leveraging BERT's capabilities for contextual matching, which enhances retrieval accuracy and relevance [29]. BERT's use in dense retrieval has shown strong performance in handling synonym and polysemy issues by representing words and sentences in a rich, multi-dimensional space, effectively bridging the gap between user intent and document content. Research has demonstrated that BERT-based dense retrieval models, such as those described by [PERSON] et al. and [PERSON] et al., are particularly effective for tasks requiring deep semantic alignment, offering significant improvements over traditional lexical retrieval models like BM25. These advancements make BERT an ideal model for enhancing retrieval quality in complex, context-dependent searches [30; 31]. Without pertinent documents, the model produces a context-free reply containing guidance for acquiring the required information. During the concluding phase of answer generation, the obtained context is transmitted to the Palm-2 model, which constructs a response informed by the input query and the pertinent context. The architecture is engineered for scalability and accuracy, guaranteeing that each query is processed autonomously, avoiding superfluous repetition or extraneous information. The architecture's focus on context retrieval markedly improves model accuracy while maintaining quick reaction times. The architecture proposed in this paper can be seen in Figure 1:  
The proposed architecture can be examined through its building blocks, which consist of three main components: the database, the server application, and the client application. * The integration of pgvector enhances the PostgreSQL relational database for vector-based data storage and querying. The system is enabled to execute intricate searches predicated on semantic similarity, which is essential for the assistant's operation. Vector-based storage facilitates applications such as natural language processing and recommendation systems, enabling the assistant to extract the most contextually pertinent information from a collection of papers. * The server program, created in Python utilizing the Flask framework, is the foundation of assistance.