A New Hybrid Search Approach to Optimize the Retrieval of Information from the Website at the Universidad Politécnica Salesiana

This paper presents a novel hybrid search approach to improve information retrieval from the Salesian Polytechnic University website, addressing the challenge of efficiently managing and accessing the growing volume of information. Leveraging virtual assistant technology, the study combines vector similarity and keyword-based techniques to optimize data retrieval. The methodology involves a structured process, including information gathering, architecture design, search execution and analysis of the results. The system architecture consists of three key layers: the intelligent layer, which uses the OpenAI API for query processing; the data layer, which uses the Qdrant database for storage; and the logic layer, responsible for query execution. Two search methods are applied: Vector similarity search, which retrieves data based on contextual relevance, and keyword search with BM25, which sorts documents by keyword relevance. Testing and analysis confirm that the hybrid search method significantly improves the efficiency and accuracy of information retrieval. The results show a significant improvement in the request measures obtained, where the 4 highest percentages were selected to obtain the context from which the answer is derived. The highest similarity values were 5.56, followed by 3.84, the effectiveness of this method in various knowledge areas of the university website. In conclusion, the hybrid search approach presented in this paper offers a promising solution to efficiently retrieve information from the Salesian Polytechnic University website, improve accessibility and ultimately improve user satisfaction.