The primary goal of our semantic search engine is to enable advanced search capabilities that extract content based on semantics, not just keywords. This approach allows for a deeper understanding of the searched materials, making it possible to uncover connections and insights that might be missed by traditional keyword-based searches. By leveraging the power of AI and natural language processing, we aim to make vast collections of historical testimonies more accessible to researchers, historians, and other interested parties. This enhanced accessibility opens up new possibilities for academic research, historical analysis, and the preservation of important cultural and historical knowledge.
Our semantic search system combines advanced AI technologies with a modular architecture to deliver accurate and relevant search results. The system utilizes the RAG (Retrieval-Augmented Generation) method for efficient searching within a corpus of historical testimonies. This approach allows for more nuanced and context-aware searches, greatly enhancing the user's ability to find relevant information.