Real-time full-text search holds essential value in current digital libraries because it helps users find documents with content rather than names [1]. Users can perform content-based searches that reveal files through the extraction of textual contents within documents and optimize the retrieval process for research databases as well as legal document search and enterprise knowledge management solutions [2]. A full-text search technique-powered document retrieval system, which seeks to create a content-based file searching mechanism, is analyzed within this report. These search systems implement ranking as their main step to evaluate document relevance through the combination of term frequency and document length analysis with inverse document frequency factors [3]. The foundation for improving search accuracy and efficiency depends heavily on knowledge about directory creation as well as score calculation methods. The research also explores performance comparison between Whoosh and Elasticsearch regarding their scaling capabilities and their abilities to index data and respond to search queries and rank results [4]. Whoosh functions best for compact document sets, yet Elasticsearch delivers real-time search functionality for extensive data collections. The final report will present the most effective solution for creating a content-based search system with high performance levels for various application domains.