This randomized trial demonstrates a new web application for article analysis, suggesting efficient information retrieval for users.
The sheer volume of online news published every day makes it increasingly difficult for readers and researchers to keep up, let alone draw connections across sources. This paper describes the design, implementation, and deployment of a News Research Tool — a Streamlit web application that allows users to supply news article URLs, convert them into a searchable knowledge base, and pose natural language questions against the indexed content. The system is built around a Retrieval-Augmented Generation (RAG) pipeline that combines LangChain for orchestration, OpenAI's text-embedding-3-small model for semantic embedding, and Facebook's FAISS library for efficient vector retrieval.
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Bhanu Teja (2026) studied this question.
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