The exploration and retrieval of information from large, unstructured document collections remain challenging. Unsupervised techniques, such as clustering and topic modeling, provide only a coarse overview of thematic structure, while traditional keyword searches often require extensive manual effort. Recent advances in large language models and retrieval-augmented generation (RAG) introduce new opportunities by enabling focused retrieval of relevant documents or chunks tailored to a user’s query. This allows for dynamic, chat-like interactions that streamline exploration and improve access to pertinent information. This article introduces Topic-RAG, a chat engine that integrates topic modeling with RAG to support interactive and exploratory document retrieval. Topic-RAG uses BERTopic to identify the most relevant topics for a given query and restricts retrieval to documents or chunks within those topics. This targeted strategy enhances retrieval relevance by narrowing the search space to thematically aligned content. We utilize the pipeline on 4,711 articles related to nuclear energy from the Impresso historical Swiss newspaper corpus. Our experimental results demonstrate that Topic-RAG outperforms a baseline RAG architecture that does not incorporate topic modeling, as measured by widely recognized metrics, such as BERTScore (including Precision, Recall and F1), ROUGE and UniEval. Topic-RAG also achieves improvements in computational efficiency for both single and batch query processing. In addition, we performed a qualitative analysis in collaboration with domain experts, who assessed the system’s effectiveness in supporting historically grounded research. Although our evaluation is focused on historical newspaper articles, the proposed approach more generally integrates topic information to enhance retrieval performance within a transparent and user-configurable pipeline effectively. It supports the targeted retrieval of contextually rich and semantically relevant content while also allowing users to adjust key parameters such as the number of documents retrieved. This flexibility provides greater control and adaptability to meet diverse research needs in historical inquiry, literary analysis and cultural studies. Due to copyright restrictions, the raw data cannot be publicly shared. Data access instructions are provided in the repository, and the replication code is available on GitHub: https://github.com/KeerthanaMurugaraj/Topic-RAG-for-Historical-Newspapers.
