Evaluating Retrieval-Augmented Generation(RAG) systems remains a challenging task: existingmetrics often collapse heterogeneous behaviorsinto single scores and provide little insightinto whether errors arise from retrieval,reasoning, or grounding. In this paper, we introduceRAGVUE, a diagnostic and explainableframework for automated, reference-freeevaluation of RAG pipelines. RAGVUE decomposesRAG behavior into retrieval quality,answer relevance and completeness, strictclaim-level faithfulness, and judge calibration.Each metric includes a structured explanation,making the evaluation process transparent. Ourframework supports both manual metric selectionand fully automated agentic evaluation. Italso provides a Python API, CLI, and a localStreamlit interface for interactive usage. Incomparative experiments, RAGVUE surfacesfine-grained failures that existing tools suchas RAGAS often overlook. We showcase thefull RAGVUE workflow and illustrate how itcan be integrated into research pipelines andpractical RAG development. The source codeand detailed instructions on usage are publiclyavailable on Github.

Analyzing historical discourse in large-scale newspaper archives requires scalable and interpretable methods to uncover hidden themes. This study systematically evaluates topic modeling approaches for newspaper articles from 1955 to 2018, comparing probabilistic LDA, matrix factorization NMF, and neural-based models such as Top2Vec and BERTopic across various preprocessing strategies. We benchmark these methods on topic coherence, diversity, scalability, and interpretability. While LDA is commonly used in historical text analysis, our findings demonstrate that BERTopic, leveraging contextual embeddings, consistently outperforms classical models in all tested aspects, making it a more robust choice for large-scale textual corpora. Additionally, we highlight the trade-offs between preprocessing strategies and model performance, emphasizing the importance of tailored pipeline design. These insights advance the field of historical NLP, offering concrete guidance for historians and computational social scientists in selecting the most effective topic-modeling approach for analyzing digitized archives. Our code will be publicly available on GitHub.