Sanskrit Subhasitas encapsulate centuries of cultural and philosophical wisdom, yet remain underutilized in the digital age due to linguistic and contextual barriers. In this work, we present Pragya, a retrieval-augmented generation (RAG) framework for semantic recommendation of Subhasitas. We curate a dataset of 200 verses annotated with thematic tags such as motivation, friendship, and compassion. Using sentence embeddings (IndicBERT), the system retrieves top-k verses relevant to user queries. The retrieved results are then passed to a generative model (Mistral LLM) to produce transliterations, translations, and contextual explanations. Experimental evaluation demonstrates that semantic retrieval significantly outperforms keyword matching in precision and relevance, while user studies highlight improved accessibility through generated summaries. To our knowledge, this is the first attempt at integrating retrieval and generation for Sanskrit Subhasitas, bridging cultural heritage with modern applied AI.

翻译：梵语箴言凝聚了数个世纪的文化与哲学智慧，但由于语言和语境障碍，在数字时代仍未得到充分利用。本研究提出Pragya，一个基于检索增强生成框架的梵语箴言语义推荐系统。我们构建了一个包含200条诗节的数据集，并标注了诸如激励、友谊、同情等主题标签。该系统利用句子嵌入模型，根据用户查询检索最相关的k条诗节。检索结果随后输入生成模型，生成转写、翻译及语境解释。实验评估表明，语义检索在精确度和相关性上显著优于关键词匹配；用户研究则证实生成式摘要有效提升了内容的可及性。据我们所知，这是首次将检索与生成技术相结合应用于梵语箴言研究的尝试，为文化遗产与现代应用人工智能搭建了桥梁。