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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.18479 |
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Table of Contents:
- Large Language Models (LLM) are disrupting science and research in different subjects and industries. Here we report a minimum-viable-product (MVP) web application called $\textbf{ScienceSage}$. It leverages generative artificial intelligence (GenAI) to help researchers disrupt the speed, magnitude and scope of product innovation. $\textbf{ScienceSage}$ enables researchers to build, store, update and query a knowledge base (KB). A KB codifies user's knowledge/information of a given domain in both vector index and knowledge graph (KG) index for efficient information retrieval and query. The knowledge/information can be extracted from user's textual documents, images, videos, audios and/or the research reports generated based on a research question and the latest relevant information on internet. The same set of KBs interconnect three functions on $\textbf{ScienceSage}$: 'Generate Research Report', 'Chat With Your Documents' and 'Chat With Anything'. We share our learning to encourage discussion and improvement of GenAI's role in scientific research.