Disrupt Your Research Using Generative AI Powered ScienceSage

TL;DR

ScienceSage is a web application that uses generative AI to help researchers build and query knowledge bases from diverse data sources, accelerating scientific discovery and innovation.

Contribution

This paper introduces ScienceSage, a novel MVP platform integrating generative AI with knowledge bases for enhanced research productivity and information retrieval.

Findings

Effective integration of vector and knowledge graph indices.

Supports multi-modal data extraction and querying.

Facilitates rapid research report generation and document interaction.

Abstract

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.