AquiLLM: a RAG Tool for Capturing Tacit Knowledge in Research Groups

TL;DR

AquiLLM is a modular RAG system designed to help research groups capture, store, and retrieve their informal and formal knowledge efficiently while respecting privacy concerns.

Contribution

The paper introduces AquiLLM, a novel lightweight, modular RAG tool tailored for research groups to manage diverse document types with configurable privacy features.

Findings

Supports various document types for comprehensive knowledge capture.

Enables privacy-aware retrieval of internal research materials.

Facilitates access to tacit knowledge within research teams.

Abstract

Research groups face persistent challenges in capturing, storing, and retrieving knowledge that is distributed across team members. Although structured data intended for analysis and publication is often well managed, much of a group's collective knowledge remains informal, fragmented, or undocumented--often passed down orally through meetings, mentoring, and day-to-day collaboration. This includes private resources such as emails, meeting notes, training materials, and ad hoc documentation. Together, these reflect the group's tacit knowledge--the informal, experience-based expertise that underlies much of their work. Accessing this knowledge can be difficult, requiring significant time and insider understanding. Retrieval-augmented generation (RAG) systems offer promising solutions by enabling users to query and generate responses grounded in relevant source material. However, most current RAG-LLM systems are oriented toward public documents and overlook the privacy concerns of internal research materials. We introduce AquiLLM (pronounced ah-quill-em), a lightweight, modular RAG system designed to meet the needs of research groups. AquiLLM supports varied document types and configurable privacy settings, enabling more effective access to both formal and informal knowledge within scholarly groups.