开源AI框架AquiLLM助科研组捕获隐性知识
AquiLLM: An Architecture for Supporting Tacit Knowledge Capture in Research Groups
- 基于开源模型构建模块化RAG架构，支持本地部署
- 集成多模态与记忆功能，提升知识捕捉能力
- 适合注重隐私与可复现性的科研团队使用
近年来，检索增强生成（RAG）与大语言模型（LLM）的进步使研究人员能够将AI融入科学工作流。然而，使用专有的商业AI系统会引发透明度、可复现性和隐私方面的担忧，这些对科学实践至关重要。为此，我们开发了AquiLLM——一个基于开源权重模型的开放源码模块化RAG-LLM框架，旨在帮助研究组捕获隐性知识。本文提出一系列架构改进与功能增强，包括本地嵌入与重排序、多模态能力、兼容OpenAI的推理接口、用户界面优化、语义与情景记忆功能以及技能支持。这些改进基于与天体物理学家和环境研究者等领域专家的讨论，推动AI系统更贴近科学研究实践。
原文摘要 · Abstract (English)
Recent advances in retrieval-augmented generation (RAG) and large language models (LLMs) enable researchers to integrate AI into scientific workflows. However, using proprietary commercial AI systems raises concerns about transparency, reproducibility and privacy, which are essential for scientific practices. To this end, AquiLLM was developed as an open-source modular RAG-LLM framework using open-weight models, designed to support research groups in capturing tacit knowledge. In this work, we present a series of architectural improvements and feature enhancements to AquiLLM, including local embedding and reranking, multimodal capabilities, OpenAI-compatible inference interfaces, user interface improvements, semantic and episodic memory capabilities, and skills support. These enhancements were informed by discussions with domain experts, including astrophysicists and environmental researchers, and represent a step toward AI systems more closely aligned with scientific research practices.
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