科研团队想用AI又不放心商业闭源？AquiLLM开源、用开放权重模型，还能本地跑嵌入和重排序，专为捕获隐性知识设计，值得看看。
AquiLLM是一个开源的模块化RAG-LLM框架，使用开放权重模型，旨在帮助研究团队捕获隐性知识。该框架针对专有商业AI系统在透明度、可复现性和隐私方面的不足而设计。最新版本引入了本地嵌入与重排序、多模态能力、OpenAI兼容推理接口、语义与情景记忆及技能支持等增强功能。这些改进基于与天体物理学家和环境研究者的讨论，使AI系统更贴合科研实践。
AquiLLM: An Architecture for Supporting Tacit Knowledge Capture in Research Groups
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.