  In this work, we present HuixiangDou, a technical assistant powered by Large
Language Models (LLM). This system is designed to assist algorithm developers
by providing insightful responses to questions related to open-source algorithm
projects, such as computer vision and deep learning projects from OpenMMLab. We
further explore the integration of this assistant into the group chats of
instant messaging (IM) tools such as WeChat and Lark. Through several iterative
improvements and trials, we have developed a sophisticated technical chat
assistant capable of effectively answering users' technical questions without
causing message flooding. This paper's contributions include: 1) Designing an
algorithm pipeline specifically for group chat scenarios; 2) Verifying the
reliable performance of text2vec in task rejection; 3) Identifying three
critical requirements for LLMs in technical-assistant-like products, namely
scoring ability, In-Context Learning (ICL), and Long Context. We have made the
source code, android app and web service available at Github
(https://github.com/internlm/huixiangdou), OpenXLab
(https://openxlab.org.cn/apps/detail/tpoisonooo/huixiangdou-web) and YouTube
(https://youtu.be/ylXrT-Tei-Y) to aid in future research and application.
HuixiangDou is applicable to any group chat within IM tools.
