Automatic FAQ: Building a Multi-Agent Systems to Extract Insight from User Discussions

For many products, especially software, online user discussion provides a valuable trove of data for improvement. Manually reading all discussion and extracting key themes and insights is, however, typically prohibitive in terms of human labor. In this presentation, we develop a multi-agent system built with open-source tooling that is capable of summarizing large corpora of user discussion from Discord, GitHub, and other sources, extracting insights on common themes across users in order to improve product usability and documentation. We construct the system with Milvus, HuggingFace, and LangChain libraries, and demonstrate its usefulness on analyzing the Milvus user documentation.

Stefan Webb

Zilliz

Stefan Webb is a Developer Advocate at Zilliz, where he advocates for the open-source vector database, Milvus. Prior to this, he spent three years in the industry as an Applied ML Researcher at Twitter and Meta, collaborating with product teams to tackle their most complex challenges. Stefan holds a PhD from the University of Oxford and has published papers at leading machine learning conferences such as NeurIPS, ICLR, and ICML. He is passionate about generative AI and is eager to leverage his deep technical expertise to contribute to the open-source community.