This paper presents the development and evaluation of a Large Language Model (LLM), also known as foundation models, based multi-agent system framework for complex event processing (CEP) with a focus on video query processing use cases. The primary goal is to create a proof-of-concept (POC) that integrates state-of-the-art LLM orchestration frameworks with publish/subscribe (pub/sub) tools to address the integration of LLMs with current CEP systems. Utilizing the Autogen framework in conjunction with Kafka message brokers, the system demonstrates an autonomous CEP pipeline capable of handling complex workflows. Extensive experiments evaluate the system's performance across varying configurations, complexities, and video resolutions, revealing the trade-offs between functionality and latency. The results show that while higher agent count and video complexities increase latency, the system maintains high consistency in narrative coherence. This research builds upon and contributes to, existing novel approaches to distributed AI systems, offering detailed insights into integrating such systems into existing infrastructures.

本研究针对传统复杂事件处理（CEP）系统与大型语言模型（LLM）集成的不足，提出了一种基于LLM的多智能体系统框架，专注于视频查询处理应用。通过结合Autogen框架和Kafka消息代理，该系统构建了一个自主的CEP管道，显著提高了处理复杂工作流的能力，实验证明了其在不同配置和视频分辨率下的性能优越性。

基于大型语言模型的多智能体系统增强的复杂事件处理管道用于多媒体互联网