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.

翻译：本文提出并评估了一种基于大语言模型（亦称基础模型）的多智能体系统框架，用于复杂事件处理，并聚焦于视频查询处理用例。其主要目标是创建一个概念验证，将先进的大语言模型编排框架与发布/订阅工具相结合，以解决大语言模型与现有复杂事件处理系统的集成问题。该系统利用Autogen框架与Kafka消息代理协同工作，展示了一个能够处理复杂工作流的自主复杂事件处理流水线。通过大量实验，评估了系统在不同配置、复杂度和视频分辨率下的性能，揭示了功能与延迟之间的权衡关系。结果表明，尽管更高的智能体数量和视频复杂度会增加延迟，但系统在叙事连贯性方面保持了高度一致性。本研究基于并贡献于分布式人工智能系统的新颖方法，为将此类系统集成到现有基础设施中提供了详细的见解。