Can LLMs improve CEP for video queries?
Large Language Model Based Multi-Agent System Augmented Complex Event Processing Pipeline for Internet of Multimedia Things
This paper explores using Large Language Models (LLMs) within a multi-agent system to improve complex event processing (CEP), specifically for video analysis. It presents a proof-of-concept using the Autogen framework and Kafka message broker.
Key LLM-MAS points include: different agent types (conversable, assistant, user proxy) within Autogen facilitate complex workflows; integrating external tools and functions enhances agent capabilities and system robustness; increasing agent numbers improves functionality but also increases latency due to communication overhead; video complexity impacts processing time and accuracy, with lower resolutions improving speed but reducing detail; and dynamic speaker selection within the multi-agent conversation is crucial for efficiency.