The New Era of Knowledge Retrieval: Multi-Agent Systems Meet Generative AI

In the realm of interactive chat systems, the fusion of Multi-Agent Systems (MAS) with Generative Artificial Intelligence (GAl) presents a promising approach to dynamic information retrieval and personalized user experiences. Handling diverse data sources with distinct modalities, especially in real-time, poses challenges. This paper provides a comprehensive overview of MAS and GAl, emphasizing their synergistic potential in complex real-time searches. A notable contribution is an experimental prototype adept at navigating real-time data sources beyond the AI's training data, enhancing the user's information-seeking experience. By integrating MAS adaptability with GAl's data-processing capabilities, our approach delivers valuable real-time insights. The exploration of knowledge graphs based on acquired data further enriches the system. However, inherent limitations include scalability challenges, data integrity maintenance, and the refinement of the user experience. Addressing these challenges lays the foundation for future research in the exciting intersection of MAS and GAl, offering insights into the potential of this combined approach in advancing interactive chat systems.