An Intelligent Multi-Agent RAG System for Attributed Question Answering

Multi-agent systems (MAS) offer an efficient design pattern for tackling complex distributed issues by utilising numerous independent agents that work together towards a shared goal. In this research work, the proposed MAS approach for attributed question answering (QA) comprises intelligent agents working in tandem to complete both retrieval and generation tasks to generate precise, trustworthy, and contextually relevant responses. The framework maximises answer accuracy, measured by coverage and relevance, and answer faithfulness, which is a metric that quantifies how strongly answers are anchored to the retrieved documents. The use of a mixed retrieval technique incorporating both sparse (BM25) and dense (E5) approaches enhances recall rates relative to other baseline modelsthat utilise only onetype ofretrieval model.Further, the solution involves a dual LLM setup giving users the freedom to either select cloud-based (OpenAI GPT) or on- premise (Llama) inference services, thereby resolving privacy issues.