Non-factoid question answering (NFQA) poses a significant challenge due to its open-ended nature, diverse intents, and the necessity for multi-aspect reasoning, rendering conventional retrieval-augmented generation (RAG) approaches insufficient. To address this, we introduce Typed-RAG, a type-aware framework utilizing multi-aspect query decomposition tailored specifically for NFQA. Typed-RAG categorizes NFQs into distinct types—such as debate, experience, and comparison—and decomposes them into single-aspect sub-queries for targeted retrieval and generation. By synthesizing the retrieved results of these sub-queries, Typed-RAG generates more informative and contextually relevant responses. Additionally, we present Wiki-NFQA, a novel benchmark dataset encompassing diverse NFQ types. Experimental evaluation demonstrates that TypeRAG consistently outperforms baseline approaches, confirming the effectiveness of type-aware decomposition in improving both retrieval quality and answer generation for NFQA tasks.