HSG-RAG: Hierarchical Knowledge Base Construction for Embedded System Development

Customization is a fundamental aspect of embedded system development, requiring developers to acquire extensive domain-specific knowledge from technical documents. However, the sheer volume of these documents, coupled with the intricate relationships between their contents, makes it challenging to efficiently retrieve the necessary information. This challenge highlights the need for a structured approach to organize domain knowledge, with explicit representation of interrelationships. Moreover, while advanced large language models (LLMs) show promise in aiding embedded system development, they often lack the specialized knowledge needed to address domain-specific queries effectively. In this article, we present HSG-RAG, a knowledge base construction and retrieval method tailored for embedded system development that leverages knowledge graphs to represent the hierarchical structure within technical documentation. Unlike prior retrieval-augmented generation (RAG) or GraphRAG-based approaches, which build the index either rely on semantic similarity or keyword co-occurrence, HSG-RAG captures the inherited dependency and hierarchical relationships in the documents, which benefits the retrieval in both performance and efficiency. We also introduce a benchmark for evaluating the effectiveness of RAG systems in solving real-world challenges in embedded systems, particularly for multi-hop question answering. Experimental results show that HSG-RAG outperforms both RAG and GraphRAG, generating more specific and concise responses.