To leverage software repository data, which contains valuable information for understanding the software development process, this study integrated a knowledge graph with an LLM-based chatbot to improve the accuracy of repository-related questions. A two-step approach was used: building a knowledge graph from repository data and combining it with the LLM. The results showed that the LLM's inference capabilities led to errors in answering 150 questions of varying difficulty on five open source projects. However, applying few-shot chain-of-thought prompting improved accuracy to 84%. The results outperformed MSRBot and GPT-4o-search-preview, demonstrating its efficiency and usability in user studies.