Open repository projects serve as valuable resources for scholarly communication and knowledge dissemination. However, interacting with these repositories can be challenging due to the vast amount of data, the restriction of browsing features, and the limitation of relational database queries. This paper presents a technical procedure and coding details that leverage Retrieval-augmented Generation (RAG), Large Language Models (LLMs), embeddings, and LangChain to develop an AI-enhanced conversation application tailored for communication with open repository systems, specifically focusing on DSpace. The chatbot uses RAG to enhance response generation by integrating relevant information retrieved from repository structural data and text documents. LLMs form the chatbot’s core generation capabilities, ensuring coherent and contextually appropriate interactions. Embeddings are used to semantically enrich queries and responses, thereby enhancing understanding and relevance. LangChain coordinates among these components, managing the information flow and interaction between users and repositories.