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This article presents the architecture and functionality of a decision support system module designed for assessing climate risks of a region, based on a Large Language Model (LLM) enhanced with Retrieval-Augmented Generation (RAG) technology. In particular, the following are discussed: the RAG-based architecture, which serves as the system's primary component, the database required to store the information utilized by the language model and the user-friendly web interface. The functional capabilities of the system are illustrated and described via a use case diagram. The article also includes a quantitative and qualitative evaluation of the system's performance to generate accurate and relevant responses based on the content of the documents.