Evaluating Rag–llm Toolsets For Chatbot Support and Product Recommendation In 3d Marketplaces: A State-of-The-Art Review

Recent advances in Large Language Models (LLMs) have significantly improved the capabilities of conversational agents in digital platforms. In particular, the integration of Retrieval-Augmented Generation (RAG) techniques has enabled chatbots to provide more accurate, context-aware, and up-to-date responses by leveraging external knowledge sources. These developments are especially relevant for emerging digital commerce environments such as 3D marketplaces, where users require interactive assistance not only for platform navigation and customer support but also for personalized product discovery. This paper presents a state-of-the-art review of RAG and LLM toolsets for the development of intelligent chatbots designed to support users in 3D marketplaces. The review focuses on current frameworks, architectures, and platforms that combine retrieval mechanisms with generative models to enhance conversational performance. Particular attention is given to toolsets that facilitate integration with product databases, knowledge bases, and recommendation systems.