Retail-GPT: leveraging Retrieval Augmented Generation (RAG) for building E-commerce Chat Assistants

Large Language Models (LLMs), especially following the release of OpenAI's GPT series, have significantly disrupted textual human-machine interaction. They have enabled the development of chat assistants--also known as chatbots--that engage in more natural conversations and better understand users' needs. When combined with Retrieval-Augmented Generation (RAG) [1] techniques, these models can interact with other software systems and expand the information encoded in their parameters with data retrieved from external sources. Some examples described in the literature include FACTS [2], NVIDIA's framework for building assistants that leverage enterprise data for enhancing employee productivity and Abbasian et al. [3] health agents, focused on assisting users with healthcare-related tasks. Another possible domain for such systems is online shopping and delivery services. With estimated global retail e-commerce sales surpassing 6.3 trillion US dollars in 2024 [4], developing alternatives to enhance the customer experience in online purchases holds significant commercial value. In this context, this work describes Retail-GPT, an original open-source RAG-based chatbot designed to guide users through product recommendations and assist with cart operations, aiming to enhance user engagement with retail e-commerce and serve as a virtual sales agent. The goal of this system is to test the viability of such an assistant and provide an adaptable approach for implementing sales chatbots across different retail businesses.