Question Answering is one of the challenging points of research in natural language processing recently. The problem of automating the answering process for the user's queries became required. So, there were several papers suggested different system architectures for building a question answering systems. In this research paper, we suggest our own system architecture taking into consideration that the input of the system architecture is only the asked question. The suggested system architecture is a long-form open domain question answering that contains mainly two layers. The natural language processing layer which holds the data module and the computing module. This layer is responsible for many operations like pre-processing, preparing, storing the data along with taking the user's question then providing the suitable answer. The dataset of the proposed system has to be documents annotated with questions and answers extracted from these documents. Also, it has to be in SQUAD format. The computing module is a retriever-reader based deep learning model. This model achieves scores: 67% Recall@100 using dense passage retriever model and 67.7% F1 score for reader model. the Interface layer is the second layer which includes the APIs module and the user-interface module. Finally, we will discuss a real time case study for the system.