Visual question answering (VQA) is a task that answers the questions by giving an image, and thus involves both language and vision methods to solve, which make the VQA tasks a frontier interdisciplinary field. In recent years, as the great progress made in simple question tasks (e.g. object recognition), researchers start to shift their interests to the questions that require knowledge and reasoning. Knowledge-based VQA requires answering questions with external knowledge in addition to the content of images. One dataset that is mostly used in evaluating knowledge-based VQA is OK-VQA, but it lacks a gold standard knowledge corpus for retrieval. Existing work leverages different knowledge bases (e.g., ConceptNet and Wikipedia) to obtain external knowledge. Because of varying knowledge bases, it is hard to fairly compare models' performance. To address this issue, this paper collects a natural language knowledge base that can be used for any question answering (QA) system. Moreover, a Visual Retriever-Reader pipeline is proposed to approach knowledge-based VQA, where the visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge. The retriever is constructed with two versions: term based retriever which uses best matching 25 (BM25), and neural based retriever where the latest dense passage retriever (DPR) is introduced. To encode the visual information, the image and caption are encoded separately in the two kinds of neural based retriever: Image-DPR and Caption-DPR. There are also two styles of readers, classification reader and extraction reader. Both the retriever and reader are trained with weak supervision. The experimental results show that a good retriever can significantly improve the reader's performance on the OK-VQA challenge.