This tutorial outlines the process of building a Flask application that allows users to upload PDFs to a database, conduct vector similarity searches, and use OpenAI to answer questions about the documents. The application leverages several technologies, including Celery for background processing, RabbitMQ as a message broker, and SQLite for storing parsed text and embeddings. Users authenticate via Flask endpoints, upload PDFs that are processed by running OCR, and engage in similarity searches to find relevant documents. The application is designed with a job queue to handle time-intensive operations efficiently and uses Supervisor to ensure the app components remain operational. Although the app is a proof of concept with limitations like non-persistent storage and simple OCR parsing, it demonstrates potential for scalable AI-driven document management. Deployment requires a Ploomber Cloud account, an OpenAI key, and involves setting up resources for running the application, which can be accessed via a URL once deployed.