wtf is rag-deepseek-ollama?

RAG-DeepSeek-Ollama is a tool that lets you ask questions about your own PDF documents and get answers back in plain language. Instead of scrolling through a long report or research paper to find a specific detail, you can upload the file and ask the system directly, and it will read the document and respond based on what is actually inside it. Under the hood, it uses an approach called Retrieval-Augmented Generation. When you upload a PDF, the system breaks the text down into smaller, manageable chunks and converts them into a format a computer can search through quickly. When you ask a question, it first finds the most relevant sections of your document, then passes those sections to a AI language model to generate a natural-sounding answer. The AI model used here is DeepSeek-R1, running locally through a tool called Ollama, and the interface for interacting with it is a simple web page built with Streamlit. This project would be useful for anyone who regularly works with lengthy documents and wants to extract information from them faster. For example, a student could use it to study a dense textbook chapter by asking it to summarize specific concepts. A founder or product manager could upload market research reports and ask questions about specific data points buried inside. Because the tool processes documents locally through Ollama, users can run the AI on their own machine rather than sending their private files to a third-party cloud service. To get started, you need a computer running Python and Ollama, which is the software that runs the AI model on your local machine. The README does not go into further detail about advanced configuration or how much computing power is required, but the core setup involves downloading the project and installing its required Python packages.