Classroom PDF Question Answering Tool With Retrieval-Augmented Search

Added Jun 2025 3 design docs

Course materials in schools and universities overwhelmingly live in PDFs, including lecture slides, scanned readings, lab manuals, and past papers, and students interact with them in the least efficient way possible: scrolling and skimming. When revision time is short, the question is rarely whether the answer exists in the document but where, and educators equally need quick ways to pull content from their own teaching materials. The intern builds a question answering tool for the education sector that solves this directly. Through a Python Flask application, users upload PDFs and ask questions in plain language. The backend implements a retrieval-augmented generation pipeline with LangChain: documents are split into chunks, embedded, and stored for similarity search, and when a question arrives the most relevant chunks are retrieved and passed to OpenAI models to generate an answer grounded in the document, with the supporting passages displayed so students can read the original context. The intern builds the upload flow, the chunking and retrieval configuration, and the answer presentation that encourages verification rather than blind trust. The project teaches the canonical RAG architecture end to end, covering document processing, embeddings, vector search, and grounded generation, packaged in a Flask application with an obvious daily use case.

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