Upload-and-Ask PDF Reader for Study and Legal Documents

Added Jun 2025 3 design docs

Whether it is a landlord agreement, a course syllabus, or a fifty-page reading assignment, most people interrogate PDFs the same way: scroll, squint, and hope the relevant paragraph jumps out. Students and people facing legal documents feel this hardest, because the answer they need is usually present but buried in language designed for experts. Letting a user upload the document and simply ask their question closes that gap.

The intern builds this tool as a Streamlit application in Python. A user uploads a PDF and types questions in plain language; behind the interface, LangChain's PDF loader extracts the document text, which is split into chunks and embedded for retrieval. Each question triggers a retrieval-augmented generation flow: the most relevant chunks are found by similarity search and passed to the language model, which composes an answer grounded in the document, with the supporting excerpts shown so users can confirm the answer against the original text. The intern implements the upload handling, the loader and splitter configuration, and the question-answer interface with source display.

The project teaches the complete RAG pattern at an approachable scale, covering document loading, chunking, embeddings, retrieval, and grounded generation, and leaves the intern with both a genuinely useful study tool and the architecture underlying most commercial document AI.

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