Exam_tutor

Aryan Raj — Sun, 04 Oct 2026 15:09:25 +0000

What I Built

A local exam-prep tutor for my friend Nishant. He [his real problem, e.g. "studies from scattered notes and web pages and has no way to test himself"] before [course/exam]. Paste URLs or upload PDFs, then either ask questions (answered only from his notes, with sources shown) or get quizzed and graded on his own answers.

Demo

[video link]

Code

Aryan1092raj / Exam_tutor

Exam Tutor

Local RAG study partner built for my friend Nishant. Paste URLs or upload notes, then ask questions or get quizzed. Everything runs on-device with open-weight models via Ollama. No API key, no internet needed after setup, notes never leave the laptop.

Setup

# 1. install Ollama: https://ollama.com
ollama pull qwen2.5:3b
ollama pull nomic-embed-text

# 2. run
pip install -r requirements.txt
streamlit run app.py

Use

Sidebar: paste URLs (web pages or PDFs) or upload files, click Ingest.
Ask mode: question gets answered only from the ingested notes, with sources shown.
Quiz me mode: enter a topic, get an exam-style question, answer it, get graded.

Sample sources to try (data structures):

https://en.wikipedia.org/wiki/Binary_search_tree
https://en.wikipedia.org/wiki/AVL_tree
https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm

How it works

Chunk text (900 chars, 150 overlap)
Embed chunks with nomic-embed-text (Ollama)
Cosine similarity over numpy matrix, top 4 chunks
qwen2.5:3b answers using only retrieved chunks
Swap model: edit CHAT_MODEL in app.py

Stack

…

View on GitHub

How I Built It

Chat model: Qwen2.5 3B via Ollama, runs on a laptop CPU/GPU.
Embeddings: nomic-embed-text via Ollama.
Retrieval: notes chunked (900 chars, 150 overlap), embedded, cosine similarity over a numpy matrix, top 4 chunks fed to the model.
Grounding: system prompt restricts answers to retrieved notes; if notes don't cover it, it says so.
Quiz mode: model writes one exam-style question from retrieved chunks, then grades the student's answer against them.
UI: Streamlit. Ingestion via trafilatura (web) and pypdf (PDFs).
Whole thing is ~150 lines, one file.

Why Does Open Innovation Matter?

Offline: works with bad hostel wifi. After model pull, zero internet needed.
Private: his notes never leave his laptop.
Free: no API key, no per-query cost, no rate limit during exam week.
Swappable: changing the model is one line (CHAT_MODEL). [Add your real 3B vs 7B comparison, or delete this sentence.]
A closed API would need billing, a key, and a connection, which is exactly what a student doesn't want the night before an exam.

DEV Community: Aryan Raj