🎓 AI Study Assistant

A fully local, AI-powered study companion. Upload your PDF study materials and get an AI chatbot, auto-generated summaries, key points, and practice quizzes — all powered by a local Ollama installation. No OpenAI key, no Pinecone account, no paid API of any kind is required.

Features

1. Upload one or more PDF study materials.
2. Automatic text extraction and chunking of the documents.
3. Local vector embeddings via Ollama's nomic-embed-text model.
4. Fast semantic search over your material using FAISS.
5. AI chatbot (RAG) that answers questions using your uploaded material.
6. One-click AI-generated summary of the material.
7. One-click AI-generated key points list.
8. AI-generated multiple-choice quiz from your material.
9. Instant quiz scoring with correct-answer review.
10. Quiz history and scores saved permanently in a local SQLite database.
11. Simple, student-friendly dashboard (documents uploaded, quizzes taken, average score, best score, recent history).
12. Modern, responsive, sidebar-navigation UI.
13. Clear error handling and loading indicators throughout.

Technologies Used

• Python 3.12
• Flask
• Ollama — local LLM inference
  • llama3.2 for chat, summaries, key points, and quiz generation
  • nomic-embed-text for embeddings
• FAISS (faiss-cpu) for vector similarity search
• pypdf for PDF text extraction
• SQLite (built into Python) for quiz history storage
• HTML5 / CSS3 / vanilla JavaScript

Folder Structure

04_ai_study_assistant/
│
├── app.py                 # Main Flask application (all routes)
├── database.py             # SQLite setup, quiz history, dashboard stats
├── vector_store.py          # FAISS vector store + Ollama embeddings
├── requirements.txt
├── README.md
├── .gitignore
├── .env.example
│
├── uploads/                # Temporary storage for uploaded PDFs (auto-cleared)
│   └── .gitkeep
│
├── data/                    # SQLite database file lives here
│   └── .gitkeep
│
├── templates/
│   └── index.html
│
└── static/
    ├── style.css
    └── script.js

Prerequisites

• Python 3.12 (recommended; the packages in requirements.txt are chosen for Python 3.12 compatibility on Windows, macOS, and Linux).
• Ollama installed and running locally: https://ollama.com/download

Installation (Windows PowerShell)

1. Open PowerShell in the project folder:

   cd 04_ai_study_assistant
   

2. Create a virtual environment:

   python -m venv venv
   

3. Activate it:

   venv\Scripts\Activate.ps1
   

   If you get an execution-policy error, run PowerShell as Administrator and execute: Set-ExecutionPolicy -Scope CurrentUser RemoteSigned

4. Install dependencies:

   pip install -r requirements.txt
   

5. Pull the required Ollama models (skip any you already have installed):

   ollama pull llama3.2
   ollama pull nomic-embed-text
   

   Make sure Ollama is running — it typically runs automatically in the background after installation. If not, run ollama serve in a separate terminal window.

6. (Optional) Configure environment variables:

   copy .env.example .env
   

   Edit .env if you want to change the model names, Ollama host, or the Flask secret key.

Running the Application

python app.py

Then open your browser at: http://localhost:5004

macOS / Linux

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
ollama pull llama3.2
ollama pull nomic-embed-text
python app.py

How to Use

1. Open the app and go to the Upload Material tab.
2. Upload one or more PDF study documents.
3. Go to Ask Questions to chat with an AI about your material.
4. Go to Summary to generate a quick review summary.
5. Go to Key Points to get an extracted bullet list of important concepts.
6. Go to Quiz Me, choose how many questions you want, and generate a quiz.
7. Answer the quiz and click Submit Quiz to see your score.
8. Check the Dashboard tab to see your quiz history and stats over time.

Database

The SQLite database file (data/study_assistant.db) is created automatically the first time you run the app — no manual setup needed. It stores:

• documents — metadata about uploaded files.
• quiz_history — every quiz attempt with score and timestamp.
• quiz_questions — the individual questions/answers for each attempt.

Troubleshooting

| Problem | Solution | |---|---| | Could not connect to Ollama | Make sure Ollama is installed and running. Run ollama serve in a terminal, then retry. | | Errors mentioning nomic-embed-text | Run ollama pull nomic-embed-text to install the embedding model. | | Errors mentioning llama3.2 | Run ollama pull llama3.2 to install the chat/generation model. | | Quiz or summary generation is slow | Large PDFs and larger models take longer. Try a smaller PDF, fewer questions, or a smaller model. | | "Could not extract text from ..." | The PDF is likely a scanned image without selectable text. Use a text-based PDF, or OCR it first. | | Port 5004 already in use | Edit the last line of app.py and change port=5004 to a free port. | | faiss-cpu fails to install | Make sure you're using Python 3.12 (not 3.13/3.14) and the latest pip (python -m pip install --upgrade pip). | | Quiz history not showing up | Make sure the data/ folder is writable; the app creates study_assistant.db there automatically. |

Notes

• Everything runs 100% locally through Ollama — no API keys, no cloud costs.
• Uploaded PDFs are deleted from the server immediately after their text is extracted; only the extracted text (as vector embeddings) is kept in memory for the current server session.
• The vector index is in-memory and rebuilt each time you restart the server — re-upload your material after a restart if you want to keep asking questions or generating quizzes about it. Quiz history in SQLite, however, is permanent.