src/README.md

⚡ AI PDF Review Assistant

   ___  _     ____  ____  _____     __   __  
  / _ \| |   |  _ \|  _ \| ____|   / /   \ \ 
 | | | | |   | |_) | |_) |  _|    / /     \ \
 | |_| | |___| __/|  _ <| |___   / /       \ \
  \__\_\_____|_|   |_| \_\_____|/_/  REVIEW  \_\

"Knowledge locked in pages... until now."

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🌸 What is this?

AI PDF Review Assistant is an intelligent document analysis system powered by Google Gemini and a custom RAG (Retrieval-Augmented Generation) pipeline. Feed it your PDFs — it reads them, understands them, and answers your questions like a brilliant study partner who never sleeps.

Ask in Bengali or English. Get answers with full LaTeX math rendering. It remembers the conversation. It finds exactly what you need.

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⚔️ The Pipeline — How It Works

  [ Your PDF ]
       │
       ▼
  ┌─────────────────────────────┐
  │  PyMuPDF converts each      │
  │  page → image               │
  └────────────┬────────────────┘
               │
               ▼
  ┌─────────────────────────────┐
  │  Gemini Vision reads every  │
  │  image — Bengali, math,     │
  │  diagrams, handwriting      │
  └────────────┬────────────────┘
               │
               ▼
  ┌─────────────────────────────┐
  │  Gemini Embedding converts  │
  │  text → vectors (3072D)     │
  └────────────┬────────────────┘
               │
               ▼
  ┌─────────────────────────────┐
  │  ChromaDB stores vectors    │
  │  on disk permanently        │
  └────────────┬────────────────┘
               │
       [ You ask a question ]
               │
               ▼
  ┌─────────────────────────────┐
  │  Your question → embedded   │
  │  → ChromaDB finds closest   │
  │  matching pages             │
  └────────────┬────────────────┘
               │
               ▼
  ┌─────────────────────────────┐
  │  Gemini answers using only  │
  │  YOUR notes as context      │
  └─────────────────────────────┘

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🚀 How to Use

1. Create a .env file in the project root:

GEMINI_API_KEY=your_api_key_here

2. Get your free Gemini API key from: https://aistudio.google.com/apikey

3. Create and activate a virtual environment:

# Windows
python -m venv venv
venv\Scripts\activate

# Mac/Linux
python -m venv venv
source venv/bin/activate

4. Install all dependencies:

pip install -r requirements.txt

5. Launch the app:

streamlit run app.py

6. Use the Extractor first from the sidebar to process your PDFs before asking questions.

7. Done — start asking! ⚡

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📁 Project Structure

ai-pdf-review-assistant/
│
├── app.py               # Streamlit UI
├── extract.py           # Image → text extraction via Gemini
├── embed.py             # Text → vector embeddings
├── store.py             # Store vectors in ChromaDB
├── query.py             # Question answering pipeline
│
├── data/
│   └── vector_db/       # ChromaDB persistent storage
│
├── extracted_texts.json
├── embeddings.json
├── .env                 # Your API key goes here
├── requirements.txt
└── README.md

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🗡️ Tech Stack

Language      →  Python
UI            →  Streamlit
Vector DB     →  ChromaDB
PDF Parser    →  PyMuPDF (fitz)
AI Model      →  Google Gemini (vision + embedding + generation)

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📜 License

Copyright 2026 Renerfia

Licensed under the Apache License, Version 2.0

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Built with obsession. Powered by Gemini. Forged in Python.

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