AI PDF Reader

A local RAG (Retrieval-Augmented Generation) app that lets you upload PDFs and chat with them. Runs entirely on your machine using Ollama — no data sent to external servers.

How it works

1. Upload a PDF
2. The backend chunks the PDF and generates embeddings using nomic-embed-text
3. Embeddings are stored in a local ChromaDB vector database
4. When you ask a question, relevant chunks are retrieved and passed to mistral to generate an answer with source citations

Stack

• Frontend — React + Vite + Tailwind CSS
• Backend — Python + Flask
• Embeddings — Ollama (nomic-embed-text)
• LLM — Ollama (mistral)
• Vector DB — ChromaDB

Prerequisites

• Python 3.10+
• Node.js
• Ollama installed and running

Setup

1. Pull Ollama models

ollama pull mistral
ollama pull nomic-embed-text

2. Install Python dependencies

pip install flask flask-cors langchain langchain-ollama langchain-chroma langchain-community pypdf chromadb

3. Install frontend dependencies

cd frontend
npm install

Running the app

You need two terminals open at the same time.

Terminal 1 — Flask backend (from the project root):

python3 api.py

Terminal 2 — Vite frontend (from the frontend/ folder):

cd frontend
npm run dev

Then open http://localhost:5173 in your browser.

Usage

1. Drop a PDF into the sidebar or click to browse
2. Wait for indexing to complete (may take a moment depending on PDF size)
3. Ask questions about your document in the chat input
4. The AI will answer based on the document contents and cite the source chunks

Resetting the database

To clear all indexed documents:

python3 main.py --reset

Notes

• All processing is local — your PDFs never leave your machine
• Indexing and response speed depends on your hardware (Apple Silicon runs faster via Ollama's GPU support)
• Only PDF files are supported for upload