📄 PDReader - AI-Powered PDF Q&A

Upload any PDF and chat with it using AI. PDReader uses Retrieval-Augmented Generation (RAG) to understand your documents and answer questions accurately.

✨ Features

• 📤 Drag & Drop Upload - Simply drop your PDFs into the interface
• 🔄 Smart Processing - Automatically extracts text, chunks content, and creates embeddings
• 💬 Natural Chat - Ask questions in plain English and get relevant answers
• 📚 Multi-Document Support - Chat with multiple PDFs at once
• 🔍 Source Citations - See exactly which parts of the document the answer came from
• 🗃️ Document Management - View, delete, and manage your uploaded documents
• 💾 Persistent Storage - Documents and their vector stores are saved locally
• 🔐 Privacy-First - Your documents stay on your machine

🛠️ Tech Stack

Backend

Technology	Purpose
FastAPI	High-performance API framework
LangChain	LLM framework & document processing
FAISS	Vector similarity search
OpenAI	GPT models for embeddings & chat
PyPDF	PDF text extraction

Frontend

Technology	Purpose
React	UI framework
TypeScript	Type safety
Tailwind CSS	Styling
Vite	Build tool
Lucide React	Icons

🏗️ System Design

┌─────────────────────────────────────────────────────────────────────────────┐
│                              Frontend (React + Vite)                        │
│                                                                             │
│   Document Upload ─────► Chat Interface ─────► Source Citations             │
│   (Drag & Drop)          (Real-time Chat)     (Page + Chunk refs)           │
└─────────────────────────────────────────────────────────────────────────────┘
                                      │
                                      │ HTTP
                                      ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                              Backend (FastAPI)                              │
│                                                                             │
│   ┌─────────────┐        ┌─────────────┐     ┌────────────────────────┐     │
│   │  Documents  │        │    Chat     │     │       Health           │     │
│   │   Router    │        │   Router    │     │       Router           │     │
│   │  (CRUD ops) │        │  (Q&A)      │     │    (Status check)      │     │
│   └──────┬──────┘        └──────┬──────┘     └────────────┬───────────┘     │
│          │                      │                         │                 |
│          └──────────────────────┼─────────────────────────┘                 | 
│                                 │                                           │
│   ┌─────────────────────────────┼─────────────────────────────────────────┐ │
│   │                     Services Layer                                    │ │
│   │                                                                       │ │
│   │   ┌──────────────┐  ┌──────────────┐  ┌─────────────────────────┐     │ │
│   │   │   PDF        │  │   Vector     │  │        LLM              │     │ │
│   │   │  Processing  │  │   Search     │  │      Service            │     │ │
│   │   │  (PyPDF +    │  │  (FAISS +    │  │  (GPT-3.5-turbo)        │     │ │
│   │   │  LangChain)  │  │  OpenAI      │  │                         │     │ │
│   │   │              │  │  Embeddings) │  │                         │     │ │
│   │   └──────────────┘  └──────────────┘  └─────────────────────────┘     │ │
│   └───────────────────────────────────────────────────────────────────────┘ │
│                               │                                             │
└───────────────────────────────┼─────────────────────────────────────────────┘
                                │
          ┌─────────────────────┼─────────────────────┐
          │                     │                     │
          ▼                     ▼                     ▼
   ┌─────────────┐       ┌─────────────┐       ┌─────────────┐
   │   Local     │       │   FAISS     │       │   OpenAI    │
   │   File      │       │   Vector    │       │    API      │
   │   System    │       │   Store     │       │             │
   │  (PDFs +    │       │  (Local)    │       │             │
   │   JSON)     │       │             │       │             │
   └─────────────┘       └─────────────┘       └─────────────┘

🚀 Getting Started

Prerequisites

• Python 3.11+
• Node.js 18+
• OpenAI API Key (Get one here)

Installation

1. Clone the repository

git clone https://github.com/yourusername/PDReader.git
cd PDReader

2. Backend Setup

cd backend

# Create virtual environment
python -m venv venv

# Activate (Windows)
venv\Scripts\activate

# Activate (Mac/Linux)
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

3. Configure API Key

# Copy the example env file
cp .env.example .env

# Edit .env and add your OpenAI API key
OPENAI_API_KEY=sk-your-api-key-here

4. Start Backend

uvicorn main:app --reload --port 8000

5. Frontend Setup (in a new terminal)

cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

🎉 Usage

1. Open http://localhost:5173 in your browser
2. Upload a PDF using the drag & drop zone or file picker
3. Wait for the document status to show "ready" (processing happens automatically)
4. Ask questions about your document in the chat box
5. View source citations to see where answers came from

📡 API Endpoints

Method	Endpoint	Description
GET	/health	Health check
POST	/api/documents/upload	Upload PDF(s)
GET	/api/documents	List all documents
GET	/api/documents/{id}	Get document details
DELETE	/api/documents/{id}	Delete a document
DELETE	/api/documents	Delete all documents
POST	/api/chat	Ask a question

Example: Chat Request

POST /api/chat
{
  "query": "What is this document about?",
  "document_ids": ["doc-uuid-1", "doc-uuid-2"]
}

Example Response

{
  "answer": "This document is an annual report...",
  "sources": [
    {
      "document_id": "doc-uuid-1",
      "filename": "report.pdf",
      "chunk_text": "Annual Report 2024...",
      "page": 1
    }
  ],
  "model": "gpt-3.5-turbo"
}

📁 Project Structure

PDReader/
├── backend/
│   ├── main.py          # FastAPI application & routes
│   ├── services.py      # PDF processing & LLM logic
│   ├── schemas.py       # Pydantic models
│   ├── requirements.txt # Python dependencies
│   └── .env             # Environment variables
├── frontend/
│   ├── src/
│   │   ├── App.tsx      # Main React component
│   │   ├── api.ts       # API client functions
│   │   ├── types.ts     # TypeScript types
│   │   └── index.css    # Global styles
│   ├── package.json     # Node dependencies
│   └── vite.config.ts   # Vite configuration
└── README.md

⚙️ Configuration

Customize behavior by editing backend/services.py:

Variable	Default	Description
CHUNK_SIZE	500	Text chunk size for embeddings
CHUNK_OVERLAP	50	Overlap between chunks
TOP_K	4	Number of documents to retrieve
OPENAI_MODEL	gpt-3.5-turbo	LLM model to use

🙏 Acknowledgments

• LangChain for the amazing RAG abstractions
• FAISS for efficient similarity search
• OpenAI for the LLM capabilities