PaperMind AI v2

A modern PDF document chat application powered by AI that allows you to upload PDF documents and have intelligent conversations about their content.

Features

• PDF Upload & Processing: Upload PDF documents and extract text content
• AI-Powered Chat: Chat with your documents using Google's Gemini AI
• Smart Context Retrieval: Uses Pinecone vector database for relevant context
• Real-time Streaming: Get AI responses in real-time with streaming
• Responsive Design: Modern, responsive UI that works on all devices
• Document Preview: Preview uploaded documents in a side panel
• Chat History: Maintains conversation history for each document

Tech Stack

Frontend

• React 18 with TypeScript
• Vite for fast development and building
• Tailwind CSS for styling
• React Dropzone for file uploads
• React Markdown for rich text rendering
• Axios for API communication

Backend

• Node.js with TypeScript
• Express.js for API server
• Google Gemini AI for text embeddings and chat completions
• Pinecone for vector database and similarity search
• PDF-parse for PDF text extraction
• Multer for file upload handling

Prerequisites

• Node.js 18+
• pnpm 9+
• Google Gemini API key
• Pinecone API key and index

Setup Instructions

1. Clone and Install Dependencies

git clone <repository-url>
cd papermind-ai-v2
pnpm install

2. Environment Configuration

Backend Environment

Copy the example environment file and configure your API keys:

cd backend
cp env.example .env

Edit .env and add your API keys:

PORT=3001
FRONTEND_URL=http://localhost:5173

# API Keys (Replace with your actual keys)
GEMINI_API_KEY=your_gemini_api_key_here
PINECONE_API_KEY=your_pinecone_api_key_here
PINECONE_INDEX=your_pinecone_index_name_here

Frontend Environment

cd frontend
cp env.example .env

Edit .env:

VITE_API_BASE_URL=http://localhost:3001

3. Pinecone Setup

1. Create a Pinecone account at pinecone.io
2. Create a new index with:
   • Dimensions: 768 (for text-embedding-004 model)
   • Metric: cosine
   • Cloud: Choose your preferred region
3. Copy your API key and index name to the backend .env file

4. Google Gemini Setup

1. Get your API key from Google AI Studio
2. Add it to the backend .env file

Development

Start Both Frontend and Backend

pnpm dev

Start Only Frontend

pnpm dev:frontend

Start Only Backend

pnpm dev:backend

Build for Production

pnpm build

API Endpoints

POST /api/upload

Upload and process a PDF document.

Request: Multipart form data with file field Response:

{
  "message": "File uploaded and processed successfully.",
  "document": {
    "id": "doc-1234567890",
    "name": "document.pdf"
  }
}

POST /api/chat

Chat with a document using streaming response.

Request:

{
  "message": "What is this document about?",
  "documentId": "doc-1234567890"
}

Response: Server-sent events stream with AI response chunks.

Project Structure

papermind-ai-v2/
├── frontend/                 # React frontend application
│   ├── src/
│   │   ├── components/       # React components
│   │   ├── context/         # React context providers
│   │   ├── hooks/           # Custom React hooks
│   │   ├── services/        # API and utility services
│   │   └── types.ts         # TypeScript type definitions
│   ├── package.json
│   └── vite.config.ts
├── backend/                  # Node.js backend API
│   ├── src/
│   │   ├── controllers/     # Request handlers
│   │   ├── middleware/      # Express middleware
│   │   ├── routes/          # API routes
│   │   ├── services/        # Business logic services
│   │   └── app.ts           # Express app setup
│   ├── package.json
│   └── tsconfig.json
└── package.json             # Root monorepo configuration

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License.

Support

For support and questions, please open an issue on GitHub.