Skip to content

README.md

Latest commit

 

History

History
196 lines (146 loc) · 4.55 KB

README.md

File metadata and controls

196 lines (146 loc) · 4.55 KB

AI Profile Assistant

A React + Python personal homepage AI assistant project where users can learn about your background, skills, project experience, and more through a chat interface.

Features

  • 🤖 Intelligent AI assistant powered by OpenAI GPT-4
  • 🎨 Modern UI design with dark theme support
  • 📱 Responsive design for mobile devices
  • 🔄 Frontend-backend separation architecture
  • 📄 Configurable personal data
  • 🌐 GitHub Pages deployment support
  • 🧠 True RAG (Retrieval-Augmented Generation) system

Project Structure

├── src/                     # Frontend React application
│   ├── components/         # React components
│   ├── pages/             # Page components
│   ├── utils/             # Utility functions
│   └── ...
├── backend/                # Backend Python API
│   ├── app.py             # Flask application
│   ├── rag_system.py      # RAG system module
│   ├── personal_data.json # Personal data
│   ├── requirements.txt   # Python dependencies
│   └── ...
├── .github/workflows/     # GitHub Actions
└── README.md

Quick Start

1. Clone the project

git clone <your-repo-url>
cd ai-profile-assistant

2. Configure personal data

Edit the backend/personal_data.json file with your personal information:

{
  "basic": {
    "name": "Your Name",
    "title": "Your Professional Title",
    "email": "your.email@example.com",
    "location": "Your Location",
    "summary": "Your professional summary"
  },
  "skills": {
    "programmingLanguages": ["Python", "JavaScript"],
    "mlFrameworks": ["TensorFlow", "PyTorch"],
    // ... more information
  }
  // ... other information
}

3. Set up backend

Option A: Local Development

  1. Navigate to the backend directory:
cd backend
  1. Install dependencies:
pip install -r requirements.txt
  1. Set up environment variables:
cp env.example .env
# Edit .env and add your OpenAI API key
  1. Run the backend server:
python app.py

The server will start at http://localhost:5001

4. Configure frontend

  1. Copy env.example to .env:
cp env.example .env
  1. Set the backend API address:
VITE_API_BASE_URL=http://localhost:5001

5. Local development

# Install frontend dependencies
npm install

# Start frontend development server
npm run dev

6. Deploy to GitHub Pages

  1. Add secrets to your GitHub repository:

    • VITE_API_BASE_URL: Your backend API address

  2. Push code to the main branch - GitHub Actions will automatically deploy

  3. Enable GitHub Pages in repository settings, select the gh-pages branch

Configuration

Environment Variables

  • OPENAI_API_KEY: OpenAI API key (required for backend)
  • VITE_API_BASE_URL: Backend API address (required for frontend)

Personal Data Configuration

The backend/personal_data.json file contains the following sections:

  • basic: Basic information (name, title, email, etc.)
  • skills: Skills list
  • experience: Work experience
  • projects: Project experience
  • education: Education background
  • certifications: Certifications
  • interests: Interests and hobbies
  • careerGoals: Career objectives

API Endpoints

POST /api/chat

Send a message to the AI assistant

GET /api/health

Health check endpoint

POST /api/rebuild-vectorstore

Rebuild the vector database (use after updating personal data)

RAG System

This project implements a true RAG (Retrieval-Augmented Generation) system:

  1. Document Processing: Converts personal data to structured documents
  2. Text Chunking: Splits documents into manageable chunks
  3. Vectorization: Uses OpenAI embeddings to convert text to vectors
  4. Storage: Stores vectors in ChromaDB
  5. Retrieval: Searches for most relevant document chunks based on user queries
  6. Generation: Passes retrieved context to AI for response generation

RAG Advantages

  • Precise Retrieval: Only retrieves information relevant to the question
  • Reduced Hallucination: Based on retrieved real information
  • Scalability: Supports large documents and complex queries
  • Efficiency: Avoids sending all information to AI
  • Cost Optimization: Reduces token usage

Technology Stack

Frontend

  • React 18
  • TypeScript
  • Vite
  • Tailwind CSS
  • shadcn/ui
  • React Router

Backend

  • Python 3.8+
  • Flask
  • OpenAI API
  • ChromaDB (vector database)
  • LangChain
  • CORS support

Contributing

Issues and Pull Requests are welcome!

License

MIT License