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

• 🤖 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

├── 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

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

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
}

1. Navigate to the backend directory:

cd backend

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

cp env.example .env
# Edit .env and add your OpenAI API key

4. Run the backend server:

python app.py

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

1. Copy env.example to .env:

cp env.example .env

2. Set the backend API address:

VITE_API_BASE_URL=http://localhost:5001

# Install frontend dependencies
npm install

# Start frontend development server
npm run dev

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

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

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

Send a message to the AI assistant

Health check endpoint

Rebuild the vector database (use after updating personal data)

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

• 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

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

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

Issues and Pull Requests are welcome!

MIT License