Skip to content

Latest commit

 

History

History
301 lines (241 loc) · 6.22 KB

README.md

File metadata and controls

301 lines (241 loc) · 6.22 KB

AI Document Assistant

An intelligent document assistant that allows users to chat with their documents using state-of-the-art language models. Built with FastAPI, React, and LangChain.

Features

  • 📄 Document Processing: Support for PDF, DOCX, CSV, and JSON files
  • 💬 Interactive Chat: Natural conversation with your documents
  • 🔍 Smart Search: Semantic search across all uploaded documents
  • 📊 Source Citations: Automatic citation of sources in responses
  • 💾 Persistent Conversations: Chat history preserved between sessions
  • 📥 Export Functionality: Export conversations as PDF or TXT

Tech Stack

Backend

  • FastAPI
  • LangChain
  • Ollama (DeepSeek model)
  • ChromaDB
  • ReportLab
  • Python 3.11+

Frontend

  • React
  • Material-UI
  • Axios
  • React Markdown
  • React Syntax Highlighter

Local Development Setup

Prerequisites

  1. Install Python 3.11 or higher
  2. Install Node.js 18 or higher
  3. Install Ollama from ollama.ai
  4. Pull the DeepSeek model: 
    ollama pull deepseek-r1:32b

Backend Setup

  1. Clone the repository:

    git clone https://github.com/yourusername/ai-doc-assistant.git
cd ai-doc-assistant

  2. Create and activate a virtual environment:

    python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

  3. Install dependencies:

    pip install -r requirements.txt

  4. Create necessary directories:

    mkdir -p uploads exports vectordb/conversations

  5. Start the backend server:

    uvicorn main:app --reload --port 8000

Frontend Setup

  1. Navigate to the frontend directory:

    cd frontend

  2. Install dependencies:

    npm install

  3. Start the development server:

    npm start

The application will be available at http://localhost:3000

Production Deployment

Backend Deployment

  1. Set up a Linux server (Ubuntu 20.04+ recommended)

  2. Install system dependencies:

    sudo apt update
sudo apt install python3.11 python3.11-venv nginx

  3. Install Ollama:

    curl -fsSL https://ollama.ai/install.sh | sh

  4. Pull the DeepSeek model:

    ollama pull deepseek-r1:32b

  5. Clone and set up the application:

    git clone https://github.com/yourusername/ai-doc-assistant.git
cd ai-doc-assistant
python3.11 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

  6. Create a systemd service for Ollama:

    sudo nano /etc/systemd/system/ollama.service

    Add the following content:

    [Unit]
Description=Ollama Service
After=network.target

[Service]
Type=simple
User=root
ExecStart=/usr/bin/ollama serve
Restart=always

[Install]
WantedBy=multi-user.target

  7. Create a systemd service for the FastAPI application:

    sudo nano /etc/systemd/system/aidoc.service

    Add the following content:

    [Unit]
Description=AI Document Assistant
After=network.target

[Service]
User=ubuntu
WorkingDirectory=/path/to/ai-doc-assistant
Environment="PATH=/path/to/ai-doc-assistant/venv/bin"
ExecStart=/path/to/ai-doc-assistant/venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
Restart=always

[Install]
WantedBy=multi-user.target

  8. Configure Nginx:

    sudo nano /etc/nginx/sites-available/aidoc

    Add the following content:

    server {
    listen 80;
    server_name your_domain.com;

    location / {
        root /path/to/ai-doc-assistant/frontend/build;
        try_files $uri $uri/ /index.html;
    }

    location /api {
        proxy_pass http://localhost:8000;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection 'upgrade';
        proxy_set_header Host $host;
        proxy_cache_bypass $http_upgrade;
    }
}

  9. Enable and start services:

    sudo ln -s /etc/nginx/sites-available/aidoc /etc/nginx/sites-enabled/
sudo systemctl enable nginx
sudo systemctl enable ollama
sudo systemctl enable aidoc
sudo systemctl start nginx
sudo systemctl start ollama
sudo systemctl start aidoc

Frontend Deployment

  1. Build the frontend:

    cd frontend
npm install
npm run build

  2. Copy the build files to the server:

    scp -r build/* user@your_server:/path/to/ai-doc-assistant/frontend/build/

Environment Variables

Create a .env file in the root directory:

# Backend
UPLOAD_DIR=uploads
DB_DIR=vectordb
EXPORT_DIR=exports
MAX_UPLOAD_SIZE=10485760  # 10MB

# Frontend
REACT_APP_API_URL=http://localhost:8000

For production, update the REACT_APP_API_URL to your domain.

Security Considerations

  1. Set up SSL/TLS using Let's Encrypt:

    sudo apt install certbot python3-certbot-nginx
sudo certbot --nginx -d your_domain.com

  2. Configure firewall:

    sudo ufw allow 80
sudo ufw allow 443
sudo ufw allow 22
sudo ufw enable

  3. Set up proper file permissions:

    sudo chown -R ubuntu:ubuntu /path/to/ai-doc-assistant
chmod -R 755 /path/to/ai-doc-assistant

Maintenance

  • Monitor logs:

    sudo journalctl -u ollama -f
sudo journalctl -u aidoc -f

  • Update the application:

    cd /path/to/ai-doc-assistant
git pull
source venv/bin/activate
pip install -r requirements.txt
sudo systemctl restart aidoc

Troubleshooting

  1. Check service status:

    sudo systemctl status ollama
sudo systemctl status aidoc
sudo systemctl status nginx

  2. Check logs:

    sudo journalctl -u ollama -n 100
sudo journalctl -u aidoc -n 100
sudo tail -f /var/log/nginx/error.log

  3. Common issues:

    • Port conflicts: Check if ports 8000 and 3000 are free
    • Memory issues: Monitor RAM usage with htop
    • Disk space: Check available space with df -h

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.