# Deployment Guide for Educational Research Methods Chatbot

This guide provides instructions for deploying the Educational Research Methods Chatbot as a permanent website.

## Prerequisites

- Docker and Docker Compose installed on the host machine
- An OpenAI API key for Command R+ access
- A server or cloud provider for hosting the containerized application

## Deployment Options

### Option 1: Deploy to a Cloud Provider (Recommended)

1. **Set up a cloud instance**:
   - AWS EC2
   - Google Cloud Compute Engine
   - DigitalOcean Droplet
   - Azure Virtual Machine

2. **Install Docker and Docker Compose on the instance**

3. **Upload the application files to the instance**

4. **Set environment variables**:
   ```
   export OPENAI_API_KEY=your_api_key_here
   ```

5. **Build and start the containers**:
   ```
   cd research_methods_chatbot
   docker-compose -f deployment/docker-compose.yml up -d
   ```

6. **Configure a domain name** (optional):
   - Purchase a domain name from a registrar
   - Point the domain to your server's IP address
   - Set up SSL with Let's Encrypt

### Option 2: Deploy to a Static Hosting Service

For a simpler deployment with limited functionality:

1. **Modify the frontend to use a separate API endpoint**
2. **Deploy the frontend to a static hosting service** (GitHub Pages, Netlify, Vercel)
3. **Deploy the backend to a serverless platform** (AWS Lambda, Google Cloud Functions)

## Maintenance

- **Monitoring**: Set up monitoring for the application to ensure it remains operational
- **Updates**: Periodically update dependencies and the LLM model
- **Backups**: Regularly backup any persistent data

## Security Considerations

- **API Key**: Keep your OpenAI API key secure
- **Rate Limiting**: Implement rate limiting to prevent abuse
- **Input Validation**: Ensure all user inputs are properly validated

## Scaling

If the application receives high traffic:

1. **Horizontal Scaling**: Deploy multiple instances behind a load balancer
2. **Caching**: Implement caching for common queries
3. **Database Optimization**: Optimize the vector database for faster retrieval

## Troubleshooting

- **Container Issues**: Check Docker logs with `docker logs container_name`
- **API Errors**: Verify your OpenAI API key is valid and has sufficient credits
- **Performance Problems**: Monitor resource usage and scale as needed