| # 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 | |