cauvang32
9c180bdd89
- Removed the `analyze_data_file` function from tool definitions to streamline functionality. - Enhanced the `execute_python_code` function description to clarify auto-installation of packages and file handling. - Deleted the `python_executor.py` module to simplify the codebase and improve maintainability. - Introduced a new `token_counter.py` module for efficient token counting for OpenAI API requests, including support for Discord image links and cost estimation.
462 lines
8.7 KiB
Markdown
462 lines
8.7 KiB
Markdown
# Docker Deployment Guide
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## ✅ Docker Compatibility Verification
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All new features are **fully compatible** with Docker deployment:
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### 1. ✅ File Storage System
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- **Location**: `/tmp/bot_code_interpreter/` (created in Dockerfile)
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- **Volumes**: Mounted in docker-compose.yml for persistence
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- **Permissions**: Set to 777 for read/write access
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### 2. ✅ Code Interpreter
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- **Dependencies**: All runtime libraries included (HDF5, OpenBLAS, etc.)
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- **Venv**: Persistent volume for package cache
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- **Timeout**: Configurable via environment variables
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### 3. ✅ 200+ File Types
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- **Libraries**: Build dependencies included for all file formats
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- **Runtime**: All required shared libraries present
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---
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## 🚀 Quick Start
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### Option 1: Using Docker Compose (Recommended)
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```bash
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# 1. Make sure .env file is configured
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cat .env
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# 2. Start the bot
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docker-compose up -d
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# 3. Check logs
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docker-compose logs -f bot
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# 4. Stop the bot
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docker-compose down
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```
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### Option 2: Using Docker CLI
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```bash
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# 1. Build the image
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docker build -t chatgpt-discord-bot .
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# 2. Run the container
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docker run -d \
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--name chatgpt-bot \
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--env-file .env \
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-v bot_files:/tmp/bot_code_interpreter/user_files \
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-v bot_venv:/tmp/bot_code_interpreter/venv \
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-v bot_outputs:/tmp/bot_code_interpreter/outputs \
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--restart always \
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chatgpt-discord-bot
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# 3. Check logs
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docker logs -f chatgpt-bot
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```
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---
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## ⚙️ Configuration
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### Environment Variables
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All configuration is done via the `.env` file:
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```bash
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# Discord & API
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DISCORD_TOKEN=your_token_here
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OPENAI_API_KEY=your_api_key_here
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OPENAI_BASE_URL=https://models.github.ai/inference
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MONGODB_URI=mongodb+srv://...
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# File Management
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FILE_EXPIRATION_HOURS=48 # Files expire after 48 hours (-1 = never)
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MAX_FILES_PER_USER=20 # Max 20 files per user
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# Code Execution
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CODE_EXECUTION_TIMEOUT=300 # 5 minutes timeout
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# Timezone
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TIMEZONE=Asia/Ho_Chi_Minh
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```
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### Volume Mounts
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The docker-compose.yml includes three volumes:
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1. **bot_files**: Persistent storage for user files
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- Path: `/tmp/bot_code_interpreter/user_files`
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- Purpose: Keeps files across container restarts
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2. **bot_venv**: Persistent Python virtual environment
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- Path: `/tmp/bot_code_interpreter/venv`
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- Purpose: Caches installed packages (faster restarts)
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3. **bot_outputs**: Generated output files
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- Path: `/tmp/bot_code_interpreter/outputs`
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- Purpose: Stores generated plots, CSVs, etc.
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### Resource Limits
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Adjust in docker-compose.yml based on your needs:
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```yaml
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deploy:
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resources:
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limits:
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cpus: '2.0' # Max 2 CPU cores
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memory: 2G # Max 2GB RAM
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reservations:
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cpus: '0.5' # Min 0.5 CPU cores
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memory: 512M # Min 512MB RAM
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```
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---
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## 🔧 Troubleshooting
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### Issue: Files not persisting after restart
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**Solution**: Ensure volumes are properly mounted:
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```bash
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# Check volumes
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docker volume ls
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# Inspect volume
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docker volume inspect bot_files
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# If volumes are missing, recreate them
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docker-compose down
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docker-compose up -d
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```
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### Issue: Package installation fails
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**Solution**: Check if venv volume has enough space:
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```bash
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# Check volume size
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docker system df -v
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# Clear old volumes if needed
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docker volume prune
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```
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### Issue: Timeout errors
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**Solution**: Increase timeout in .env or docker-compose.yml:
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```bash
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CODE_EXECUTION_TIMEOUT=900 # 15 minutes for heavy processing
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```
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### Issue: Out of memory
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**Solution**: Increase memory limit in docker-compose.yml:
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```yaml
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limits:
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memory: 4G # Increase to 4GB
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```
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### Issue: File permissions error
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**Solution**: Check /tmp directory permissions:
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```bash
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# Enter container
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docker exec -it <container_id> sh
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# Check permissions
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ls -la /tmp/bot_code_interpreter/
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# Fix if needed (already set in Dockerfile)
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chmod -R 777 /tmp/bot_code_interpreter/
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```
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---
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## 📊 Monitoring
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### View Logs
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```bash
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# All logs
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docker-compose logs -f bot
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# Last 100 lines
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docker-compose logs --tail=100 bot
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# Filter by level
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docker-compose logs bot | grep ERROR
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```
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### Check Resource Usage
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```bash
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# Real-time stats
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docker stats
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# Container info
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docker inspect chatgpt-bot
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```
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### Healthcheck Status
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```bash
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# Check health
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docker ps
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# If unhealthy, check logs
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docker logs chatgpt-bot
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```
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---
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## 🔄 Updates
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### Update to Latest Version
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```bash
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# Pull latest image
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docker-compose pull
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# Restart with new image
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docker-compose up -d
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# Check logs
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docker-compose logs -f bot
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```
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### Rebuild from Source
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```bash
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# Rebuild image
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docker-compose build --no-cache
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# Restart
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docker-compose up -d
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```
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---
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## 💾 Backup
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### Backup Volumes
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```bash
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# Backup user files
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docker run --rm \
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-v bot_files:/data \
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-v $(pwd):/backup \
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alpine tar czf /backup/bot_files_backup.tar.gz /data
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# Backup venv
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docker run --rm \
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-v bot_venv:/data \
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-v $(pwd):/backup \
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alpine tar czf /backup/bot_venv_backup.tar.gz /data
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```
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### Restore Volumes
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```bash
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# Restore user files
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docker run --rm \
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-v bot_files:/data \
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-v $(pwd):/backup \
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alpine sh -c "cd /data && tar xzf /backup/bot_files_backup.tar.gz --strip 1"
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```
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---
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## 🏗️ Build Details
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### Multi-Stage Build
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The Dockerfile uses a multi-stage build for optimization:
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**Stage 1: Builder**
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- Installs all build dependencies
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- Compiles Python packages
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- Strips debug symbols for smaller size
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**Stage 2: Runtime**
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- Only includes runtime dependencies
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- Much smaller final image
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- Faster startup time
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### Included Dependencies
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**Build-time:**
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- gcc, g++, rust, cargo
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- HDF5, OpenBLAS, LAPACK development files
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- Image processing libraries (freetype, libpng, libjpeg)
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**Runtime:**
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- HDF5, OpenBLAS, LAPACK shared libraries
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- Image processing runtime libraries
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- Git (for package installations)
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- Bash (for shell scripts in code execution)
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---
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## 🔒 Security
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### Best Practices
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1. **Never commit .env file**
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```bash
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# .env is in .gitignore
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git status # Should not show .env
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```
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2. **Use secrets management**
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```bash
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# For production, use Docker secrets
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docker secret create discord_token token.txt
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```
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3. **Limit container permissions**
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```yaml
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# In docker-compose.yml
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security_opt:
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- no-new-privileges:true
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```
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4. **Regular updates**
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```bash
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# Update base image regularly
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docker-compose pull
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docker-compose up -d
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```
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---
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## 📈 Performance Optimization
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### 1. Persistent Venv
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The venv volume caches installed packages:
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- **First run**: Installs packages (slow)
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- **Subsequent runs**: Uses cache (fast)
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### 2. Layer Caching
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The Dockerfile is optimized for layer caching:
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- Requirements installed in separate layer
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- Application code copied last
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- Only rebuilds changed layers
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### 3. Resource Allocation
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Adjust based on usage:
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- **Light usage**: 0.5 CPU, 512MB RAM
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- **Medium usage**: 1 CPU, 1GB RAM
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- **Heavy usage**: 2+ CPUs, 2GB+ RAM
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---
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## ✅ Verification Checklist
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Before deploying:
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- [ ] `.env` file configured with all required variables
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- [ ] Docker and Docker Compose installed
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- [ ] Sufficient disk space for volumes (5GB+ recommended)
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- [ ] Network access to Discord API and MongoDB
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- [ ] Ports not conflicting with other services
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After deploying:
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- [ ] Container is running: `docker ps`
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- [ ] No errors in logs: `docker-compose logs bot`
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- [ ] Bot online in Discord
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- [ ] File uploads work
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- [ ] Code execution works
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- [ ] Files persist after restart
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---
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## 🎯 Production Deployment
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### Recommended Setup
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```yaml
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version: '3.8'
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services:
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bot:
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image: ghcr.io/coder-vippro/chatgpt-discord-bot:latest
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env_file:
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- .env
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restart: always
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volumes:
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- bot_files:/tmp/bot_code_interpreter/user_files
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- bot_venv:/tmp/bot_code_interpreter/venv
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- bot_outputs:/tmp/bot_code_interpreter/outputs
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deploy:
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resources:
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limits:
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cpus: '2.0'
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memory: 2G
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reservations:
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cpus: '1.0'
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memory: 1G
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healthcheck:
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test: ["CMD", "python3", "-c", "import sys; sys.exit(0)"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 40s
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logging:
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driver: "json-file"
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options:
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max-size: "10m"
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max-file: "3"
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volumes:
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bot_files:
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driver: local
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bot_venv:
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driver: local
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bot_outputs:
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driver: local
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```
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---
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## 📞 Support
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If you encounter issues:
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1. Check logs: `docker-compose logs -f bot`
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2. Verify volumes: `docker volume ls`
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3. Check resources: `docker stats`
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4. Review configuration: `cat .env`
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5. Test file access: `docker exec -it <container> ls -la /tmp/bot_code_interpreter/`
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---
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## 🎉 Summary
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✅ **Docker Setup Complete!**
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The bot is now fully compatible with Docker deployment with:
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- Persistent file storage
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- Cached package installations
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- Configurable resource limits
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- Health monitoring
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- Production-ready configuration
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**Deploy with confidence!** 🚀
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