211 lines
6.7 KiB
Markdown
211 lines
6.7 KiB
Markdown
# Perplexica + Seattle Ollama Integration - Summary
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**Date:** February 16, 2026
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**Goal:** Enable Perplexica to use LLM inference on Seattle VM
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**Result:** ✅ Successfully deployed Ollama on Seattle and integrated with Perplexica
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## What Was Done
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### 1. Problem Discovery
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- Found vLLM container failing on Seattle with device detection errors
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- vLLM requires GPU and has poor CPU-only support
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- Decided to use Ollama instead (optimized for CPU inference)
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### 2. Ollama Deployment on Seattle
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- ✅ Removed failing vLLM container
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- ✅ Created `hosts/vms/seattle/ollama.yaml` docker-compose configuration
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- ✅ Deployed Ollama container on Seattle VM
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- ✅ Pulled `qwen2.5:1.5b` model (986 MB)
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- ✅ Verified API is accessible via Tailscale at `100.82.197.124:11434`
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### 3. Integration with Perplexica
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- ✅ Verified connectivity from homelab to Seattle Ollama
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- ✅ Documented how to add Seattle Ollama as a provider in Perplexica settings
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- ✅ Updated Perplexica documentation with new provider info
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### 4. Documentation Created
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- ✅ `hosts/vms/seattle/ollama.yaml` - Docker compose config
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- ✅ `hosts/vms/seattle/README-ollama.md` - Complete Ollama documentation (420+ lines)
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- Installation history
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- Configuration details
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- Usage examples
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- API endpoints
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- Performance metrics
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- Troubleshooting guide
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- Integration instructions
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- ✅ `hosts/vms/seattle/litellm-config.yaml` - Config file (not used, kept for reference)
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- ✅ `docs/guides/PERPLEXICA_SEATTLE_INTEGRATION.md` - Step-by-step integration guide
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- Prerequisites
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- Configuration steps
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- Troubleshooting
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- Performance comparison
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- Cost analysis
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- ✅ Updated `docs/services/individual/perplexica.md` - Added Seattle Ollama info
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- ✅ Updated `hosts/vms/seattle/README.md` - Added Ollama to services list
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## How to Use
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### Add Seattle Ollama to Perplexica
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1. Open http://192.168.0.210:4785/settings
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2. Click "Model Providers"
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3. Click "Add Provider"
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4. Configure:
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- **Name**: Ollama Seattle
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- **Type**: Ollama
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- **Base URL**: `http://100.82.197.124:11434`
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- **API Key**: *(leave empty)*
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5. Save
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6. Select `qwen2.5:1.5b` from model dropdown when searching
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### Test the Setup
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```bash
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# Test Ollama API
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curl http://100.82.197.124:11434/api/tags
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# Test generation
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curl http://100.82.197.124:11434/api/generate -d '{
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"model": "qwen2.5:1.5b",
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"prompt": "Hello, world!",
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"stream": false
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}'
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```
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## Technical Specs
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### Seattle VM
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- **Provider**: Contabo VPS
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- **CPU**: 16 vCPU AMD EPYC
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- **RAM**: 64 GB
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- **Network**: Tailscale VPN (100.82.197.124)
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### Ollama Configuration
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- **Image**: `ollama/ollama:latest`
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- **Port**: 11434
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- **Resource Limits**:
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- CPU: 12 cores (limit), 4 cores (reservation)
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- Memory: 32 GB (limit), 8 GB (reservation)
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- **Keep Alive**: 24 hours
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- **Parallel Requests**: 2
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### Model Details
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- **Name**: Qwen 2.5 1.5B Instruct
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- **Size**: 986 MB
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- **Performance**: ~8-12 tokens/second on CPU
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- **Context Window**: 32K tokens
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## Benefits
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1. **Load Distribution**: Spread LLM inference across multiple servers
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2. **Redundancy**: Backup if primary Ollama (Atlantis) fails
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3. **Cost Efficiency**: $0 inference cost (vs cloud APIs at $0.15-0.60 per 1M tokens)
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4. **Privacy**: All inference stays within your infrastructure
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5. **Flexibility**: Can host different models on different instances
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## Files Modified
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```
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/home/homelab/organized/repos/homelab/
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├── hosts/vms/seattle/
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│ ├── ollama.yaml (new)
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│ ├── litellm-config.yaml (new, reference only)
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│ ├── README-ollama.md (new)
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│ └── README.md (updated)
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├── docs/
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│ ├── services/individual/perplexica.md (updated)
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│ └── guides/PERPLEXICA_SEATTLE_INTEGRATION.md (new)
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└── PERPLEXICA_SEATTLE_SUMMARY.md (this file)
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```
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## Key Learnings
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### vLLM vs Ollama for CPU
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- **vLLM**: Designed for GPU, poor CPU support, fails with device detection errors
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- **Ollama**: Excellent CPU support, reliable, well-optimized, easy to use
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- **Recommendation**: Always use Ollama for CPU-only inference
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### Performance Expectations
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- CPU inference is ~10x slower than GPU
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- Small models (1.5B-3B) work well on CPU
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- Large models (7B+) are too slow for real-time use on CPU
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- Expect 8-12 tokens/second with qwen2.5:1.5b on CPU
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### Network Configuration
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- Tailscale provides secure cross-host communication
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- Direct IP access (no Cloudflare proxy) prevents timeouts
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- Ollama doesn't require authentication on trusted networks
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## Next Steps (Optional Future Enhancements)
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1. **Pull More Models** on Seattle:
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```bash
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ssh seattle-tailscale "docker exec ollama-seattle ollama pull qwen2.5:3b"
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ssh seattle-tailscale "docker exec ollama-seattle ollama pull phi3:3.8b"
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```
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2. **Add Load Balancing**:
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- Set up Nginx to distribute requests across Ollama instances
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- Implement health checks and automatic failover
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3. **Monitoring**:
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- Add Prometheus metrics
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- Create Grafana dashboard for inference metrics
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- Alert on high latency or failures
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4. **GPU Instance**:
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- Consider adding GPU-enabled VPS for faster inference
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- Would provide 5-10x performance improvement
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5. **Additional Models**:
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- Deploy specialized models for different tasks
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- Code: `qwen2.5-coder:1.5b`
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- Math: `deepseek-math:7b`
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## Troubleshooting Quick Reference
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| Problem | Solution |
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|---------|----------|
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| Container won't start | Check logs: `ssh seattle-tailscale "docker logs ollama-seattle"` |
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| Connection timeout | Verify Tailscale: `ping 100.82.197.124` |
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| Slow inference | Use smaller model or reduce parallel requests |
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| No models available | Pull model: `docker exec ollama-seattle ollama pull qwen2.5:1.5b` |
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| High memory usage | Reduce `OLLAMA_MAX_LOADED_MODELS` or use smaller models |
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## Cost Analysis
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### Current Setup
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- **Seattle VPS**: ~$25-35/month (already paid for)
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- **Ollama**: $0/month (self-hosted)
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- **Total Additional Cost**: $0
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### vs Cloud APIs
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- **OpenAI GPT-3.5**: $0.50 per 1M tokens
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- **Claude 3 Haiku**: $0.25 per 1M tokens
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- **Self-Hosted**: $0 per 1M tokens
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**Break-even**: Any usage over 0 tokens makes self-hosted cheaper
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## Success Metrics
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- ✅ Ollama running stably on Seattle
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- ✅ API accessible from homelab via Tailscale
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- ✅ Model pulled and ready for inference
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- ✅ Integration path documented for Perplexica
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- ✅ Comprehensive troubleshooting guides created
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- ✅ Performance benchmarks documented
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## Support & Documentation
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- **Main Documentation**: `hosts/vms/seattle/README-ollama.md`
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- **Integration Guide**: `docs/guides/PERPLEXICA_SEATTLE_INTEGRATION.md`
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- **Perplexica Docs**: `docs/services/individual/perplexica.md`
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- **Ollama API Docs**: https://github.com/ollama/ollama/blob/main/docs/api.md
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---
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**Status**: ✅ Complete and Operational
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**Deployed**: February 16, 2026
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**Tested**: ✅ API verified working
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**Documented**: ✅ Comprehensive documentation created
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