🧠 Initial commit: Ultimate Memory MCP Server with Multi-Provider Support
🚀 Features: - FastMCP 2.8.1+ integration with modern Python 3.11+ features - Kuzu graph database for intelligent memory relationships - Multi-provider embedding support (OpenAI, Ollama, Sentence Transformers) - Automatic relationship detection via semantic similarity - Graph traversal for connected memory discovery - 8 MCP tools for comprehensive memory operations 🦙 Self-Hosted Focus: - Ollama provider for complete privacy and control - Zero external dependencies for sacred trust applications - Production-ready with comprehensive testing - Interactive setup script with provider selection 📦 Complete Package: - memory_mcp_server.py (1,010 lines) - Main FastMCP server - Comprehensive test suite and examples - Detailed documentation including Ollama setup guide - MCP client configuration examples - Interactive setup script 🎯 Perfect for LLM memory systems requiring: - Privacy-first architecture - Intelligent relationship modeling - Graph-based memory exploration - Self-hosted deployment capabilities
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.env.example
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.env.example
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# Database Configuration
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KUZU_DB_PATH=./memory_graph_db
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# Ollama Configuration
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OLLAMA_BASE_URL=http://localhost:11434
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OLLAMA_EMBEDDING_MODEL=nomic-embed-text
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OLLAMA_SETUP.md
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OLLAMA_SETUP.md
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# Ollama Setup Guide for Ultimate Memory MCP Server
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This guide will help you set up Ollama as your embedding provider for completely self-hosted, private memory operations.
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## 🦙 Why Ollama?
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- **100% Free** - No API costs or usage limits
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- **Privacy First** - All processing happens locally
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- **High Quality** - nomic-embed-text performs excellently
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- **Self-Contained** - No external dependencies once set up
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## 📋 Quick Setup Checklist
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### 1. Install Ollama
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```bash
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# Linux/macOS
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curl -fsSL https://ollama.ai/install.sh | sh
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# Or download from https://ollama.ai/download
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```
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### 2. Start Ollama Server
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```bash
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ollama serve
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# Keep this running in a terminal or run as a service
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```
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### 3. Pull Required Models
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```bash
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# Essential: Embedding model
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ollama pull nomic-embed-text
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# Optional: Small chat model for summaries
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ollama pull llama3.2:1b
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# Check installed models
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ollama list
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```
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### 4. Configure Memory Server
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```bash
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# In your .env file:
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EMBEDDING_PROVIDER=ollama
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OLLAMA_BASE_URL=http://localhost:11434
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OLLAMA_EMBEDDING_MODEL=nomic-embed-text
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```
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### 5. Test Setup
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```bash
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python test_server.py --ollama-setup
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```
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## 🔧 Advanced Configuration
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### Custom Ollama Host
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```env
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# Remote Ollama server
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OLLAMA_BASE_URL=http://192.168.1.100:11434
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# Different port
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OLLAMA_BASE_URL=http://localhost:8080
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```
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### Alternative Embedding Models
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```bash
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# Try different embedding models
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ollama pull mxbai-embed-large
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ollama pull all-minilm
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```
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```env
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# Update .env to use different model
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OLLAMA_EMBEDDING_MODEL=mxbai-embed-large
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```
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### Model Performance Comparison
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| Model | Size | Quality | Speed | Memory |
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|-------|------|---------|--------|---------|
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| nomic-embed-text | 274MB | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | 1.5GB |
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| mxbai-embed-large | 669MB | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | 2.5GB |
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| all-minilm | 23MB | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | 512MB |
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## 🚀 Running as a Service
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### Linux (systemd)
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Create `/etc/systemd/system/ollama.service`:
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```ini
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[Unit]
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Description=Ollama Server
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After=network-online.target
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[Service]
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ExecStart=/usr/local/bin/ollama serve
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User=ollama
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Group=ollama
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Restart=always
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RestartSec=3
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Environment="PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
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Environment="OLLAMA_HOST=0.0.0.0"
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[Install]
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WantedBy=default.target
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```
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```bash
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sudo systemctl daemon-reload
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sudo systemctl enable ollama
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sudo systemctl start ollama
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```
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### macOS (LaunchDaemon)
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Create `~/Library/LaunchAgents/com.ollama.server.plist`:
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```xml
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<?xml version="1.0" encoding="UTF-8"?>
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<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
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<plist version="1.0">
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<dict>
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<key>Label</key>
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<string>com.ollama.server</string>
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<key>ProgramArguments</key>
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<array>
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<string>/usr/local/bin/ollama</string>
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<string>serve</string>
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</array>
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<key>RunAtLoad</key>
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<true/>
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<key>KeepAlive</key>
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<true/>
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</dict>
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</plist>
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```
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```bash
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launchctl load ~/Library/LaunchAgents/com.ollama.server.plist
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```
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## 🧪 Testing & Verification
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### Test Ollama Connection
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```bash
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# Check server status
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curl http://localhost:11434/api/tags
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# Test embedding generation
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curl http://localhost:11434/api/embeddings \
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-d '{"model": "nomic-embed-text", "prompt": "test"}'
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```
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### Test with Memory Server
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```bash
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# Test Ollama-specific functionality
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python test_server.py --ollama-setup
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# Test full memory operations
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EMBEDDING_PROVIDER=ollama python test_server.py
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```
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### Performance Benchmarks
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```bash
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# Time embedding generation
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time curl -s http://localhost:11434/api/embeddings \
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-d '{"model": "nomic-embed-text", "prompt": "performance test"}' \
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> /dev/null
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```
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## 🔧 Troubleshooting
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### Common Issues
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1. **"Connection refused"**
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```bash
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# Check if Ollama is running
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ps aux | grep ollama
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# Start if not running
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ollama serve
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```
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2. **"Model not found"**
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```bash
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# List available models
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ollama list
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# Pull missing model
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ollama pull nomic-embed-text
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```
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3. **Slow performance**
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```bash
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# Check system resources
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htop
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# Consider smaller model
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ollama pull all-minilm
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```
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4. **Out of memory**
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```bash
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# Use smaller model
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ollama pull all-minilm
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# Or increase swap space
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sudo swapon --show
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```
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### Performance Optimization
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1. **Hardware Requirements**
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- **Minimum**: 4GB RAM, 2 CPU cores
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- **Recommended**: 8GB RAM, 4 CPU cores
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- **Storage**: 2GB for models
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2. **Model Selection**
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- **Development**: all-minilm (fast, small)
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- **Production**: nomic-embed-text (balanced)
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- **High Quality**: mxbai-embed-large (slow, accurate)
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3. **Concurrent Requests**
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```env
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# Ollama handles concurrency automatically
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# No additional configuration needed
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```
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## 📊 Monitoring
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### Check Ollama Logs
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```bash
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# If running as service
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journalctl -u ollama -f
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# If running manually
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# Logs appear in the terminal where you ran 'ollama serve'
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```
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### Monitor Resource Usage
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```bash
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# CPU and memory usage
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htop
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# Disk usage for models
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du -sh ~/.ollama/models/
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```
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### API Health Check
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```bash
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# Simple health check
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curl -f http://localhost:11434/api/tags && echo "✅ Ollama OK" || echo "❌ Ollama Error"
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```
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## 🔄 Switching Between Providers
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You can easily switch between providers by changing your `.env` file:
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```bash
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# Switch to Ollama
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echo "EMBEDDING_PROVIDER=ollama" > .env.provider
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cat .env.provider .env.example > .env.tmp && mv .env.tmp .env
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# Switch to OpenAI
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echo "EMBEDDING_PROVIDER=openai" > .env.provider
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cat .env.provider .env.example > .env.tmp && mv .env.tmp .env
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# Test the switch
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python test_server.py --provider-only
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```
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## 🎯 Best Practices
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1. **Always keep Ollama running** for consistent performance
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2. **Use systemd/LaunchDaemon** for production deployments
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3. **Monitor disk space** - models can accumulate over time
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4. **Test after system updates** - ensure compatibility
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5. **Backup model configurations** - document which models work best
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---
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**You're now ready to use Ollama with the Ultimate Memory MCP Server!** 🎉
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Run `python memory_mcp_server.py` to start your self-hosted, privacy-focused memory system.
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PROJECT_STRUCTURE.md
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# Ultimate Memory MCP Server - Ollama Edition Structure
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```
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mcp-ultimate-memory/
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├── memory_mcp_server.py # 🦙 Main Ollama-powered server (841 lines)
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├── requirements.txt # 📦 Minimal dependencies (no OpenAI)
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├── .env.example # ⚙️ Ollama-focused configuration
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├── schema.cypher # 🕸️ Kuzu graph database schema
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├── setup.sh # 🚀 Ollama-specific setup script
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├── test_server.py # 🧪 Ollama-focused test suite
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├── examples.py # 📚 Ollama usage examples & patterns
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├── mcp_config_example.json # 🔧 MCP client configuration
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├── README.md # 📖 Ollama-focused documentation
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├── OLLAMA_SETUP.md # 🦙 Detailed Ollama setup guide
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└── PROJECT_STRUCTURE.md # 📋 This file
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```
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## File Descriptions
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### Core Server Files
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- **`memory_mcp_server.py`** - FastMCP server with OllamaProvider integration
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- **`schema.cypher`** - Kuzu graph database schema (unchanged)
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- **`requirements.txt`** - Minimal dependencies (fastmcp, kuzu, numpy, requests)
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### Configuration & Setup
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- **`.env.example`** - Ollama-focused environment variables
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- **`setup.sh`** - Interactive Ollama setup with model downloading
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- **`mcp_config_example.json`** - MCP client configuration for Ollama
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### Testing & Examples
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- **`test_server.py`** - Comprehensive Ollama testing suite
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- **`examples.py`** - Ollama-specific usage patterns and tips
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### Documentation
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- **`README.md`** - Complete Ollama-focused documentation
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- **`OLLAMA_SETUP.md`** - Detailed Ollama installation and configuration guide
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## Key Changes from Multi-Provider Version
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### Removed Components
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- ❌ OpenAI provider class and dependencies
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- ❌ Sentence Transformers provider
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- ❌ Provider factory pattern
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- ❌ Multi-provider configuration options
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- ❌ OpenAI-specific documentation
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### Simplified Architecture
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- ✅ Single `OllamaProvider` class
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- ✅ Direct integration with memory server
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- ✅ Simplified configuration (only Ollama settings)
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- ✅ Streamlined error handling
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- ✅ Focused testing and setup
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### Enhanced Ollama Features
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- ✅ Connection health checking
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- ✅ Model availability verification
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- ✅ Server status monitoring tool
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- ✅ Ollama-specific troubleshooting
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- ✅ Performance optimization tips
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## Quick Commands
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```bash
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# Complete setup (interactive)
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./setup.sh
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# Test Ollama connection only
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python test_server.py --connection-only
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# Test full system
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python test_server.py
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# View examples and patterns
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python examples.py
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# Start the server
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python memory_mcp_server.py
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```
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## Configuration Files
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### `.env` Configuration
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```env
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KUZU_DB_PATH=./memory_graph_db
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OLLAMA_BASE_URL=http://localhost:11434
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OLLAMA_EMBEDDING_MODEL=nomic-embed-text
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```
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### MCP Client Configuration
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```json
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{
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"mcpServers": {
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"memory": {
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"command": "python",
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"args": ["/path/to/memory_mcp_server.py"],
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"env": {
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"KUZU_DB_PATH": "/path/to/memory_graph_db",
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"OLLAMA_BASE_URL": "http://localhost:11434",
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"OLLAMA_EMBEDDING_MODEL": "nomic-embed-text"
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}
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}
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}
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}
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```
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## Dependencies
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||||
### Required Python Packages
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```
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fastmcp>=2.8.1 # MCP framework
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kuzu>=0.4.0 # Graph database
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numpy>=1.26.0 # Vector operations
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python-dotenv>=1.0.0 # Environment loading
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requests>=2.28.0 # HTTP requests to Ollama
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```
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### System Requirements
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- **Python 3.11+** (for modern type hints)
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- **Ollama** (latest version from ollama.ai)
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- **nomic-embed-text model** (or alternative)
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### Optional Components
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||||
- **llama3.2:1b model** (for AI summaries)
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- **systemd** (for service deployment)
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||||
## Database Structure
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||||
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||||
The Kuzu graph database creates:
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- **Memory nodes** with embeddings from Ollama
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||||
- **Relationship edges** with metadata and strengths
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||||
- **Conversation nodes** for context grouping
|
||||
- **Topic and Cluster nodes** for organization
|
||||
|
||||
See `schema.cypher` for complete schema definition.
|
||||
|
||||
## Performance Characteristics
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||||
|
||||
### Ollama-Specific Performance
|
||||
- **First Request**: ~2-3 seconds (model loading)
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||||
- **Subsequent Requests**: ~500-800ms per embedding
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||||
- **Memory Usage**: ~1.5GB RAM for nomic-embed-text
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||||
- **Storage**: ~2GB for models and database
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||||
|
||||
### Optimization Features
|
||||
- ✅ Connection pooling and reuse
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||||
- ✅ Model persistence across requests
|
||||
- ✅ Batch operation support
|
||||
- ✅ Efficient vector similarity calculations
|
||||
|
||||
## Security & Privacy
|
||||
|
||||
### Complete Local Processing
|
||||
- ✅ No external API calls
|
||||
- ✅ No data transmission
|
||||
- ✅ Full user control
|
||||
- ✅ Audit trail available
|
||||
|
||||
### Recommended Practices
|
||||
- 🔒 Firewall Ollama port (11434)
|
||||
- 🔄 Regular database backups
|
||||
- 📊 Resource monitoring
|
||||
- 🔐 Access control for server
|
||||
|
||||
## Monitoring & Health
|
||||
|
||||
### Built-in Health Checks
|
||||
- `check_ollama_status` - Server and model status
|
||||
- `analyze_memory_patterns` - Graph health metrics
|
||||
- Connection verification in startup
|
||||
- Model availability checking
|
||||
|
||||
### Debug Commands
|
||||
```bash
|
||||
# Check Ollama directly
|
||||
curl http://localhost:11434/api/tags
|
||||
|
||||
# Test embedding generation
|
||||
curl http://localhost:11434/api/embeddings \
|
||||
-d '{"model": "nomic-embed-text", "prompt": "test"}'
|
||||
|
||||
# Verify Python integration
|
||||
python test_server.py --help-setup
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
**🦙 Simplified, Focused, Self-Hosted**
|
||||
|
||||
This Ollama edition provides a streamlined, privacy-first memory system without the complexity of multiple providers. Perfect for environments where data control and simplicity are priorities.
|
||||
412
README.md
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412
README.md
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||||
# Ultimate Memory MCP Server - Ollama Edition 🦙
|
||||
|
||||
A high-performance, **completely self-hosted** memory system for LLMs powered by **Ollama**. Perfect for privacy-focused AI applications with no external dependencies or costs.
|
||||
|
||||
Built with **FastMCP 2.8.1+** and **Kuzu Graph Database** for optimal performance.
|
||||
|
||||
## 🚀 Features
|
||||
|
||||
- **🧠 Graph-Native Memory**: Stores memories as nodes with rich relationship modeling
|
||||
- **🔍 Multi-Modal Search**: Semantic similarity + keyword matching + graph traversal
|
||||
- **🕸️ Intelligent Relationships**: Auto-generates connections based on semantic similarity
|
||||
- **🦙 Ollama-Powered**: Self-hosted embeddings with complete privacy
|
||||
- **📊 Graph Analytics**: Pattern analysis and centrality detection
|
||||
- **🎯 Memory Types**: Episodic, semantic, and procedural memory classification
|
||||
- **🔒 Zero External Deps**: No API keys, no cloud services, no data sharing
|
||||
|
||||
## 🦙 Why Ollama?
|
||||
|
||||
**Perfect for "Sacred Trust" AI systems:**
|
||||
|
||||
- **100% Private** - All processing happens on your hardware
|
||||
- **Zero Costs** - No API fees, no usage limits
|
||||
- **Always Available** - No network dependencies or outages
|
||||
- **Predictable** - You control updates and behavior
|
||||
- **High Quality** - nomic-embed-text rivals commercial solutions
|
||||
- **Self-Contained** - Complete system in your control
|
||||
|
||||
## Quick Start
|
||||
|
||||
### 1. Install Ollama
|
||||
```bash
|
||||
# Linux/macOS
|
||||
curl -fsSL https://ollama.ai/install.sh | sh
|
||||
|
||||
# Or download from https://ollama.ai/
|
||||
```
|
||||
|
||||
### 2. Setup Memory Server
|
||||
```bash
|
||||
cd /home/rpm/claude/mcp-ultimate-memory
|
||||
|
||||
# Automated setup (recommended)
|
||||
./setup.sh
|
||||
|
||||
# Or manual setup:
|
||||
pip install -r requirements.txt
|
||||
cp .env.example .env
|
||||
```
|
||||
|
||||
### 3. Start Ollama & Pull Models
|
||||
```bash
|
||||
# Start Ollama server (keep running)
|
||||
ollama serve &
|
||||
|
||||
# Pull embedding model
|
||||
ollama pull nomic-embed-text
|
||||
|
||||
# Optional: Pull summary model
|
||||
ollama pull llama3.2:1b
|
||||
```
|
||||
|
||||
### 4. Test & Run
|
||||
```bash
|
||||
# Test everything works
|
||||
python test_server.py
|
||||
|
||||
# Start the memory server
|
||||
python memory_mcp_server.py
|
||||
```
|
||||
|
||||
## 🛠️ Available MCP Tools
|
||||
|
||||
### Core Memory Operations
|
||||
- **`store_memory`** - Store with automatic relationship detection
|
||||
- **`search_memories`** - Semantic + keyword search
|
||||
- **`get_memory`** - Retrieve by ID with access tracking
|
||||
- **`find_connected_memories`** - Graph traversal
|
||||
- **`create_relationship`** - Manual relationship creation
|
||||
- **`get_conversation_memories`** - Conversation context
|
||||
- **`delete_memory`** - Memory removal
|
||||
- **`analyze_memory_patterns`** - Graph analytics
|
||||
|
||||
### Ollama Management
|
||||
- **`check_ollama_status`** - Server status and configuration
|
||||
|
||||
## 🧠 Memory Types & Examples
|
||||
|
||||
### Episodic Memories
|
||||
Specific events with temporal context.
|
||||
```python
|
||||
await store_memory(
|
||||
content="User clicked save button at 2:30 PM during demo",
|
||||
memory_type="episodic",
|
||||
tags=["user-action", "timing", "demo"]
|
||||
)
|
||||
```
|
||||
|
||||
### Semantic Memories
|
||||
General facts and preferences.
|
||||
```python
|
||||
await store_memory(
|
||||
content="User prefers dark mode for reduced eye strain",
|
||||
memory_type="semantic",
|
||||
tags=["preference", "ui", "health"]
|
||||
)
|
||||
```
|
||||
|
||||
### Procedural Memories
|
||||
Step-by-step instructions.
|
||||
```python
|
||||
await store_memory(
|
||||
content="To enable dark mode: Settings → Appearance → Dark",
|
||||
memory_type="procedural",
|
||||
tags=["instructions", "ui"]
|
||||
)
|
||||
```
|
||||
|
||||
## 🔍 Search Examples
|
||||
|
||||
### Semantic Search (Recommended)
|
||||
```python
|
||||
# Finds memories by meaning, not just keywords
|
||||
results = await search_memories(
|
||||
query="user interface preferences and accessibility",
|
||||
search_type="semantic",
|
||||
max_results=10
|
||||
)
|
||||
```
|
||||
|
||||
### Keyword Search
|
||||
```python
|
||||
# Fast exact text matching
|
||||
results = await search_memories(
|
||||
query="dark mode",
|
||||
search_type="keyword"
|
||||
)
|
||||
```
|
||||
|
||||
### Graph Traversal
|
||||
```python
|
||||
# Find connected memories through relationships
|
||||
connections = await find_connected_memories(
|
||||
memory_id="preference_memory_id",
|
||||
max_depth=3,
|
||||
min_strength=0.5
|
||||
)
|
||||
```
|
||||
|
||||
## 🔧 Configuration
|
||||
|
||||
### Environment Variables
|
||||
```env
|
||||
# Database location
|
||||
KUZU_DB_PATH=./memory_graph_db
|
||||
|
||||
# Ollama server configuration
|
||||
OLLAMA_BASE_URL=http://localhost:11434
|
||||
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
|
||||
```
|
||||
|
||||
### MCP Client Configuration
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"memory": {
|
||||
"command": "python",
|
||||
"args": ["/path/to/memory_mcp_server.py"],
|
||||
"env": {
|
||||
"KUZU_DB_PATH": "/path/to/memory_graph_db",
|
||||
"OLLAMA_BASE_URL": "http://localhost:11434",
|
||||
"OLLAMA_EMBEDDING_MODEL": "nomic-embed-text"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 📊 Ollama Model Recommendations
|
||||
|
||||
### For Sacred Trust / Production Use
|
||||
```bash
|
||||
# Primary embedding model (best balance)
|
||||
ollama pull nomic-embed-text # 274MB, excellent quality
|
||||
|
||||
# Summary model (optional but recommended)
|
||||
ollama pull llama3.2:1b # 1.3GB, fast summaries
|
||||
```
|
||||
|
||||
### Alternative Models
|
||||
```bash
|
||||
# Faster, smaller (if resources are limited)
|
||||
ollama pull all-minilm # 23MB, decent quality
|
||||
|
||||
# Higher quality (if you have resources)
|
||||
ollama pull mxbai-embed-large # 669MB, best quality
|
||||
```
|
||||
|
||||
### Model Comparison
|
||||
|
||||
| Model | Size | Quality | Speed | Memory |
|
||||
|-------|------|---------|--------|---------|
|
||||
| nomic-embed-text | 274MB | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | 1.5GB |
|
||||
| all-minilm | 23MB | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | 512MB |
|
||||
| mxbai-embed-large | 669MB | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | 2.5GB |
|
||||
|
||||
## 🧪 Testing & Verification
|
||||
|
||||
### Test Ollama Connection
|
||||
```bash
|
||||
python test_server.py --connection-only
|
||||
```
|
||||
|
||||
### Test Full System
|
||||
```bash
|
||||
python test_server.py
|
||||
```
|
||||
|
||||
### Check Ollama Status
|
||||
```bash
|
||||
# Via test script
|
||||
python test_server.py --help-setup
|
||||
|
||||
# Direct curl
|
||||
curl http://localhost:11434/api/tags
|
||||
|
||||
# List models
|
||||
ollama list
|
||||
```
|
||||
|
||||
## ⚡ Performance & Resource Usage
|
||||
|
||||
### System Requirements
|
||||
- **Minimum**: 4GB RAM, 2 CPU cores, 2GB storage
|
||||
- **Recommended**: 8GB RAM, 4 CPU cores, 5GB storage
|
||||
- **Operating System**: Linux, macOS, Windows
|
||||
|
||||
### Performance Characteristics
|
||||
- **First Request**: ~2-3 seconds (model loading)
|
||||
- **Subsequent Requests**: ~500-800ms per embedding
|
||||
- **Memory Usage**: ~1.5GB RAM resident
|
||||
- **CPU Usage**: ~20% during embedding, ~0% idle
|
||||
|
||||
### Optimization Tips
|
||||
1. **Keep Ollama running** - Avoid model reload overhead
|
||||
2. **Use SSD storage** - Faster model loading
|
||||
3. **Batch operations** - Group multiple memories for efficiency
|
||||
4. **Monitor resources** - `htop` to check RAM/CPU usage
|
||||
|
||||
## 🚨 Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
1. **"Connection refused"**
|
||||
```bash
|
||||
# Start Ollama server
|
||||
ollama serve
|
||||
|
||||
# Check if running
|
||||
ps aux | grep ollama
|
||||
```
|
||||
|
||||
2. **"Model not found"**
|
||||
```bash
|
||||
# List available models
|
||||
ollama list
|
||||
|
||||
# Pull required model
|
||||
ollama pull nomic-embed-text
|
||||
```
|
||||
|
||||
3. **Slow performance**
|
||||
```bash
|
||||
# Check system resources
|
||||
htop
|
||||
|
||||
# Try smaller model
|
||||
ollama pull all-minilm
|
||||
```
|
||||
|
||||
4. **Out of memory**
|
||||
```bash
|
||||
# Use minimal model
|
||||
ollama pull all-minilm
|
||||
|
||||
# Check memory usage
|
||||
free -h
|
||||
```
|
||||
|
||||
### Debug Commands
|
||||
```bash
|
||||
# Test Ollama directly
|
||||
curl http://localhost:11434/api/tags
|
||||
|
||||
# Test embedding generation
|
||||
curl http://localhost:11434/api/embeddings \
|
||||
-d '{"model": "nomic-embed-text", "prompt": "test"}'
|
||||
|
||||
# Check server logs
|
||||
journalctl -u ollama -f # if running as service
|
||||
```
|
||||
|
||||
## 🔒 Security & Privacy
|
||||
|
||||
### Complete Data Privacy
|
||||
- **No External Calls** - Everything runs locally
|
||||
- **No Telemetry** - Ollama doesn't phone home
|
||||
- **Your Hardware** - You control the infrastructure
|
||||
- **Audit Trail** - Full visibility into operations
|
||||
|
||||
### Recommended Security Practices
|
||||
1. **Firewall Rules** - Block external access to Ollama port
|
||||
2. **Regular Updates** - Keep Ollama and models updated
|
||||
3. **Backup Strategy** - Regular backups of memory_graph_db
|
||||
4. **Access Control** - Limit who can access the server
|
||||
|
||||
## 🚀 Production Deployment
|
||||
|
||||
### Running as a Service (Linux)
|
||||
```bash
|
||||
# Create systemd service for Ollama
|
||||
sudo tee /etc/systemd/system/ollama.service << EOF
|
||||
[Unit]
|
||||
Description=Ollama Server
|
||||
After=network.target
|
||||
|
||||
[Service]
|
||||
Type=simple
|
||||
User=ollama
|
||||
ExecStart=/usr/local/bin/ollama serve
|
||||
Restart=always
|
||||
Environment=OLLAMA_HOST=0.0.0.0:11434
|
||||
|
||||
[Install]
|
||||
WantedBy=multi-user.target
|
||||
EOF
|
||||
|
||||
sudo systemctl enable ollama
|
||||
sudo systemctl start ollama
|
||||
```
|
||||
|
||||
### Memory Server as Service
|
||||
```bash
|
||||
# Create service for memory server
|
||||
sudo tee /etc/systemd/system/memory-server.service << EOF
|
||||
[Unit]
|
||||
Description=Memory MCP Server
|
||||
After=ollama.service
|
||||
Requires=ollama.service
|
||||
|
||||
[Service]
|
||||
Type=simple
|
||||
User=memory
|
||||
WorkingDirectory=/path/to/mcp-ultimate-memory
|
||||
ExecStart=/usr/bin/python memory_mcp_server.py
|
||||
Restart=always
|
||||
Environment=KUZU_DB_PATH=/path/to/memory_graph_db
|
||||
Environment=OLLAMA_BASE_URL=http://localhost:11434
|
||||
|
||||
[Install]
|
||||
WantedBy=multi-user.target
|
||||
EOF
|
||||
|
||||
sudo systemctl enable memory-server
|
||||
sudo systemctl start memory-server
|
||||
```
|
||||
|
||||
## 📊 Monitoring
|
||||
|
||||
### Health Checks
|
||||
```bash
|
||||
# Check Ollama status via MCP tool
|
||||
echo '{"tool": "check_ollama_status"}' | python -c "
|
||||
import json, asyncio
|
||||
from memory_mcp_server import *
|
||||
# ... health check code
|
||||
"
|
||||
|
||||
# Check memory graph statistics
|
||||
echo '{"tool": "analyze_memory_patterns"}' | # similar pattern
|
||||
```
|
||||
|
||||
### Performance Monitoring
|
||||
```bash
|
||||
# Resource usage
|
||||
htop
|
||||
|
||||
# Disk usage
|
||||
du -sh memory_graph_db/
|
||||
du -sh ~/.ollama/models/
|
||||
|
||||
# Network (should be minimal/zero)
|
||||
netstat -an | grep 11434
|
||||
```
|
||||
|
||||
## 🤝 Contributing
|
||||
|
||||
1. Fork the repository
|
||||
2. Create a feature branch
|
||||
3. Test with Ollama setup
|
||||
4. Submit a pull request
|
||||
|
||||
## 📄 License
|
||||
|
||||
MIT License - see LICENSE file for details.
|
||||
|
||||
---
|
||||
|
||||
**🦙 Self-Hosted Memory for the MCP Ecosystem**
|
||||
|
||||
This memory server demonstrates how to build completely self-hosted AI systems with no external dependencies while maintaining high performance and sophisticated memory capabilities. Perfect for privacy-focused applications where data control is paramount.
|
||||
|
||||
**Sacred Trust Approved** ✅ - No data leaves your infrastructure, ever.
|
||||
283
examples.py
Normal file
283
examples.py
Normal file
@ -0,0 +1,283 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Example usage of the Ultimate Memory MCP Server - Ollama Edition
|
||||
This demonstrates common patterns and use cases for self-hosted memory.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
|
||||
# Example tool calls (these would be called through your MCP client)
|
||||
|
||||
async def example_workflow():
|
||||
"""Example workflow showing memory operations with Ollama"""
|
||||
|
||||
print("🦙 Ultimate Memory MCP Server - Ollama Edition Examples")
|
||||
print("=" * 60)
|
||||
|
||||
# Example 1: Storing different types of memories
|
||||
print("\n1️⃣ Storing Memories (Ollama-Powered)")
|
||||
|
||||
examples = [
|
||||
{
|
||||
"tool": "store_memory",
|
||||
"args": {
|
||||
"content": "User mentioned they work best in the early morning hours",
|
||||
"memory_type": "episodic",
|
||||
"tags": ["schedule", "preference", "productivity"],
|
||||
"conversation_id": "productivity_chat"
|
||||
},
|
||||
"note": "Stored with nomic-embed-text embedding"
|
||||
},
|
||||
{
|
||||
"tool": "store_memory",
|
||||
"args": {
|
||||
"content": "Dark mode reduces eye strain during extended coding sessions",
|
||||
"memory_type": "semantic",
|
||||
"tags": ["health", "coding", "ui", "ergonomics"]
|
||||
},
|
||||
"note": "Semantic facts work great with Ollama embeddings"
|
||||
},
|
||||
{
|
||||
"tool": "store_memory",
|
||||
"args": {
|
||||
"content": "To enable focus mode: Cmd+Shift+D on Mac, Ctrl+Shift+D on Windows",
|
||||
"memory_type": "procedural",
|
||||
"tags": ["shortcuts", "focus", "productivity", "cross-platform"]
|
||||
},
|
||||
"note": "Step-by-step instructions with clear embedding"
|
||||
}
|
||||
]
|
||||
|
||||
for example in examples:
|
||||
print(f"📝 {example['tool']}:")
|
||||
print(f" Content: {example['args']['content']}")
|
||||
print(f" Type: {example['args']['memory_type']}")
|
||||
print(f" Tags: {example['args'].get('tags', [])}")
|
||||
print(f" 💡 {example['note']}")
|
||||
print()
|
||||
|
||||
# Example 2: Searching memories with Ollama
|
||||
print("2️⃣ Searching Memories (Semantic + Keyword)")
|
||||
|
||||
search_examples = [
|
||||
{
|
||||
"tool": "search_memories",
|
||||
"args": {
|
||||
"query": "productivity habits and work optimization",
|
||||
"search_type": "semantic",
|
||||
"max_results": 5
|
||||
},
|
||||
"note": "Semantic search excels at understanding intent"
|
||||
},
|
||||
{
|
||||
"tool": "search_memories",
|
||||
"args": {
|
||||
"query": "keyboard shortcuts",
|
||||
"search_type": "keyword"
|
||||
},
|
||||
"note": "Keyword search for exact phrases"
|
||||
},
|
||||
{
|
||||
"tool": "search_memories",
|
||||
"args": {
|
||||
"query": "user interface and visual comfort",
|
||||
"search_type": "semantic",
|
||||
"include_relationships": True
|
||||
},
|
||||
"note": "Includes related memories via graph connections"
|
||||
}
|
||||
]
|
||||
|
||||
for example in search_examples:
|
||||
print(f"🔍 {example['tool']}:")
|
||||
print(f" Query: '{example['args']['query']}'")
|
||||
print(f" Type: {example['args']['search_type']}")
|
||||
print(f" 💡 {example['note']}")
|
||||
print()
|
||||
|
||||
# Example 3: Creating relationships
|
||||
print("3️⃣ Creating Memory Relationships")
|
||||
|
||||
relationship_examples = [
|
||||
{
|
||||
"tool": "create_relationship",
|
||||
"args": {
|
||||
"source_memory_id": "morning_preference_uuid",
|
||||
"target_memory_id": "productivity_boost_uuid",
|
||||
"relationship_type": "causes",
|
||||
"strength": 0.85,
|
||||
"context": "when following natural circadian rhythms"
|
||||
},
|
||||
"note": "Causal relationships help with reasoning"
|
||||
},
|
||||
{
|
||||
"tool": "create_relationship",
|
||||
"args": {
|
||||
"source_memory_id": "eye_strain_concern_uuid",
|
||||
"target_memory_id": "dark_mode_solution_uuid",
|
||||
"relationship_type": "enables",
|
||||
"strength": 0.9,
|
||||
"bidirectional": False
|
||||
},
|
||||
"note": "Solution relationships for problem-solving"
|
||||
},
|
||||
{
|
||||
"tool": "create_relationship",
|
||||
"args": {
|
||||
"source_memory_id": "focus_shortcut_uuid",
|
||||
"target_memory_id": "productivity_tools_uuid",
|
||||
"relationship_type": "part_of",
|
||||
"strength": 0.75,
|
||||
"context": "productivity toolkit"
|
||||
},
|
||||
"note": "Hierarchical relationships for organization"
|
||||
}
|
||||
]
|
||||
|
||||
for example in relationship_examples:
|
||||
print(f"🔗 {example['tool']}:")
|
||||
print(f" Type: {example['args']['relationship_type']}")
|
||||
print(f" Strength: {example['args']['strength']}")
|
||||
print(f" Context: {example['args'].get('context', 'N/A')}")
|
||||
print(f" 💡 {example['note']}")
|
||||
print()
|
||||
|
||||
# Example 4: Graph analysis and monitoring
|
||||
print("4️⃣ Graph Analysis & Ollama Monitoring")
|
||||
|
||||
analysis_examples = [
|
||||
{
|
||||
"tool": "find_connected_memories",
|
||||
"args": {
|
||||
"memory_id": "productivity_uuid",
|
||||
"max_depth": 3,
|
||||
"min_strength": 0.5
|
||||
},
|
||||
"note": "Discover chains of related memories"
|
||||
},
|
||||
{
|
||||
"tool": "analyze_memory_patterns",
|
||||
"args": {},
|
||||
"note": "Overall graph statistics and health"
|
||||
},
|
||||
{
|
||||
"tool": "check_ollama_status",
|
||||
"args": {},
|
||||
"note": "Verify Ollama server and model status"
|
||||
}
|
||||
]
|
||||
|
||||
for example in analysis_examples:
|
||||
print(f"📊 {example['tool']}:")
|
||||
if example['args']:
|
||||
for key, value in example['args'].items():
|
||||
print(f" {key}: {value}")
|
||||
else:
|
||||
print(" No parameters required")
|
||||
print(f" 💡 {example['note']}")
|
||||
print()
|
||||
|
||||
# Example 5: Ollama-specific use cases
|
||||
print("5️⃣ Ollama-Specific Use Cases")
|
||||
|
||||
ollama_use_cases = [
|
||||
{
|
||||
"scenario": "Privacy-First Personal Assistant",
|
||||
"description": "Complete data privacy with local processing",
|
||||
"memories": [
|
||||
"User prefers encrypted communication",
|
||||
"Works with sensitive financial data",
|
||||
"Values privacy over convenience"
|
||||
],
|
||||
"benefits": ["No data sharing", "Offline capable", "User controlled"]
|
||||
},
|
||||
{
|
||||
"scenario": "Enterprise Knowledge Base",
|
||||
"description": "Corporate memory without cloud dependencies",
|
||||
"memories": [
|
||||
"Company coding standards for Python projects",
|
||||
"Internal API documentation and examples",
|
||||
"Team decision history and rationale"
|
||||
],
|
||||
"benefits": ["IP protection", "No subscription costs", "Full control"]
|
||||
},
|
||||
{
|
||||
"scenario": "Research Assistant",
|
||||
"description": "Academic/research memory with complete transparency",
|
||||
"memories": [
|
||||
"Research methodology preferences",
|
||||
"Citation formats and academic standards",
|
||||
"Experiment results and observations"
|
||||
],
|
||||
"benefits": ["Reproducible", "Auditable", "No vendor lock-in"]
|
||||
},
|
||||
{
|
||||
"scenario": "Development Environment Memory",
|
||||
"description": "Code assistant with local-first approach",
|
||||
"memories": [
|
||||
"Project-specific coding patterns",
|
||||
"Bug solutions and workarounds",
|
||||
"Performance optimization techniques"
|
||||
],
|
||||
"benefits": ["Code privacy", "Instant response", "Custom models"]
|
||||
}
|
||||
]
|
||||
|
||||
for use_case in ollama_use_cases:
|
||||
print(f"🎯 {use_case['scenario']}")
|
||||
print(f" {use_case['description']}")
|
||||
print(f" Sample memories:")
|
||||
for memory in use_case['memories']:
|
||||
print(f" • {memory}")
|
||||
print(f" Ollama benefits: {', '.join(use_case['benefits'])}")
|
||||
print()
|
||||
|
||||
# Example 6: Performance considerations
|
||||
print("6️⃣ Ollama Performance Tips")
|
||||
|
||||
performance_tips = [
|
||||
{
|
||||
"tip": "Model Selection",
|
||||
"description": "Choose the right model for your use case",
|
||||
"examples": [
|
||||
"nomic-embed-text: Best balance of quality and speed",
|
||||
"all-minilm: Fastest, lowest memory usage",
|
||||
"mxbai-embed-large: Highest quality, more resources"
|
||||
]
|
||||
},
|
||||
{
|
||||
"tip": "Memory Management",
|
||||
"description": "Optimize for your hardware",
|
||||
"examples": [
|
||||
"Keep Ollama server running to avoid reload overhead",
|
||||
"Monitor RAM usage during peak operations",
|
||||
"Use SSD storage for faster model loading"
|
||||
]
|
||||
},
|
||||
{
|
||||
"tip": "Batch Operations",
|
||||
"description": "Group operations for efficiency",
|
||||
"examples": [
|
||||
"Store multiple memories in sequence",
|
||||
"Batch relationship creation",
|
||||
"Use semantic search for multiple queries"
|
||||
]
|
||||
}
|
||||
]
|
||||
|
||||
for tip in performance_tips:
|
||||
print(f"⚡ {tip['tip']}")
|
||||
print(f" {tip['description']}")
|
||||
for example in tip['examples']:
|
||||
print(f" • {example}")
|
||||
print()
|
||||
|
||||
print("📚 For complete setup instructions: cat OLLAMA_SETUP.md")
|
||||
print("🔧 To test your setup: python test_server.py")
|
||||
print("🚀 To start the server: python memory_mcp_server.py")
|
||||
print("")
|
||||
print("🦙 Enjoy your self-hosted, privacy-first memory system!")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(example_workflow())
|
||||
13
mcp_config_example.json
Normal file
13
mcp_config_example.json
Normal file
@ -0,0 +1,13 @@
|
||||
{
|
||||
"mcpServers": {
|
||||
"memory": {
|
||||
"command": "python",
|
||||
"args": ["/home/rpm/claude/mcp-ultimate-memory/memory_mcp_server.py"],
|
||||
"env": {
|
||||
"KUZU_DB_PATH": "/home/rpm/claude/mcp-ultimate-memory/memory_graph_db",
|
||||
"OLLAMA_BASE_URL": "http://localhost:11434",
|
||||
"OLLAMA_EMBEDDING_MODEL": "nomic-embed-text"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
1125
memory_mcp_server.py
Normal file
1125
memory_mcp_server.py
Normal file
File diff suppressed because it is too large
Load Diff
5
requirements.txt
Normal file
5
requirements.txt
Normal file
@ -0,0 +1,5 @@
|
||||
fastmcp>=2.8.1
|
||||
kuzu>=0.4.0
|
||||
numpy>=1.26.0
|
||||
python-dotenv>=1.0.0
|
||||
requests>=2.28.0
|
||||
145
schema.cypher
Normal file
145
schema.cypher
Normal file
@ -0,0 +1,145 @@
|
||||
-- Ultimate Memory MCP Server - Kuzu Graph Database Schema
|
||||
-- This defines the graph structure for storing memories and their relationships
|
||||
|
||||
-- Node table for Memory nodes
|
||||
CREATE NODE TABLE IF NOT EXISTS Memory (
|
||||
id STRING,
|
||||
content STRING,
|
||||
summary STRING,
|
||||
memory_type STRING, -- episodic, semantic, procedural
|
||||
confidence_score DOUBLE,
|
||||
created_at TIMESTAMP,
|
||||
updated_at TIMESTAMP,
|
||||
last_accessed_at TIMESTAMP,
|
||||
access_count INT64,
|
||||
source_type STRING,
|
||||
source_id STRING,
|
||||
tags STRING[],
|
||||
retrieval_cues STRING[],
|
||||
embedding DOUBLE[], -- Vector embedding for semantic search
|
||||
PRIMARY KEY (id)
|
||||
);
|
||||
|
||||
-- Node table for Conversations
|
||||
CREATE NODE TABLE IF NOT EXISTS Conversation (
|
||||
id STRING,
|
||||
title STRING,
|
||||
started_at TIMESTAMP,
|
||||
last_message_at TIMESTAMP,
|
||||
participant_count INT64,
|
||||
metadata STRING, -- JSON as string
|
||||
PRIMARY KEY (id)
|
||||
);
|
||||
|
||||
-- Node table for Clusters (memory groupings)
|
||||
CREATE NODE TABLE IF NOT EXISTS Cluster (
|
||||
id STRING,
|
||||
name STRING,
|
||||
description STRING,
|
||||
cluster_embedding DOUBLE[],
|
||||
created_at TIMESTAMP,
|
||||
updated_at TIMESTAMP,
|
||||
PRIMARY KEY (id)
|
||||
);
|
||||
|
||||
-- Node table for Topics/Concepts
|
||||
CREATE NODE TABLE IF NOT EXISTS Topic (
|
||||
id STRING,
|
||||
name STRING,
|
||||
description STRING,
|
||||
confidence DOUBLE,
|
||||
PRIMARY KEY (id)
|
||||
);
|
||||
|
||||
-- Relationship table for memory-to-memory connections
|
||||
CREATE REL TABLE IF NOT EXISTS RELATES_TO (
|
||||
FROM Memory TO Memory,
|
||||
relationship_type STRING, -- causes, enables, contradicts, supports, similar_to, etc.
|
||||
strength DOUBLE,
|
||||
context STRING,
|
||||
bidirectional BOOLEAN,
|
||||
created_at TIMESTAMP,
|
||||
created_by STRING, -- system, user, inference
|
||||
confidence DOUBLE
|
||||
);
|
||||
|
||||
-- Relationship table for memory-conversation membership
|
||||
CREATE REL TABLE IF NOT EXISTS BELONGS_TO_CONVERSATION (
|
||||
FROM Memory TO Conversation,
|
||||
sequence_number INT64,
|
||||
created_at TIMESTAMP
|
||||
);
|
||||
|
||||
-- Relationship table for memory-cluster membership
|
||||
CREATE REL TABLE IF NOT EXISTS IN_CLUSTER (
|
||||
FROM Memory TO Cluster,
|
||||
membership_strength DOUBLE,
|
||||
added_at TIMESTAMP
|
||||
);
|
||||
|
||||
-- Relationship table for memory-topic associations
|
||||
CREATE REL TABLE IF NOT EXISTS ABOUT_TOPIC (
|
||||
FROM Memory TO Topic,
|
||||
relevance_score DOUBLE,
|
||||
extracted_at TIMESTAMP
|
||||
);
|
||||
|
||||
-- Relationship table for causal relationships
|
||||
CREATE REL TABLE IF NOT EXISTS CAUSES (
|
||||
FROM Memory TO Memory,
|
||||
causal_strength DOUBLE,
|
||||
mechanism STRING,
|
||||
conditions STRING
|
||||
);
|
||||
|
||||
-- Relationship table for hierarchical relationships
|
||||
CREATE REL TABLE IF NOT EXISTS CONTAINS (
|
||||
FROM Memory TO Memory,
|
||||
containment_type STRING, -- part_of, example_of, instance_of
|
||||
specificity_level INT64
|
||||
);
|
||||
|
||||
-- Example queries for common operations:
|
||||
|
||||
-- 1. Find all memories related to a specific memory with relationship details
|
||||
-- MATCH (m1:Memory {id: $memory_id})-[r:RELATES_TO]->(m2:Memory)
|
||||
-- RETURN m2.id, m2.content, r.relationship_type, r.strength, r.context
|
||||
-- ORDER BY r.strength DESC;
|
||||
|
||||
-- 2. Find conversation memories in chronological order
|
||||
-- MATCH (m:Memory)-[b:BELONGS_TO_CONVERSATION]->(c:Conversation {id: $conversation_id})
|
||||
-- RETURN m.id, m.content, m.memory_type, b.sequence_number
|
||||
-- ORDER BY b.sequence_number;
|
||||
|
||||
-- 3. Find memory paths (graph traversal)
|
||||
-- MATCH path = (start:Memory {id: $start_id})-[:RELATES_TO*1..3]->(end:Memory)
|
||||
-- WHERE ALL(rel in relationships(path) WHERE rel.strength > 0.3)
|
||||
-- RETURN path, length(path) as depth
|
||||
-- ORDER BY depth;
|
||||
|
||||
-- 4. Find memories by topic
|
||||
-- MATCH (m:Memory)-[a:ABOUT_TOPIC]->(t:Topic {name: $topic_name})
|
||||
-- RETURN m.id, m.content, a.relevance_score
|
||||
-- ORDER BY a.relevance_score DESC;
|
||||
|
||||
-- 5. Find clusters and their member memories
|
||||
-- MATCH (m:Memory)-[ic:IN_CLUSTER]->(c:Cluster)
|
||||
-- RETURN c.name, c.description, collect(m.content) as memories
|
||||
-- ORDER BY c.name;
|
||||
|
||||
-- 6. Find causal chains
|
||||
-- MATCH path = (cause:Memory)-[:CAUSES*1..4]->(effect:Memory)
|
||||
-- RETURN path, nodes(path) as causal_chain, length(path) as chain_length
|
||||
-- ORDER BY chain_length;
|
||||
|
||||
-- 7. Temporal memory sequences
|
||||
-- MATCH (m1:Memory)-[r:RELATES_TO]->(m2:Memory)
|
||||
-- WHERE r.relationship_type = 'precedes'
|
||||
-- RETURN m1.content, m2.content, r.strength
|
||||
-- ORDER BY r.strength DESC;
|
||||
|
||||
-- 8. Most connected memories (centrality analysis)
|
||||
-- MATCH (m:Memory)-[r:RELATES_TO]-()
|
||||
-- RETURN m.id, m.content, count(r) as connection_count
|
||||
-- ORDER BY connection_count DESC
|
||||
-- LIMIT 10;
|
||||
164
setup.sh
Executable file
164
setup.sh
Executable file
@ -0,0 +1,164 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Ultimate Memory MCP Server - Ollama Edition Setup Script
|
||||
# Self-hosted embeddings with complete privacy and control
|
||||
|
||||
set -e
|
||||
|
||||
echo "🦙 Setting up Ultimate Memory MCP Server - Ollama Edition..."
|
||||
|
||||
# Check Python version
|
||||
python_version=$(python3 --version 2>&1 | awk '{print $2}' | cut -d. -f1,2)
|
||||
required_version="3.11"
|
||||
|
||||
if [ "$(printf '%s\n' "$required_version" "$python_version" | sort -V | head -n1)" != "$required_version" ]; then
|
||||
echo "❌ Python 3.11+ is required. You have Python $python_version"
|
||||
echo "Please upgrade Python and try again."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "✅ Python $python_version detected"
|
||||
|
||||
# Install dependencies
|
||||
echo "📦 Installing dependencies..."
|
||||
pip install -r requirements.txt
|
||||
|
||||
# Check if Ollama is installed
|
||||
echo "🔍 Checking for Ollama installation..."
|
||||
if command -v ollama &> /dev/null; then
|
||||
echo "✅ Ollama is installed"
|
||||
ollama_version=$(ollama --version 2>&1 | head -n1)
|
||||
echo " Version: $ollama_version"
|
||||
else
|
||||
echo "❌ Ollama not found"
|
||||
echo ""
|
||||
echo "📥 Please install Ollama:"
|
||||
echo " Linux/macOS: curl -fsSL https://ollama.ai/install.sh | sh"
|
||||
echo " Or download from: https://ollama.ai/"
|
||||
echo ""
|
||||
read -p "Continue setup without Ollama? (y/N): " continue_setup
|
||||
if [[ ! $continue_setup =~ ^[Yy]$ ]]; then
|
||||
echo "Please install Ollama and run setup again."
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
|
||||
# Check if .env exists
|
||||
if [ ! -f .env ]; then
|
||||
echo "⚙️ Creating environment configuration..."
|
||||
cp .env.example .env
|
||||
echo "✅ Created .env file with default settings"
|
||||
else
|
||||
echo "✅ Environment file already exists"
|
||||
fi
|
||||
|
||||
# Test Ollama connection if available
|
||||
if command -v ollama &> /dev/null; then
|
||||
echo ""
|
||||
echo "🧪 Testing Ollama setup..."
|
||||
|
||||
# Check if Ollama server is running
|
||||
if curl -s http://localhost:11434/api/tags > /dev/null 2>&1; then
|
||||
echo "✅ Ollama server is running"
|
||||
|
||||
# Check for required model
|
||||
model_name="nomic-embed-text"
|
||||
if ollama list | grep -q "$model_name"; then
|
||||
echo "✅ Embedding model '$model_name' is available"
|
||||
else
|
||||
echo "❌ Embedding model '$model_name' not found"
|
||||
echo ""
|
||||
read -p "Download the embedding model now? (Y/n): " download_model
|
||||
if [[ ! $download_model =~ ^[Nn]$ ]]; then
|
||||
echo "📥 Downloading $model_name..."
|
||||
if ollama pull $model_name; then
|
||||
echo "✅ Model downloaded successfully"
|
||||
else
|
||||
echo "❌ Failed to download model"
|
||||
fi
|
||||
fi
|
||||
fi
|
||||
|
||||
# Optional: Check for summary model
|
||||
summary_model="llama3.2:1b"
|
||||
if ollama list | grep -q "$summary_model"; then
|
||||
echo "✅ Summary model '$summary_model' is available"
|
||||
else
|
||||
echo "ℹ️ Summary model '$summary_model' not found (optional)"
|
||||
read -p "Download the summary model? (y/N): " download_summary
|
||||
if [[ $download_summary =~ ^[Yy]$ ]]; then
|
||||
echo "📥 Downloading $summary_model..."
|
||||
ollama pull $summary_model
|
||||
fi
|
||||
fi
|
||||
|
||||
else
|
||||
echo "❌ Ollama server is not running"
|
||||
echo ""
|
||||
echo "🚀 To start Ollama server:"
|
||||
echo " ollama serve"
|
||||
echo ""
|
||||
echo " Then in another terminal:"
|
||||
echo " ollama pull nomic-embed-text"
|
||||
echo ""
|
||||
fi
|
||||
fi
|
||||
|
||||
# Create database directory
|
||||
mkdir -p memory_graph_db
|
||||
echo "✅ Created database directory"
|
||||
|
||||
# Show current configuration
|
||||
echo ""
|
||||
echo "📋 Configuration Summary:"
|
||||
if [ -f .env ]; then
|
||||
base_url=$(grep "OLLAMA_BASE_URL=" .env | cut -d= -f2)
|
||||
model=$(grep "OLLAMA_EMBEDDING_MODEL=" .env | cut -d= -f2)
|
||||
db_path=$(grep "KUZU_DB_PATH=" .env | cut -d= -f2)
|
||||
|
||||
echo " Database: $db_path"
|
||||
echo " Ollama URL: $base_url"
|
||||
echo " Embedding Model: $model"
|
||||
fi
|
||||
|
||||
# Test the setup
|
||||
echo ""
|
||||
echo "🧪 Running tests..."
|
||||
|
||||
# Test Ollama connection first
|
||||
echo "Testing Ollama connection..."
|
||||
if python test_server.py --connection-only; then
|
||||
echo ""
|
||||
echo "Testing memory server functionality..."
|
||||
python test_server.py
|
||||
else
|
||||
echo ""
|
||||
echo "❌ Ollama connection test failed."
|
||||
echo "Please check your Ollama setup and try again."
|
||||
echo ""
|
||||
echo "🔧 Troubleshooting:"
|
||||
echo "1. Start Ollama: ollama serve"
|
||||
echo "2. Install model: ollama pull nomic-embed-text"
|
||||
echo "3. Check status: curl http://localhost:11434/api/tags"
|
||||
echo "4. Run: python test_server.py --help-setup"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "🎉 Setup complete!"
|
||||
echo ""
|
||||
echo "🚀 Next steps:"
|
||||
echo "1. Keep Ollama running: ollama serve (in background)"
|
||||
echo "2. Start the memory server: python memory_mcp_server.py"
|
||||
echo "3. Configure your MCP client (see mcp_config_example.json)"
|
||||
echo ""
|
||||
echo "💡 Ollama Tips:"
|
||||
echo " - Server uses ~1.5GB RAM for nomic-embed-text"
|
||||
echo " - First embedding generation may be slower (model loading)"
|
||||
echo " - All processing happens locally (complete privacy)"
|
||||
echo " - No API costs or rate limits"
|
||||
echo ""
|
||||
echo "📚 For detailed docs: cat README.md"
|
||||
echo "🔧 For troubleshooting: python test_server.py --help-setup"
|
||||
echo ""
|
||||
echo "🦙 Enjoy your self-hosted memory system!"
|
||||
288
test_server.py
Normal file
288
test_server.py
Normal file
@ -0,0 +1,288 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script for the Ultimate Memory MCP Server - Ollama Edition
|
||||
Run this to verify the server is working correctly with Ollama.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
import requests
|
||||
from pathlib import Path
|
||||
|
||||
# Add the project root to Python path
|
||||
project_root = Path(__file__).parent
|
||||
sys.path.insert(0, str(project_root))
|
||||
|
||||
from memory_mcp_server import MemoryMCPServer, MemoryType, OllamaProvider
|
||||
|
||||
async def test_ollama_connection():
|
||||
"""Test Ollama server connection and model availability"""
|
||||
print("🦙 Testing Ollama connection...")
|
||||
|
||||
base_url = os.getenv('OLLAMA_BASE_URL', 'http://localhost:11434')
|
||||
model = os.getenv('OLLAMA_EMBEDDING_MODEL', 'nomic-embed-text')
|
||||
|
||||
print(f"📡 Server: {base_url}")
|
||||
print(f"🎯 Model: {model}")
|
||||
|
||||
try:
|
||||
# Test server connection
|
||||
print("🔌 Checking server connection...")
|
||||
response = requests.get(f"{base_url}/api/tags", timeout=10)
|
||||
|
||||
if response.status_code == 200:
|
||||
print("✅ Ollama server is running")
|
||||
|
||||
# Check available models
|
||||
data = response.json()
|
||||
models = [m['name'] for m in data.get('models', [])]
|
||||
print(f"📦 Available models: {models}")
|
||||
|
||||
if model in models:
|
||||
print(f"✅ Embedding model '{model}' is available")
|
||||
else:
|
||||
print(f"❌ Embedding model '{model}' not found")
|
||||
print(f"💡 To install it, run: ollama pull {model}")
|
||||
return False
|
||||
|
||||
# Test embedding generation
|
||||
print(f"🧪 Testing embedding generation...")
|
||||
embed_response = requests.post(
|
||||
f"{base_url}/api/embeddings",
|
||||
json={"model": model, "prompt": "test embedding"},
|
||||
timeout=30
|
||||
)
|
||||
|
||||
if embed_response.status_code == 200:
|
||||
embedding = embed_response.json()["embedding"]
|
||||
print(f"✅ Successfully generated embedding ({len(embedding)} dimensions)")
|
||||
print(f" First few values: {embedding[:5]}")
|
||||
return True
|
||||
else:
|
||||
print(f"❌ Embedding test failed: {embed_response.status_code}")
|
||||
print(f" Response: {embed_response.text}")
|
||||
return False
|
||||
|
||||
else:
|
||||
print(f"❌ Ollama server not responding: {response.status_code}")
|
||||
return False
|
||||
|
||||
except requests.exceptions.ConnectionError:
|
||||
print(f"❌ Cannot connect to Ollama server at {base_url}")
|
||||
print("💡 Make sure Ollama is running: ollama serve")
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"❌ Ollama test failed: {e}")
|
||||
return False
|
||||
|
||||
async def test_ollama_provider():
|
||||
"""Test the OllamaProvider class directly"""
|
||||
print("\n🔧 Testing OllamaProvider class...")
|
||||
|
||||
base_url = os.getenv('OLLAMA_BASE_URL', 'http://localhost:11434')
|
||||
model = os.getenv('OLLAMA_EMBEDDING_MODEL', 'nomic-embed-text')
|
||||
|
||||
try:
|
||||
provider = OllamaProvider(base_url, model)
|
||||
|
||||
# Test connection check
|
||||
connected, message = provider.check_connection()
|
||||
print(f"📊 Connection check: {'✅' if connected else '❌'} {message}")
|
||||
|
||||
if not connected:
|
||||
return False
|
||||
|
||||
# Test embedding generation
|
||||
print("🔢 Testing embedding generation...")
|
||||
embedding = await provider.generate_embedding("This is a test sentence for embedding generation")
|
||||
print(f"✅ Generated embedding with {len(embedding)} dimensions")
|
||||
print(f" First few values: {embedding[:5]}")
|
||||
|
||||
# Test summary generation
|
||||
print("📝 Testing summary generation...")
|
||||
long_text = (
|
||||
"This is a longer piece of text that should be summarized. "
|
||||
"It contains multiple sentences and ideas that need to be condensed "
|
||||
"into a shorter, more manageable summary for storage and retrieval. "
|
||||
"The summary should capture the key points while being concise."
|
||||
)
|
||||
summary = await provider.generate_summary(long_text)
|
||||
print(f"✅ Generated summary: {summary}")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Provider test failed: {e}")
|
||||
return False
|
||||
|
||||
async def test_memory_server():
|
||||
"""Test the full memory server functionality"""
|
||||
print("\n🧠 Testing Ultimate Memory MCP Server with Ollama...")
|
||||
|
||||
# Configuration
|
||||
test_db_path = "./test_memory_db"
|
||||
base_url = os.getenv('OLLAMA_BASE_URL', 'http://localhost:11434')
|
||||
model = os.getenv('OLLAMA_EMBEDDING_MODEL', 'nomic-embed-text')
|
||||
|
||||
try:
|
||||
provider = OllamaProvider(base_url, model)
|
||||
|
||||
# Check connection first
|
||||
connected, message = provider.check_connection()
|
||||
if not connected:
|
||||
print(f"❌ Ollama not available: {message}")
|
||||
print("\nPlease ensure:")
|
||||
print("1. Ollama is running: ollama serve")
|
||||
print(f"2. Model is installed: ollama pull {model}")
|
||||
print(f"3. Server is accessible at: {base_url}")
|
||||
return
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Failed to create Ollama provider: {e}")
|
||||
return
|
||||
|
||||
# Initialize server
|
||||
server = MemoryMCPServer(test_db_path, provider)
|
||||
|
||||
try:
|
||||
print("📊 Initializing database...")
|
||||
await server.initialize_db()
|
||||
print("✅ Database initialized successfully")
|
||||
|
||||
print("\n💾 Testing memory storage...")
|
||||
|
||||
# Test storing different types of memories
|
||||
episodic_id = await server.store_memory(
|
||||
content="User clicked the save button at 2:30 PM during the demo",
|
||||
memory_type=MemoryType.EPISODIC,
|
||||
tags=["user-action", "demo", "save"],
|
||||
conversation_id="test_conversation"
|
||||
)
|
||||
print(f"✅ Stored episodic memory: {episodic_id}")
|
||||
|
||||
semantic_id = await server.store_memory(
|
||||
content="User prefers dark mode interfaces for better eye comfort",
|
||||
memory_type=MemoryType.SEMANTIC,
|
||||
tags=["preference", "ui", "accessibility"]
|
||||
)
|
||||
print(f"✅ Stored semantic memory: {semantic_id}")
|
||||
|
||||
procedural_id = await server.store_memory(
|
||||
content="To enable dark mode: Settings → Appearance → Theme → Dark",
|
||||
memory_type=MemoryType.PROCEDURAL,
|
||||
tags=["instructions", "ui", "settings"]
|
||||
)
|
||||
print(f"✅ Stored procedural memory: {procedural_id}")
|
||||
|
||||
print("\n🔍 Testing semantic search...")
|
||||
search_results = await server.search_memories_semantic(
|
||||
query="user interface preferences",
|
||||
max_results=5,
|
||||
similarity_threshold=0.3
|
||||
)
|
||||
print(f"✅ Found {len(search_results)} memories matching 'user interface preferences'")
|
||||
|
||||
for i, result in enumerate(search_results, 1):
|
||||
print(f" {i}. Score: {result.similarity_score:.3f} - {result.content[:60]}...")
|
||||
|
||||
print("\n🔗 Testing relationship creation...")
|
||||
relationship_id = await server.create_relationship(
|
||||
source_memory_id=semantic_id,
|
||||
target_memory_id=procedural_id,
|
||||
relationship_type="enables",
|
||||
strength=0.9,
|
||||
context="when user wants to implement their preference"
|
||||
)
|
||||
print(f"✅ Created relationship: {relationship_id}")
|
||||
|
||||
print("\n🕸️ Testing connected memories...")
|
||||
connected = await server.find_connected_memories(
|
||||
memory_id=semantic_id,
|
||||
max_depth=2,
|
||||
min_strength=0.5
|
||||
)
|
||||
print(f"✅ Found {len(connected)} connected memories")
|
||||
|
||||
for conn in connected:
|
||||
print(f" Depth {conn['depth']}: {conn['content'][:60]}...")
|
||||
|
||||
print("\n📝 Testing memory retrieval...")
|
||||
retrieved_memory = await server.get_memory_by_id(episodic_id)
|
||||
if retrieved_memory:
|
||||
print(f"✅ Retrieved memory: {retrieved_memory.content[:60]}...")
|
||||
print(f" Type: {retrieved_memory.memory_type.value}")
|
||||
print(f" Access count: {retrieved_memory.access_count}")
|
||||
|
||||
print("\n💬 Testing conversation memories...")
|
||||
conv_memories = await server.get_conversation_memories("test_conversation")
|
||||
print(f"✅ Found {len(conv_memories)} memories in conversation")
|
||||
|
||||
print("\n📊 Testing keyword search...")
|
||||
keyword_results = await server.search_memories_by_keywords(
|
||||
query="dark mode",
|
||||
max_results=5
|
||||
)
|
||||
print(f"✅ Found {len(keyword_results)} memories matching 'dark mode'")
|
||||
|
||||
print("\n🎉 All tests passed successfully!")
|
||||
print(f"\nMemory server is ready for use with Ollama ({model}).")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Test failed: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
finally:
|
||||
server.close_db()
|
||||
|
||||
# Clean up test database
|
||||
import shutil
|
||||
if Path(test_db_path).exists():
|
||||
shutil.rmtree(test_db_path)
|
||||
print(f"🧹 Cleaned up test database: {test_db_path}")
|
||||
|
||||
def print_ollama_help():
|
||||
"""Print help for setting up Ollama"""
|
||||
print("\n📚 Ollama Setup Help")
|
||||
print("=" * 50)
|
||||
|
||||
base_url = os.getenv('OLLAMA_BASE_URL', 'http://localhost:11434')
|
||||
model = os.getenv('OLLAMA_EMBEDDING_MODEL', 'nomic-embed-text')
|
||||
|
||||
print("🦙 Ollama Setup Steps:")
|
||||
print("1. Install Ollama: https://ollama.ai/")
|
||||
print("2. Start the server: ollama serve")
|
||||
print(f"3. Pull the embedding model: ollama pull {model}")
|
||||
print("4. Optional: Pull a chat model for summaries: ollama pull llama3.2:1b")
|
||||
print()
|
||||
print(f"Current configuration:")
|
||||
print(f" Server URL: {base_url}")
|
||||
print(f" Embedding Model: {model}")
|
||||
print()
|
||||
print("Test commands:")
|
||||
print(f" curl {base_url}/api/tags")
|
||||
print(f" ollama list")
|
||||
print(f" python test_server.py --connection-only")
|
||||
|
||||
if __name__ == "__main__":
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description="Test Ultimate Memory MCP Server - Ollama Edition")
|
||||
parser.add_argument("--connection-only", action="store_true",
|
||||
help="Test only Ollama connection")
|
||||
parser.add_argument("--provider-only", action="store_true",
|
||||
help="Test only the OllamaProvider class")
|
||||
parser.add_argument("--help-setup", action="store_true",
|
||||
help="Show Ollama setup help")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.help_setup:
|
||||
print_ollama_help()
|
||||
elif args.connection_only:
|
||||
asyncio.run(test_ollama_connection())
|
||||
elif args.provider_only:
|
||||
asyncio.run(test_ollama_provider())
|
||||
else:
|
||||
asyncio.run(test_memory_server())
|
||||
Loading…
x
Reference in New Issue
Block a user