# Ultimate Memory MCP Server - Ollama Edition Structure

```
mcp-ultimate-memory/
├── memory_mcp_server.py       # 🦙 Main Ollama-powered server (841 lines)
├── requirements.txt           # 📦 Minimal dependencies (no OpenAI)
├── .env.example              # ⚙️ Ollama-focused configuration
├── schema.cypher             # 🕸️ Kuzu graph database schema
├── setup.sh                  # 🚀 Ollama-specific setup script
├── test_server.py            # 🧪 Ollama-focused test suite
├── examples.py               # 📚 Ollama usage examples & patterns
├── mcp_config_example.json   # 🔧 MCP client configuration
├── README.md                 # 📖 Ollama-focused documentation
├── OLLAMA_SETUP.md           # 🦙 Detailed Ollama setup guide
└── PROJECT_STRUCTURE.md      # 📋 This file
```

## File Descriptions

### Core Server Files

- **`memory_mcp_server.py`** - FastMCP server with OllamaProvider integration
- **`schema.cypher`** - Kuzu graph database schema (unchanged)
- **`requirements.txt`** - Minimal dependencies (fastmcp, kuzu, numpy, requests)

### Configuration & Setup

- **`.env.example`** - Ollama-focused environment variables
- **`setup.sh`** - Interactive Ollama setup with model downloading
- **`mcp_config_example.json`** - MCP client configuration for Ollama

### Testing & Examples

- **`test_server.py`** - Comprehensive Ollama testing suite
- **`examples.py`** - Ollama-specific usage patterns and tips

### Documentation

- **`README.md`** - Complete Ollama-focused documentation
- **`OLLAMA_SETUP.md`** - Detailed Ollama installation and configuration guide

## Key Changes from Multi-Provider Version

### Removed Components
- ❌ OpenAI provider class and dependencies
- ❌ Sentence Transformers provider
- ❌ Provider factory pattern
- ❌ Multi-provider configuration options
- ❌ OpenAI-specific documentation

### Simplified Architecture
- ✅ Single `OllamaProvider` class
- ✅ Direct integration with memory server
- ✅ Simplified configuration (only Ollama settings)
- ✅ Streamlined error handling
- ✅ Focused testing and setup

### Enhanced Ollama Features
- ✅ Connection health checking
- ✅ Model availability verification
- ✅ Server status monitoring tool
- ✅ Ollama-specific troubleshooting
- ✅ Performance optimization tips

## Quick Commands

```bash
# Complete setup (interactive)
./setup.sh

# Test Ollama connection only
python test_server.py --connection-only

# Test full system
python test_server.py

# View examples and patterns
python examples.py

# Start the server
python memory_mcp_server.py
```

## Configuration Files

### `.env` Configuration
```env
KUZU_DB_PATH=./memory_graph_db
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"
      }
    }
  }
}
```

## Dependencies

### Required Python Packages
```
fastmcp>=2.8.1      # MCP framework
kuzu>=0.4.0         # Graph database
numpy>=1.26.0       # Vector operations
python-dotenv>=1.0.0 # Environment loading
requests>=2.28.0    # HTTP requests to Ollama
```

### System Requirements
- **Python 3.11+** (for modern type hints)
- **Ollama** (latest version from ollama.ai)
- **nomic-embed-text model** (or alternative)

### Optional Components
- **llama3.2:1b model** (for AI summaries)
- **systemd** (for service deployment)

## Database Structure

The Kuzu graph database creates:
- **Memory nodes** with embeddings from Ollama
- **Relationship edges** with metadata and strengths  
- **Conversation nodes** for context grouping
- **Topic and Cluster nodes** for organization

See `schema.cypher` for complete schema definition.

## Performance Characteristics

### Ollama-Specific Performance
- **First Request**: ~2-3 seconds (model loading)
- **Subsequent Requests**: ~500-800ms per embedding
- **Memory Usage**: ~1.5GB RAM for nomic-embed-text
- **Storage**: ~2GB for models and database

### Optimization Features
- ✅ Connection pooling and reuse
- ✅ 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.