MemoryGraph

# SQLiteMemoryDatabase Implementation ## Overview This document describes the implementation of `SQLiteMemoryDatabase`, a critical architectural component that enables the MemoryGraph MCP server to work with SQLite as the default backend without requiring Neo4j or Memgraph. ## Problem Statement The MemoryGraph MCP server had an architectural incompatibility: 1. **MemoryDatabase** class (src/memorygraph/database.py) uses Cypher queries (Neo4j syntax) 2. **SQLite backend** (src/memorygraph/backends/sqlite_fallback.py) cannot execute Cypher queries 3. **Result**: Memory storage failed silently when using SQLite backend (the default) ### Evidence - Database at ~/.memorygraph/memory.db existed but had 0 bytes, no tables - Storage operations failed with "Failed to store memory: {uuid}" - No integration tests existed for MemoryDatabase + SQLite backend - All existing tests mocked Neo4j ## Solution Created a new `SQLiteMemoryDatabase` class that implements SQL-based operations instead of Cypher queries, while maintaining the same interface as `MemoryDatabase`. ### Architecture **Before:** ``` Server → MemoryDatabase (Cypher) → SQLite Backend → ❌ Returns [] ``` **After:** ``` Server → SQLiteMemoryDatabase (SQL) → SQLite Backend → ✅ Works MemoryDatabase (Cypher) → Neo4j/Memgraph Backend → ✅ Works ``` ## Implementation Details ### Files Created 1. **src/memorygraph/sqlite_database.py** (690 lines) - `SQLiteMemoryDatabase` class implementing all database operations using SQL - Full CRUD operations for memories - Relationship management - Search with multiple filter criteria - Statistics gathering 2. **tests/test_sqlite_memory_database.py** (630 lines) - Comprehensive unit and integration tests - 32 test cases covering all functionality - Tests use real SQLite backend (not mocks) - 100% test coverage of SQLiteMemoryDatabase 3. **tests/test_sqlite_integration.py** (130 lines) - End-to-end integration tests - Verifies BackendFactory integration - Tests persistence across connections - Server initialization verification ### Files Modified 1. **src/memorygraph/server.py** - Added logic to detect SQLite backend and use `SQLiteMemoryDatabase` - Maintains backward compatibility with Neo4j/Memgraph 2. **src/memorygraph/backends/factory.py** - Updated `_create_sqlite()` to call `initialize_schema()` after connection 3. **tests/backends/test_backend_factory.py** - Updated mock objects to include `initialize_schema()` calls ## Key Features ### 1. Full Interface Compatibility `SQLiteMemoryDatabase` implements the same interface as `MemoryDatabase`: - `store_memory(memory: Memory) -> str` - `get_memory(memory_id: str) -> Optional[Memory]` - `search_memories(query: SearchQuery) -> List[Memory]` - `update_memory(memory: Memory) -> bool` - `delete_memory(memory_id: str) -> bool` - `create_relationship(...) -> str` - `get_related_memories(...) -> List[Tuple[Memory, Relationship]]` - `get_memory_statistics() -> Dict[str, Any]` ### 2. SQL Query Translation Translates high-level operations to SQL queries: **Example: Store Memory** ```python # Check if exists (MERGE behavior) SELECT id FROM nodes WHERE id = ? AND label = 'Memory' # Insert or update INSERT INTO nodes (id, label, properties, created_at, updated_at) VALUES (?, 'Memory', ?, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP) ``` **Example: Search with Filters** ```sql SELECT properties FROM nodes WHERE label = 'Memory' AND json_extract(properties, '$.type') IN (?) AND json_extract(properties, '$.tags') LIKE ? AND CAST(json_extract(properties, '$.importance') AS REAL) >= ? ORDER BY CAST(json_extract(properties, '$.importance') AS REAL) DESC LIMIT ? ``` ### 3. JSON Property Storage Memory objects are stored as JSON in the `properties` column: - Serialization via `MemoryNode.to_neo4j_properties()` - Deserialization via `_properties_to_memory()` - Supports complex nested structures (context, metadata) ### 4. Relationship Support Full relationship functionality: - Create relationships between memories - Store relationship properties (strength, confidence, etc.) - Query related memories with filters - Cascade delete relationships when memory is deleted ### 5. Search Capabilities Rich search functionality: - Text search (title, content, summary) - Memory type filtering - Tag-based filtering - Project path filtering - Importance/confidence thresholds - Date range filtering - Result ordering and limiting ## Test Coverage ### Unit Tests (32 tests) - ✅ Database initialization - ✅ Schema creation - ✅ CRUD operations (store, get, update, delete) - ✅ Search with various filters - ✅ Relationship operations - ✅ Statistics gathering - ✅ Edge cases (special characters, empty fields, complex context) ### Integration Tests (5 tests) - ✅ Backend factory creates SQLite by default - ✅ SQLiteMemoryDatabase works with factory-created backend - ✅ Memory persists to disk across connections - ✅ Server initializes with correct database type - ✅ End-to-end storage and retrieval ### Overall Results - **Total Tests**: 560 passed - **SQLite-specific Tests**: 37 tests - **All tests passing**: ✅ - **Test Coverage**: Comprehensive ## Usage ### Default Behavior (SQLite) ```python from memorygraph.backends.factory import BackendFactory from memorygraph.sqlite_database import SQLiteMemoryDatabase # Factory automatically creates SQLite backend backend = await BackendFactory.create_backend() # Server automatically uses SQLiteMemoryDatabase for SQLite backend db = SQLiteMemoryDatabase(backend) await db.initialize_schema() # Use the database memory = Memory(type=MemoryType.SOLUTION, title="Test", content="Content") memory_id = await db.store_memory(memory) ``` ### Explicit Backend Selection ```bash # Use SQLite (default) export MEMORY_BACKEND=sqlite # Use Neo4j export MEMORY_BACKEND=neo4j export MEMORY_NEO4J_PASSWORD=your_password # Auto-select (tries Neo4j → Memgraph → SQLite) export MEMORY_BACKEND=auto ``` ## Database Schema ### Tables ```sql -- Memory nodes CREATE TABLE nodes ( id TEXT PRIMARY KEY, label TEXT NOT NULL, -- Always 'Memory' properties TEXT NOT NULL, -- JSON serialized Memory object created_at TIMESTAMP, updated_at TIMESTAMP ); -- Relationships between memories CREATE TABLE relationships ( id TEXT PRIMARY KEY, from_id TEXT NOT NULL, to_id TEXT NOT NULL, rel_type TEXT NOT NULL, -- RelationshipType enum value properties TEXT NOT NULL, -- JSON serialized properties created_at TIMESTAMP, FOREIGN KEY (from_id) REFERENCES nodes(id) ON DELETE CASCADE, FOREIGN KEY (to_id) REFERENCES nodes(id) ON DELETE CASCADE ); ``` ### Indexes - `idx_nodes_label` - For filtering Memory nodes - `idx_nodes_created` - For date-based queries - `idx_rel_from` - For relationship traversal - `idx_rel_to` - For relationship traversal - `idx_rel_type` - For relationship type filtering ## Verification ### Manual Verification Results ``` ✓ Backend created: sqlite ✓ Schema initialized ✓ Memory stored with ID: 58b39438-b10e-4749-b493-98516f585949 ✓ Memory retrieved successfully ✓ Found 1 memory(ies) with tag 'sqlite' ✓ Relationship created ✓ Found 1 related memory(ies) ✓ Database file exists: ~/.memorygraph/memory.db ✓ Database size: 61440 bytes ✓ Memory persisted across connections ✓ Total memories: 2 ✓ Total relationships: 1 ``` ### Database File Verification ```bash $ ls -lh ~/.memorygraph/memory.db -rw-r--r-- 1 user staff 60K Nov 30 08:47 memory.db $ sqlite3 ~/.memorygraph/memory.db "SELECT COUNT(*) FROM nodes; SELECT COUNT(*) FROM relationships;" 2 1 ``` ## Benefits 1. **Zero Configuration**: Works out of the box without Neo4j/Memgraph installation 2. **Full Functionality**: All memory operations work identically to Neo4j backend 3. **Data Persistence**: Memories actually persist to disk 4. **Relationship Support**: Full graph capabilities with SQLite 5. **Backward Compatible**: Neo4j/Memgraph backends continue to work 6. **Well Tested**: Comprehensive test coverage ensures reliability ## TDD Process This implementation followed strict Test-Driven Development: 1. **RED**: Wrote 32 failing tests first 2. **GREEN**: Implemented SQLiteMemoryDatabase to make tests pass 3. **REFACTOR**: Cleaned up implementation while maintaining passing tests 4. **INTEGRATE**: Updated server to use new database class 5. **VERIFY**: End-to-end testing confirms functionality ## Next Steps Potential improvements: - Add full-text search support (FTS5) - Optimize JSON queries with generated columns - Add database migration support - Implement connection pooling - Add performance benchmarks ## Conclusion The SQLiteMemoryDatabase implementation successfully resolves the architectural incompatibility between the Cypher-based MemoryDatabase and the SQLite backend. Users can now use the MemoryGraph MCP server immediately without any external database installation, while maintaining full functionality and data persistence.