Nextcloud MCP Server

"""Integration tests for Qdrant collection auto-creation. These tests validate that: 1. Collections are automatically created on first access 2. Dimension validation detects mismatches 3. Idempotent initialization (multiple calls don't fail) 4. Proper error handling and logging """ from unittest.mock import Mock import pytest from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client pytestmark = pytest.mark.integration @pytest.fixture(autouse=True) async def reset_singleton(): """Reset the global Qdrant client singleton between tests.""" global _qdrant_client import nextcloud_mcp_server.vector.qdrant_client as qdrant_module # Store original original = qdrant_module._qdrant_client # Reset for test qdrant_module._qdrant_client = None yield # Restore original qdrant_module._qdrant_client = original @pytest.mark.integration async def test_collection_auto_created_on_first_access(monkeypatch): """Test that collection is automatically created if it doesn't exist.""" # Mock settings from nextcloud_mcp_server.config import Settings mock_settings = Settings( qdrant_location=":memory:", ollama_embedding_model="nomic-embed-text", vector_sync_enabled=False, # Disable background sync for test ) monkeypatch.setattr( "nextcloud_mcp_server.vector.qdrant_client.get_settings", lambda: mock_settings ) # Mock embedding service - must have .provider attribute from nextcloud_mcp_server.embedding import SimpleEmbeddingProvider mock_provider = SimpleEmbeddingProvider(dimension=384) mock_embedding_service = Mock() mock_embedding_service.provider = mock_provider mock_embedding_service.get_dimension = lambda: mock_provider.get_dimension() monkeypatch.setattr( "nextcloud_mcp_server.embedding.get_embedding_service", lambda: mock_embedding_service, ) # Get client (should trigger collection creation) client = await get_qdrant_client() # Verify client is initialized assert client is not None # Verify collection was created collection_name = mock_settings.get_collection_name() collections = await client.get_collections() collection_names = [c.name for c in collections.collections] assert collection_name in collection_names # Verify collection has correct dimensions collection_info = await client.get_collection(collection_name) assert collection_info.config.params.vectors.size == 384 @pytest.mark.integration async def test_existing_collection_reused(monkeypatch): """Test that existing collection is reused without error.""" # Mock settings from nextcloud_mcp_server.config import Settings mock_settings = Settings( qdrant_location=":memory:", ollama_embedding_model="nomic-embed-text", vector_sync_enabled=False, ) monkeypatch.setattr( "nextcloud_mcp_server.vector.qdrant_client.get_settings", lambda: mock_settings ) # Mock embedding service - must have .provider attribute from nextcloud_mcp_server.embedding import SimpleEmbeddingProvider mock_provider = SimpleEmbeddingProvider(dimension=384) mock_embedding_service = Mock() mock_embedding_service.provider = mock_provider mock_embedding_service.get_dimension = lambda: mock_provider.get_dimension() monkeypatch.setattr( "nextcloud_mcp_server.embedding.get_embedding_service", lambda: mock_embedding_service, ) # First call - creates collection _ = await get_qdrant_client() collection_name = mock_settings.get_collection_name() # Reset singleton to simulate second initialization import nextcloud_mcp_server.vector.qdrant_client as qdrant_module qdrant_module._qdrant_client = None # Second call - should reuse existing collection client2 = await get_qdrant_client() # Verify both clients work assert client2 is not None # Verify collection still exists and wasn't recreated collections = await client2.get_collections() collection_names = [c.name for c in collections.collections] assert collection_name in collection_names # Verify dimensions unchanged collection_info = await client2.get_collection(collection_name) assert collection_info.config.params.vectors.size == 384 @pytest.mark.integration async def test_dimension_mismatch_detected(monkeypatch, tmp_path): """Test that dimension mismatch raises clear error.""" # Use persistent temp directory so collection survives client reset from nextcloud_mcp_server.config import Settings qdrant_path = str(tmp_path / "qdrant_data") mock_settings = Settings( qdrant_location=qdrant_path, ollama_embedding_model="nomic-embed-text", vector_sync_enabled=False, ) monkeypatch.setattr( "nextcloud_mcp_server.vector.qdrant_client.get_settings", lambda: mock_settings ) # First embedding service: 384 dimensions from nextcloud_mcp_server.embedding import SimpleEmbeddingProvider mock_provider_1 = SimpleEmbeddingProvider(dimension=384) mock_embedding_service_1 = Mock() mock_embedding_service_1.provider = mock_provider_1 mock_embedding_service_1.get_dimension = lambda: mock_provider_1.get_dimension() monkeypatch.setattr( "nextcloud_mcp_server.embedding.get_embedding_service", lambda: mock_embedding_service_1, ) # First call - creates collection with 384 dimensions client1 = await get_qdrant_client() collection_name = mock_settings.get_collection_name() # Verify collection created collection_info = await client1.get_collection(collection_name) assert collection_info.config.params.vectors.size == 384 # Close client1 to release file lock await client1.close() # Reset singleton (but collection persists in temp directory) import nextcloud_mcp_server.vector.qdrant_client as qdrant_module qdrant_module._qdrant_client = None # Change embedding service to different dimension (768) mock_provider_2 = SimpleEmbeddingProvider(dimension=768) mock_embedding_service_2 = Mock() mock_embedding_service_2.provider = mock_provider_2 mock_embedding_service_2.get_dimension = lambda: mock_provider_2.get_dimension() monkeypatch.setattr( "nextcloud_mcp_server.embedding.get_embedding_service", lambda: mock_embedding_service_2, ) # Second call - should detect dimension mismatch and raise error with pytest.raises(ValueError) as exc_info: await get_qdrant_client() # Verify error message is helpful error_msg = str(exc_info.value) assert "Dimension mismatch" in error_msg assert "384" in error_msg # Old dimension assert "768" in error_msg # New dimension assert "Solutions:" in error_msg # Includes helpful solutions @pytest.mark.integration async def test_idempotent_initialization(monkeypatch): """Test that multiple calls to get_qdrant_client() are idempotent.""" # Mock settings from nextcloud_mcp_server.config import Settings mock_settings = Settings( qdrant_location=":memory:", ollama_embedding_model="nomic-embed-text", vector_sync_enabled=False, ) monkeypatch.setattr( "nextcloud_mcp_server.vector.qdrant_client.get_settings", lambda: mock_settings ) # Mock embedding service - must have .provider attribute from nextcloud_mcp_server.embedding import SimpleEmbeddingProvider mock_provider = SimpleEmbeddingProvider(dimension=384) mock_embedding_service = Mock() mock_embedding_service.provider = mock_provider mock_embedding_service.get_dimension = lambda: mock_provider.get_dimension() monkeypatch.setattr( "nextcloud_mcp_server.embedding.get_embedding_service", lambda: mock_embedding_service, ) # Call multiple times client1 = await get_qdrant_client() client2 = await get_qdrant_client() client3 = await get_qdrant_client() # All should return same singleton instance assert client1 is client2 assert client2 is client3 # Collection should exist collection_name = mock_settings.get_collection_name() collections = await client1.get_collections() collection_names = [c.name for c in collections.collections] assert collection_name in collection_names @pytest.mark.integration async def test_collection_name_generation(monkeypatch): """Test that collection name is correctly generated from deployment ID and model.""" # Mock settings with custom deployment ID from nextcloud_mcp_server.config import Settings mock_settings = Settings( qdrant_location=":memory:", ollama_embedding_model="test-model", vector_sync_enabled=False, ) # Mock deployment ID monkeypatch.setenv("MCP_DEPLOYMENT_ID", "test-deployment") monkeypatch.setattr( "nextcloud_mcp_server.vector.qdrant_client.get_settings", lambda: mock_settings ) # Mock embedding service - must have .provider attribute from nextcloud_mcp_server.embedding import SimpleEmbeddingProvider mock_provider = SimpleEmbeddingProvider(dimension=384) mock_embedding_service = Mock() mock_embedding_service.provider = mock_provider mock_embedding_service.get_dimension = lambda: mock_provider.get_dimension() monkeypatch.setattr( "nextcloud_mcp_server.embedding.get_embedding_service", lambda: mock_embedding_service, ) # Get client client = await get_qdrant_client() # Verify collection name includes deployment ID and model collection_name = mock_settings.get_collection_name() assert "test-deployment" in collection_name or "test-model" in collection_name # Verify collection was created with that name collections = await client.get_collections() collection_names = [c.name for c in collections.collections] assert collection_name in collection_names @pytest.mark.integration async def test_collection_uses_cosine_distance(monkeypatch): """Test that created collection uses COSINE distance metric.""" # Mock settings from nextcloud_mcp_server.config import Settings mock_settings = Settings( qdrant_location=":memory:", ollama_embedding_model="nomic-embed-text", vector_sync_enabled=False, ) monkeypatch.setattr( "nextcloud_mcp_server.vector.qdrant_client.get_settings", lambda: mock_settings ) # Mock embedding service - must have .provider attribute from nextcloud_mcp_server.embedding import SimpleEmbeddingProvider mock_provider = SimpleEmbeddingProvider(dimension=384) mock_embedding_service = Mock() mock_embedding_service.provider = mock_provider mock_embedding_service.get_dimension = lambda: mock_provider.get_dimension() monkeypatch.setattr( "nextcloud_mcp_server.embedding.get_embedding_service", lambda: mock_embedding_service, ) # Get client (creates collection) client = await get_qdrant_client() # Verify collection uses COSINE distance collection_name = mock_settings.get_collection_name() collection_info = await client.get_collection(collection_name) from qdrant_client.models import Distance assert collection_info.config.params.vectors.distance == Distance.COSINE