Chris Coutinho
779d474aaa
- test_qdrant_collection_creation.py:
- Add get_vector_params() helper to handle named vectors format
- Collections use {"dense": VectorParams(...)} instead of direct VectorParams
- Fix otel_service_name setting in test_collection_name_generation
- test_sampling.py:
- Fix MCP response parsing: use json.loads(result.content[0].text)
instead of result.structuredContent (which is None)
- Add require_vector_sync_tools() helper for graceful skipping
- Add helper call to all 5 test functions
- test_rag.py:
- Add require_vector_sync_tools() helper for graceful skipping
- Fix MCP response parsing (same as sampling tests)
- Prevents 600s timeout when VECTOR_SYNC_ENABLED is not set
Tests now pass/skip cleanly when run independently. The anyio.WouldBlock
errors in full test suite runs are fixture isolation issues, not code bugs.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
330 lines
11 KiB
Python
330 lines
11 KiB
Python
"""Integration tests for Qdrant collection auto-creation.
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These tests validate that:
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1. Collections are automatically created on first access
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2. Dimension validation detects mismatches
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3. Idempotent initialization (multiple calls don't fail)
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4. Proper error handling and logging
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"""
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from unittest.mock import Mock
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import pytest
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from qdrant_client.models import VectorParams
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from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
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pytestmark = pytest.mark.integration
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def get_vector_params(collection_info) -> VectorParams:
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"""Get vector params from collection info, handling named vectors format."""
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vectors = collection_info.config.params.vectors
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if isinstance(vectors, dict):
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return vectors["dense"]
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return vectors
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@pytest.fixture(autouse=True)
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async def reset_singleton():
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"""Reset the global Qdrant client singleton between tests."""
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global _qdrant_client
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import nextcloud_mcp_server.vector.qdrant_client as qdrant_module
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# Store original
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original = qdrant_module._qdrant_client
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# Reset for test
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qdrant_module._qdrant_client = None
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yield
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# Restore original
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qdrant_module._qdrant_client = original
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@pytest.mark.integration
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async def test_collection_auto_created_on_first_access(monkeypatch):
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"""Test that collection is automatically created if it doesn't exist."""
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# Mock settings
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from nextcloud_mcp_server.config import Settings
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mock_settings = Settings(
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qdrant_location=":memory:",
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ollama_embedding_model="nomic-embed-text",
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vector_sync_enabled=False, # Disable background sync for test
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)
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monkeypatch.setattr(
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"nextcloud_mcp_server.vector.qdrant_client.get_settings", lambda: mock_settings
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)
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# Mock embedding service - must have .provider attribute
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from nextcloud_mcp_server.embedding import SimpleEmbeddingProvider
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mock_provider = SimpleEmbeddingProvider(dimension=384)
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mock_embedding_service = Mock()
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mock_embedding_service.provider = mock_provider
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mock_embedding_service.get_dimension = lambda: mock_provider.get_dimension()
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monkeypatch.setattr(
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"nextcloud_mcp_server.embedding.get_embedding_service",
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lambda: mock_embedding_service,
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)
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# Get client (should trigger collection creation)
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client = await get_qdrant_client()
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# Verify client is initialized
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assert client is not None
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# Verify collection was created
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collection_name = mock_settings.get_collection_name()
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collections = await client.get_collections()
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collection_names = [c.name for c in collections.collections]
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assert collection_name in collection_names
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# Verify collection has correct dimensions
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collection_info = await client.get_collection(collection_name)
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assert get_vector_params(collection_info).size == 384
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@pytest.mark.integration
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async def test_existing_collection_reused(monkeypatch):
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"""Test that existing collection is reused without error."""
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# Mock settings
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from nextcloud_mcp_server.config import Settings
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mock_settings = Settings(
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qdrant_location=":memory:",
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ollama_embedding_model="nomic-embed-text",
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vector_sync_enabled=False,
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)
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monkeypatch.setattr(
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"nextcloud_mcp_server.vector.qdrant_client.get_settings", lambda: mock_settings
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)
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# Mock embedding service - must have .provider attribute
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from nextcloud_mcp_server.embedding import SimpleEmbeddingProvider
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mock_provider = SimpleEmbeddingProvider(dimension=384)
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mock_embedding_service = Mock()
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mock_embedding_service.provider = mock_provider
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mock_embedding_service.get_dimension = lambda: mock_provider.get_dimension()
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monkeypatch.setattr(
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"nextcloud_mcp_server.embedding.get_embedding_service",
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lambda: mock_embedding_service,
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)
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# First call - creates collection
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_ = await get_qdrant_client()
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collection_name = mock_settings.get_collection_name()
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# Reset singleton to simulate second initialization
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import nextcloud_mcp_server.vector.qdrant_client as qdrant_module
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qdrant_module._qdrant_client = None
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# Second call - should reuse existing collection
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client2 = await get_qdrant_client()
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# Verify both clients work
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assert client2 is not None
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# Verify collection still exists and wasn't recreated
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collections = await client2.get_collections()
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collection_names = [c.name for c in collections.collections]
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assert collection_name in collection_names
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# Verify dimensions unchanged
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collection_info = await client2.get_collection(collection_name)
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assert get_vector_params(collection_info).size == 384
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@pytest.mark.integration
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async def test_dimension_mismatch_detected(monkeypatch, tmp_path):
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"""Test that dimension mismatch raises clear error."""
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# Use persistent temp directory so collection survives client reset
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from nextcloud_mcp_server.config import Settings
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qdrant_path = str(tmp_path / "qdrant_data")
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mock_settings = Settings(
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qdrant_location=qdrant_path,
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ollama_embedding_model="nomic-embed-text",
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vector_sync_enabled=False,
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)
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monkeypatch.setattr(
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"nextcloud_mcp_server.vector.qdrant_client.get_settings", lambda: mock_settings
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)
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# First embedding service: 384 dimensions
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from nextcloud_mcp_server.embedding import SimpleEmbeddingProvider
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mock_provider_1 = SimpleEmbeddingProvider(dimension=384)
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mock_embedding_service_1 = Mock()
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mock_embedding_service_1.provider = mock_provider_1
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mock_embedding_service_1.get_dimension = lambda: mock_provider_1.get_dimension()
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monkeypatch.setattr(
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"nextcloud_mcp_server.embedding.get_embedding_service",
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lambda: mock_embedding_service_1,
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)
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# First call - creates collection with 384 dimensions
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client1 = await get_qdrant_client()
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collection_name = mock_settings.get_collection_name()
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# Verify collection created
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collection_info = await client1.get_collection(collection_name)
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assert get_vector_params(collection_info).size == 384
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# Close client1 to release file lock
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await client1.close()
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# Reset singleton (but collection persists in temp directory)
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import nextcloud_mcp_server.vector.qdrant_client as qdrant_module
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qdrant_module._qdrant_client = None
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# Change embedding service to different dimension (768)
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mock_provider_2 = SimpleEmbeddingProvider(dimension=768)
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mock_embedding_service_2 = Mock()
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mock_embedding_service_2.provider = mock_provider_2
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mock_embedding_service_2.get_dimension = lambda: mock_provider_2.get_dimension()
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monkeypatch.setattr(
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"nextcloud_mcp_server.embedding.get_embedding_service",
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lambda: mock_embedding_service_2,
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)
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# Second call - should detect dimension mismatch and raise error
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with pytest.raises(ValueError) as exc_info:
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await get_qdrant_client()
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# Verify error message is helpful
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error_msg = str(exc_info.value)
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assert "Dimension mismatch" in error_msg
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assert "384" in error_msg # Old dimension
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assert "768" in error_msg # New dimension
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assert "Solutions:" in error_msg # Includes helpful solutions
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@pytest.mark.integration
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async def test_idempotent_initialization(monkeypatch):
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"""Test that multiple calls to get_qdrant_client() are idempotent."""
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# Mock settings
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from nextcloud_mcp_server.config import Settings
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mock_settings = Settings(
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qdrant_location=":memory:",
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ollama_embedding_model="nomic-embed-text",
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vector_sync_enabled=False,
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)
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monkeypatch.setattr(
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"nextcloud_mcp_server.vector.qdrant_client.get_settings", lambda: mock_settings
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)
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# Mock embedding service - must have .provider attribute
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from nextcloud_mcp_server.embedding import SimpleEmbeddingProvider
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mock_provider = SimpleEmbeddingProvider(dimension=384)
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mock_embedding_service = Mock()
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mock_embedding_service.provider = mock_provider
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mock_embedding_service.get_dimension = lambda: mock_provider.get_dimension()
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monkeypatch.setattr(
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"nextcloud_mcp_server.embedding.get_embedding_service",
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lambda: mock_embedding_service,
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)
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# Call multiple times
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client1 = await get_qdrant_client()
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client2 = await get_qdrant_client()
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client3 = await get_qdrant_client()
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# All should return same singleton instance
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assert client1 is client2
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assert client2 is client3
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# Collection should exist
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collection_name = mock_settings.get_collection_name()
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collections = await client1.get_collections()
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collection_names = [c.name for c in collections.collections]
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assert collection_name in collection_names
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@pytest.mark.integration
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async def test_collection_name_generation(monkeypatch):
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"""Test that collection name is correctly generated from deployment ID and model."""
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# Mock settings with custom deployment ID
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from nextcloud_mcp_server.config import Settings
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mock_settings = Settings(
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qdrant_location=":memory:",
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ollama_embedding_model="test-model",
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otel_service_name="test-deployment",
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vector_sync_enabled=False,
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)
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monkeypatch.setattr(
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"nextcloud_mcp_server.vector.qdrant_client.get_settings", lambda: mock_settings
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)
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# Mock embedding service - must have .provider attribute
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from nextcloud_mcp_server.embedding import SimpleEmbeddingProvider
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mock_provider = SimpleEmbeddingProvider(dimension=384)
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mock_embedding_service = Mock()
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mock_embedding_service.provider = mock_provider
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mock_embedding_service.get_dimension = lambda: mock_provider.get_dimension()
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monkeypatch.setattr(
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"nextcloud_mcp_server.embedding.get_embedding_service",
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lambda: mock_embedding_service,
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)
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# Get client
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client = await get_qdrant_client()
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# Verify collection name includes deployment ID and model
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collection_name = mock_settings.get_collection_name()
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assert "test-deployment" in collection_name or "test-model" in collection_name
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# Verify collection was created with that name
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collections = await client.get_collections()
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collection_names = [c.name for c in collections.collections]
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assert collection_name in collection_names
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@pytest.mark.integration
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async def test_collection_uses_cosine_distance(monkeypatch):
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"""Test that created collection uses COSINE distance metric."""
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# Mock settings
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from nextcloud_mcp_server.config import Settings
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mock_settings = Settings(
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qdrant_location=":memory:",
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ollama_embedding_model="nomic-embed-text",
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vector_sync_enabled=False,
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)
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monkeypatch.setattr(
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"nextcloud_mcp_server.vector.qdrant_client.get_settings", lambda: mock_settings
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)
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# Mock embedding service - must have .provider attribute
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from nextcloud_mcp_server.embedding import SimpleEmbeddingProvider
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mock_provider = SimpleEmbeddingProvider(dimension=384)
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mock_embedding_service = Mock()
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mock_embedding_service.provider = mock_provider
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mock_embedding_service.get_dimension = lambda: mock_provider.get_dimension()
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monkeypatch.setattr(
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"nextcloud_mcp_server.embedding.get_embedding_service",
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lambda: mock_embedding_service,
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)
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# Get client (creates collection)
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client = await get_qdrant_client()
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# Verify collection uses COSINE distance
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collection_name = mock_settings.get_collection_name()
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collection_info = await client.get_collection(collection_name)
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from qdrant_client.models import Distance
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assert get_vector_params(collection_info).distance == Distance.COSINE
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