Nextcloud MCP Server

"""Unit tests for OpenAI provider.""" from unittest.mock import AsyncMock, MagicMock import pytest from nextcloud_mcp_server.providers.openai import ( OPENAI_EMBEDDING_DIMENSIONS, OpenAIProvider, ) @pytest.fixture def mock_openai_client(mocker): """Mock OpenAI AsyncClient.""" mock_client = MagicMock() mock_client.embeddings = MagicMock() mock_client.chat = MagicMock() mock_client.chat.completions = MagicMock() mock_client.close = AsyncMock() mocker.patch( "nextcloud_mcp_server.providers.openai.AsyncOpenAI", return_value=mock_client ) return mock_client @pytest.mark.unit async def test_openai_embedding(mock_openai_client): """Test OpenAI embedding with text-embedding-3-small.""" # Mock response mock_embedding_data = MagicMock() mock_embedding_data.embedding = [0.1, 0.2, 0.3] mock_embedding_data.index = 0 mock_response = MagicMock() mock_response.data = [mock_embedding_data] mock_openai_client.embeddings.create = AsyncMock(return_value=mock_response) # Create provider provider = OpenAIProvider( api_key="test-key", embedding_model="text-embedding-3-small", generation_model=None, ) # Test embedding embedding = await provider.embed("test text") assert embedding == [0.1, 0.2, 0.3] mock_openai_client.embeddings.create.assert_called_once_with( input="test text", model="text-embedding-3-small", ) @pytest.mark.unit async def test_openai_embedding_batch(mock_openai_client): """Test OpenAI batch embedding.""" # Mock response mock_embedding_data_1 = MagicMock() mock_embedding_data_1.embedding = [0.1, 0.2, 0.3] mock_embedding_data_1.index = 0 mock_embedding_data_2 = MagicMock() mock_embedding_data_2.embedding = [0.4, 0.5, 0.6] mock_embedding_data_2.index = 1 mock_response = MagicMock() mock_response.data = [mock_embedding_data_1, mock_embedding_data_2] mock_openai_client.embeddings.create = AsyncMock(return_value=mock_response) # Create provider provider = OpenAIProvider( api_key="test-key", embedding_model="text-embedding-3-small", generation_model=None, ) # Test batch embedding embeddings = await provider.embed_batch(["text1", "text2"]) assert len(embeddings) == 2 assert embeddings[0] == [0.1, 0.2, 0.3] assert embeddings[1] == [0.4, 0.5, 0.6] mock_openai_client.embeddings.create.assert_called_once_with( input=["text1", "text2"], model="text-embedding-3-small", ) @pytest.mark.unit async def test_openai_generation(mock_openai_client): """Test OpenAI text generation.""" # Mock response mock_choice = MagicMock() mock_choice.message.content = "Generated response" mock_response = MagicMock() mock_response.choices = [mock_choice] mock_openai_client.chat.completions.create = AsyncMock(return_value=mock_response) # Create provider provider = OpenAIProvider( api_key="test-key", embedding_model=None, generation_model="gpt-4o-mini", ) # Test generation text = await provider.generate("test prompt", max_tokens=100) assert text == "Generated response" mock_openai_client.chat.completions.create.assert_called_once_with( model="gpt-4o-mini", messages=[{"role": "user", "content": "test prompt"}], max_tokens=100, temperature=0.7, ) @pytest.mark.unit async def test_openai_both_capabilities(mock_openai_client): """Test OpenAI with both embedding and generation models.""" # Mock embedding response mock_embedding_data = MagicMock() mock_embedding_data.embedding = [0.1, 0.2] mock_embedding_data.index = 0 mock_embed_response = MagicMock() mock_embed_response.data = [mock_embedding_data] mock_openai_client.embeddings.create = AsyncMock(return_value=mock_embed_response) # Mock generation response mock_choice = MagicMock() mock_choice.message.content = "Response" mock_gen_response = MagicMock() mock_gen_response.choices = [mock_choice] mock_openai_client.chat.completions.create = AsyncMock( return_value=mock_gen_response ) # Create provider with both models provider = OpenAIProvider( api_key="test-key", embedding_model="text-embedding-3-small", generation_model="gpt-4o-mini", ) assert provider.supports_embeddings is True assert provider.supports_generation is True # Test both capabilities embedding = await provider.embed("test") assert embedding == [0.1, 0.2] text = await provider.generate("test") assert text == "Response" @pytest.mark.unit async def test_openai_no_embeddings(): """Test OpenAI provider with no embedding model raises error.""" provider = OpenAIProvider( api_key="test-key", embedding_model=None, generation_model="gpt-4o-mini", ) assert provider.supports_embeddings is False with pytest.raises(NotImplementedError, match="no embedding_model configured"): await provider.embed("test") with pytest.raises(NotImplementedError, match="no embedding_model configured"): await provider.embed_batch(["test"]) with pytest.raises(NotImplementedError, match="no embedding_model configured"): provider.get_dimension() @pytest.mark.unit async def test_openai_no_generation(): """Test OpenAI provider with no generation model raises error.""" provider = OpenAIProvider( api_key="test-key", embedding_model="text-embedding-3-small", generation_model=None, ) assert provider.supports_generation is False with pytest.raises(NotImplementedError, match="no generation_model configured"): await provider.generate("test") @pytest.mark.unit async def test_openai_known_dimension(): """Test dimension detection for known OpenAI models.""" provider = OpenAIProvider( api_key="test-key", embedding_model="text-embedding-3-small", ) # Known model should have dimension set from lookup table assert provider.get_dimension() == 1536 @pytest.mark.unit async def test_openai_unknown_dimension_detected(mock_openai_client): """Test dimension detection for unknown model via API call.""" # Mock response with specific dimension mock_embedding_data = MagicMock() mock_embedding_data.embedding = [0.1] * 768 mock_embedding_data.index = 0 mock_response = MagicMock() mock_response.data = [mock_embedding_data] mock_openai_client.embeddings.create = AsyncMock(return_value=mock_response) provider = OpenAIProvider( api_key="test-key", embedding_model="custom-embedding-model", ) # Dimension not known yet for custom model with pytest.raises(RuntimeError, match="not detected yet"): provider.get_dimension() # Detect dimension via embed call await provider.embed("test") # Now dimension should be available assert provider.get_dimension() == 768 @pytest.mark.unit async def test_openai_github_models_api(mock_openai_client): """Test OpenAI provider with GitHub Models API configuration.""" # Mock response mock_embedding_data = MagicMock() mock_embedding_data.embedding = [0.1, 0.2, 0.3] mock_embedding_data.index = 0 mock_response = MagicMock() mock_response.data = [mock_embedding_data] mock_openai_client.embeddings.create = AsyncMock(return_value=mock_response) # Create provider with GitHub Models configuration provider = OpenAIProvider( api_key="ghp_test_token", base_url="https://models.github.ai/inference", embedding_model="openai/text-embedding-3-small", generation_model=None, ) # Known dimension for GitHub Models prefixed model assert ( provider.get_dimension() == OPENAI_EMBEDDING_DIMENSIONS["openai/text-embedding-3-small"] ) # Test embedding embedding = await provider.embed("test text") assert embedding == [0.1, 0.2, 0.3] @pytest.mark.unit async def test_openai_empty_batch(): """Test OpenAI batch embedding with empty list.""" provider = OpenAIProvider( api_key="test-key", embedding_model="text-embedding-3-small", ) embeddings = await provider.embed_batch([]) assert embeddings == [] @pytest.mark.unit async def test_openai_close(mock_openai_client): """Test OpenAI client close.""" provider = OpenAIProvider( api_key="test-key", embedding_model="text-embedding-3-small", ) await provider.close() mock_openai_client.close.assert_called_once()