""" Copyright 2024, Zep Software, Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from collections.abc import Generator from typing import Any from unittest.mock import AsyncMock, MagicMock, patch import pytest from graphiti_core.embedder.gemini import ( DEFAULT_EMBEDDING_MODEL, GeminiEmbedder, GeminiEmbedderConfig, ) from tests.embedder.embedder_fixtures import create_embedding_values def create_gemini_embedding(multiplier: float = 0.1) -> MagicMock: """Create a mock Gemini embedding with specified value multiplier.""" mock_embedding = MagicMock() mock_embedding.values = create_embedding_values(multiplier) return mock_embedding @pytest.fixture def mock_gemini_response() -> MagicMock: """Create a mock Gemini embeddings response.""" mock_result = MagicMock() mock_result.embeddings = [create_gemini_embedding()] return mock_result @pytest.fixture def mock_gemini_batch_response() -> MagicMock: """Create a mock Gemini batch embeddings response.""" mock_result = MagicMock() mock_result.embeddings = [ create_gemini_embedding(0.1), create_gemini_embedding(0.2), create_gemini_embedding(0.3), ] return mock_result @pytest.fixture def mock_gemini_client() -> Generator[Any, Any, None]: """Create a mocked Gemini client.""" with patch('google.genai.Client') as mock_client: mock_instance = mock_client.return_value mock_instance.aio = MagicMock() mock_instance.aio.models = MagicMock() mock_instance.aio.models.embed_content = AsyncMock() yield mock_instance @pytest.fixture def gemini_embedder(mock_gemini_client: Any) -> GeminiEmbedder: """Create a GeminiEmbedder with a mocked client.""" config = GeminiEmbedderConfig(api_key='test_api_key') client = GeminiEmbedder(config=config) client.client = mock_gemini_client return client @pytest.mark.asyncio async def test_create_calls_api_correctly( gemini_embedder: GeminiEmbedder, mock_gemini_client: Any, mock_gemini_response: MagicMock ) -> None: """Test that create method correctly calls the API and processes the response.""" # Setup mock_gemini_client.aio.models.embed_content.return_value = mock_gemini_response # Call method result = await gemini_embedder.create('Test input') # Verify API is called with correct parameters mock_gemini_client.aio.models.embed_content.assert_called_once() _, kwargs = mock_gemini_client.aio.models.embed_content.call_args assert kwargs['model'] == DEFAULT_EMBEDDING_MODEL assert kwargs['contents'] == ['Test input'] # Verify result is processed correctly assert result == mock_gemini_response.embeddings[0].values @pytest.mark.asyncio async def test_create_batch_processes_multiple_inputs( gemini_embedder: GeminiEmbedder, mock_gemini_client: Any, mock_gemini_batch_response: MagicMock ) -> None: """Test that create_batch method correctly processes multiple inputs.""" # Setup mock_gemini_client.aio.models.embed_content.return_value = mock_gemini_batch_response input_batch = ['Input 1', 'Input 2', 'Input 3'] # Call method result = await gemini_embedder.create_batch(input_batch) # Verify API is called with correct parameters mock_gemini_client.aio.models.embed_content.assert_called_once() _, kwargs = mock_gemini_client.aio.models.embed_content.call_args assert kwargs['model'] == DEFAULT_EMBEDDING_MODEL assert kwargs['contents'] == input_batch # Verify all results are processed correctly assert len(result) == 3 assert result == [ mock_gemini_batch_response.embeddings[0].values, mock_gemini_batch_response.embeddings[1].values, mock_gemini_batch_response.embeddings[2].values, ] if __name__ == '__main__': pytest.main(['-xvs', __file__])