""" Unit tests for embedding utilities and common functionality Tests dimension consistency, batch processing, error handling patterns, and other utilities common across embedding services. """ import pytest from unittest.mock import patch, Mock, AsyncMock import numpy as np from trustgraph.schema import EmbeddingsRequest, EmbeddingsResponse, Error from trustgraph.exceptions import TooManyRequests class MockEmbeddingProcessor: """Simple mock embedding processor for testing functionality""" def __init__(self, embedding_function=None, **params): # Store embedding function for mocking self.embedding_function = embedding_function self.model = params.get('model', 'test-model') async def on_embeddings(self, text): if self.embedding_function: return self.embedding_function(text) return [0.1, 0.2, 0.3, 0.4, 0.5] # Default test embedding class TestEmbeddingDimensionConsistency: """Test cases for embedding dimension consistency""" async def test_consistent_dimensions_single_processor(self): """Test that a single processor returns consistent dimensions""" # Arrange dimension = 128 def mock_embedding(text): return [0.1] * dimension processor = MockEmbeddingProcessor(embedding_function=mock_embedding) # Act results = [] test_texts = ["Text 1", "Text 2", "Text 3", "Text 4", "Text 5"] for text in test_texts: result = await processor.on_embeddings(text) results.append(result) # Assert for result in results: assert len(result) == dimension, f"Expected dimension {dimension}, got {len(result)}" # All results should have same dimensions first_dim = len(results[0]) for i, result in enumerate(results[1:], 1): assert len(result) == first_dim, f"Dimension mismatch at index {i}" async def test_dimension_consistency_across_text_lengths(self): """Test dimension consistency across varying text lengths""" # Arrange dimension = 384 def mock_embedding(text): # Dimension should not depend on text length return [0.1] * dimension processor = MockEmbeddingProcessor(embedding_function=mock_embedding) # Act - Test various text lengths test_texts = [ "", # Empty text "Hi", # Very short "This is a medium length sentence for testing.", # Medium "This is a very long text that should still produce embeddings of consistent dimension regardless of the input text length and content." * 10 # Very long ] results = [] for text in test_texts: result = await processor.on_embeddings(text) results.append(result) # Assert for i, result in enumerate(results): assert len(result) == dimension, f"Text length {len(test_texts[i])} produced wrong dimension" def test_dimension_validation_different_models(self): """Test dimension validation for different model configurations""" # Arrange models_and_dims = [ ("small-model", 128), ("medium-model", 384), ("large-model", 1536) ] # Act & Assert for model_name, expected_dim in models_and_dims: # Test dimension validation logic test_vector = [0.1] * expected_dim assert len(test_vector) == expected_dim, f"Model {model_name} dimension mismatch" class TestEmbeddingBatchProcessing: """Test cases for batch processing logic""" async def test_sequential_processing_maintains_order(self): """Test that sequential processing maintains text order""" # Arrange def mock_embedding(text): # Return embedding that encodes the text for verification return [ord(text[0]) / 255.0] if text else [0.0] # Normalize to [0,1] processor = MockEmbeddingProcessor(embedding_function=mock_embedding) # Act test_texts = ["A", "B", "C", "D", "E"] results = [] for text in test_texts: result = await processor.on_embeddings(text) results.append((text, result)) # Assert for i, (original_text, embedding) in enumerate(results): assert original_text == test_texts[i] expected_value = ord(test_texts[i][0]) / 255.0 assert abs(embedding[0] - expected_value) < 0.001 async def test_batch_processing_throughput(self): """Test batch processing capabilities""" # Arrange call_count = 0 def mock_embedding(text): nonlocal call_count call_count += 1 return [0.1, 0.2, 0.3] processor = MockEmbeddingProcessor(embedding_function=mock_embedding) # Act - Process multiple texts batch_size = 10 test_texts = [f"Text {i}" for i in range(batch_size)] results = [] for text in test_texts: result = await processor.on_embeddings(text) results.append(result) # Assert assert call_count == batch_size assert len(results) == batch_size for result in results: assert result == [0.1, 0.2, 0.3] async def test_concurrent_processing_simulation(self): """Test concurrent processing behavior simulation""" # Arrange import asyncio processing_times = [] def mock_embedding(text): import time processing_times.append(time.time()) return [len(text) / 100.0] # Encoding text length processor = MockEmbeddingProcessor(embedding_function=mock_embedding) # Act - Simulate concurrent processing test_texts = [f"Text {i}" for i in range(5)] tasks = [processor.on_embeddings(text) for text in test_texts] results = await asyncio.gather(*tasks) # Assert assert len(results) == 5 assert len(processing_times) == 5 # Results should correspond to text lengths for i, result in enumerate(results): expected_value = len(test_texts[i]) / 100.0 assert abs(result[0] - expected_value) < 0.001 class TestEmbeddingErrorHandling: """Test cases for error handling in embedding services""" async def test_embedding_function_error_handling(self): """Test error handling in embedding function""" # Arrange def failing_embedding(text): raise Exception("Embedding model failed") processor = MockEmbeddingProcessor(embedding_function=failing_embedding) # Act & Assert with pytest.raises(Exception, match="Embedding model failed"): await processor.on_embeddings("Test text") async def test_rate_limit_exception_propagation(self): """Test that rate limit exceptions are properly propagated""" # Arrange def rate_limited_embedding(text): raise TooManyRequests("Rate limit exceeded") processor = MockEmbeddingProcessor(embedding_function=rate_limited_embedding) # Act & Assert with pytest.raises(TooManyRequests, match="Rate limit exceeded"): await processor.on_embeddings("Test text") async def test_none_result_handling(self): """Test handling when embedding function returns None""" # Arrange def none_embedding(text): return None processor = MockEmbeddingProcessor(embedding_function=none_embedding) # Act result = await processor.on_embeddings("Test text") # Assert assert result is None async def test_invalid_embedding_format_handling(self): """Test handling of invalid embedding formats""" # Arrange def invalid_embedding(text): return "not a list" # Invalid format processor = MockEmbeddingProcessor(embedding_function=invalid_embedding) # Act result = await processor.on_embeddings("Test text") # Assert assert result == "not a list" # Returns what the function provides class TestEmbeddingUtilities: """Test cases for embedding utility functions and helpers""" def test_vector_normalization_simulation(self): """Test vector normalization logic simulation""" # Arrange test_vectors = [ [1.0, 2.0, 3.0], [0.5, -0.5, 1.0], [10.0, 20.0, 30.0] ] # Act - Simulate L2 normalization normalized_vectors = [] for vector in test_vectors: magnitude = sum(x**2 for x in vector) ** 0.5 if magnitude > 0: normalized = [x / magnitude for x in vector] else: normalized = vector normalized_vectors.append(normalized) # Assert for normalized in normalized_vectors: magnitude = sum(x**2 for x in normalized) ** 0.5 assert abs(magnitude - 1.0) < 0.0001, "Vector should be unit length" def test_cosine_similarity_calculation(self): """Test cosine similarity calculation between embeddings""" # Arrange vector1 = [1.0, 0.0, 0.0] vector2 = [0.0, 1.0, 0.0] vector3 = [1.0, 0.0, 0.0] # Same as vector1 # Act - Calculate cosine similarities def cosine_similarity(v1, v2): dot_product = sum(a * b for a, b in zip(v1, v2)) mag1 = sum(x**2 for x in v1) ** 0.5 mag2 = sum(x**2 for x in v2) ** 0.5 return dot_product / (mag1 * mag2) if mag1 * mag2 > 0 else 0 sim_12 = cosine_similarity(vector1, vector2) sim_13 = cosine_similarity(vector1, vector3) # Assert assert abs(sim_12 - 0.0) < 0.0001, "Orthogonal vectors should have 0 similarity" assert abs(sim_13 - 1.0) < 0.0001, "Identical vectors should have 1.0 similarity" def test_embedding_validation_helpers(self): """Test embedding validation helper functions""" # Arrange valid_embeddings = [ [0.1, 0.2, 0.3], [1.0, -1.0, 0.0], [] # Empty embedding ] invalid_embeddings = [ None, "not a list", [1, 2, "three"], # Mixed types [[1, 2], [3, 4]] # Nested lists ] # Act & Assert def is_valid_embedding(embedding): if not isinstance(embedding, list): return False return all(isinstance(x, (int, float)) for x in embedding) for embedding in valid_embeddings: assert is_valid_embedding(embedding), f"Should be valid: {embedding}" for embedding in invalid_embeddings: assert not is_valid_embedding(embedding), f"Should be invalid: {embedding}" async def test_embedding_metadata_handling(self): """Test handling of embedding metadata and properties""" # Arrange def metadata_embedding(text): return { "vectors": [0.1, 0.2, 0.3], "model": "test-model", "dimension": 3, "text_length": len(text) } # Mock processor that returns metadata class MetadataProcessor(MockEmbeddingProcessor): async def on_embeddings(self, text): result = metadata_embedding(text) return result["vectors"] # Return only vectors for compatibility processor = MetadataProcessor() # Act result = await processor.on_embeddings("Test text with metadata") # Assert assert isinstance(result, list) assert len(result) == 3 assert result == [0.1, 0.2, 0.3]