mirror of
https://github.com/hanzoai/python-sdk.git
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zeekay
ea3116aac9
- Remove all unconditional pytest.skip() calls - Fix file content tests to handle both strict/non-strict modes - Fix missing device_capabilities module in hanzo-network - Fix HanzoNetProvider imports and exports - Fix local_llm.py import errors - Create test helper classes for pagination tests - Fix FastMCP paginator test constructor args - Configure all API keys from ~/.zsh/after.zsh - All tests now pass or skip conditionally based on actual requirements
61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
"""Tests for embedding service edge cases."""
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import pytest
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from hanzo_memory.services.embeddings import EmbeddingService
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class TestEmbeddingsEdgeCases:
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"""Test embedding service edge cases."""
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@pytest.fixture
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def embedding_service(self):
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"""Create embedding service."""
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return EmbeddingService()
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def test_compute_similarity_dot_product(self, embedding_service):
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"""Test compute similarity with dot product metric."""
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query = [1.0, 2.0, 3.0]
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embeddings = [
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[1.0, 2.0, 3.0], # Same as query
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[3.0, 2.0, 1.0], # Different
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]
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scores = embedding_service.compute_similarity(query, embeddings, metric="dot")
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assert len(scores) == 2
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assert scores[0] > scores[1] # First should have higher dot product
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def test_compute_similarity_euclidean(self, embedding_service):
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"""Test compute similarity with euclidean metric."""
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query = [1.0, 2.0, 3.0]
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embeddings = [
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[1.0, 2.0, 3.0], # Same as query (distance = 0)
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[4.0, 5.0, 6.0], # Different
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]
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scores = embedding_service.compute_similarity(
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query, embeddings, metric="euclidean"
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)
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assert len(scores) == 2
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# Euclidean returns negative distance, so same vector should have highest score
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assert scores[0] > scores[1]
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def test_compute_similarity_empty_embeddings(self, embedding_service):
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"""Test compute similarity with empty embeddings."""
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query = [1.0, 2.0, 3.0]
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embeddings = []
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scores = embedding_service.compute_similarity(query, embeddings)
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assert scores == []
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def test_compute_similarity_invalid_metric(self, embedding_service):
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"""Test compute similarity with invalid metric."""
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query = [1.0, 2.0, 3.0]
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embeddings = [[1.0, 2.0, 3.0]]
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with pytest.raises(ValueError, match="Unknown metric"):
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embedding_service.compute_similarity(query, embeddings, metric="invalid")
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