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Home / Function/ test_pairwise_embedding_distance_eval_chain_euclidean_distance() — langchain Function Reference

test_pairwise_embedding_distance_eval_chain_euclidean_distance() — langchain Function Reference

Architecture documentation for the test_pairwise_embedding_distance_eval_chain_euclidean_distance() function in test_embedding.py from the langchain codebase.

Function python LangChainCore ApiManagement

Entity Profile

Dependency Diagram

graph TD
  8f5b4357_cbc2_92d9_ed65_ad3130d2004f["test_pairwise_embedding_distance_eval_chain_euclidean_distance()"]
  89627e7b_46a2_902e_b342_68623bc9d7ef["test_embedding.py"]
  8f5b4357_cbc2_92d9_ed65_ad3130d2004f -->|defined in| 89627e7b_46a2_902e_b342_68623bc9d7ef
  style 8f5b4357_cbc2_92d9_ed65_ad3130d2004f fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/tests/integration_tests/evaluation/embedding_distance/test_embedding.py lines 72–82

def test_pairwise_embedding_distance_eval_chain_euclidean_distance(
    pairwise_embedding_distance_eval_chain: PairwiseEmbeddingDistanceEvalChain,
    vectors: tuple[np.ndarray, np.ndarray],
) -> None:
    """Test the euclidean distance."""
    from scipy.spatial.distance import euclidean

    pairwise_embedding_distance_eval_chain.distance_metric = EmbeddingDistance.EUCLIDEAN
    result = pairwise_embedding_distance_eval_chain._compute_score(np.array(vectors))
    expected = euclidean(*vectors)
    assert np.isclose(result, expected)

Domain

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Defined In

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Frequently Asked Questions

What does test_pairwise_embedding_distance_eval_chain_euclidean_distance() do?
test_pairwise_embedding_distance_eval_chain_euclidean_distance() is a function in the langchain codebase, defined in libs/langchain/tests/integration_tests/evaluation/embedding_distance/test_embedding.py.
Where is test_pairwise_embedding_distance_eval_chain_euclidean_distance() defined?
test_pairwise_embedding_distance_eval_chain_euclidean_distance() is defined in libs/langchain/tests/integration_tests/evaluation/embedding_distance/test_embedding.py at line 72.

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