mirror of
https://github.com/hwchase17/langchain
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55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
"""Test vector store utility functions."""
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import numpy as np
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from langchain.vectorstores.utils import maximal_marginal_relevance
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def test_maximal_marginal_relevance_lambda_zero() -> None:
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query_embedding = np.random.random(size=5)
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embedding_list = [query_embedding, query_embedding, np.zeros(5)]
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expected = [0, 2]
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actual = maximal_marginal_relevance(
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query_embedding, embedding_list, lambda_mult=0, k=2
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)
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assert expected == actual
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def test_maximal_marginal_relevance_lambda_one() -> None:
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query_embedding = np.random.random(size=5)
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embedding_list = [query_embedding, query_embedding, np.zeros(5)]
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expected = [0, 1]
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actual = maximal_marginal_relevance(
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query_embedding, embedding_list, lambda_mult=1, k=2
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)
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assert expected == actual
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def test_maximal_marginal_relevance() -> None:
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query_embedding = np.array([1, 0])
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# Vectors that are 30, 45 and 75 degrees from query vector (cosine similarity of
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# 0.87, 0.71, 0.26) and the latter two are 15 and 60 degree from the first
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# (cosine similarity 0.97 and 0.71). So for 3rd vector be chosen, must be case that
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# 0.71lambda - 0.97(1 - lambda) < 0.26lambda - 0.71(1-lambda)
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# -> lambda ~< .26 / .71
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embedding_list = [[3**0.5, 1], [1, 1], [1, 2 + (3**0.5)]]
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expected = [0, 2]
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actual = maximal_marginal_relevance(
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query_embedding, embedding_list, lambda_mult=(25 / 71), k=2
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)
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assert expected == actual
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expected = [0, 1]
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actual = maximal_marginal_relevance(
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query_embedding, embedding_list, lambda_mult=(27 / 71), k=2
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)
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assert expected == actual
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def test_maximal_marginal_relevance_query_dim() -> None:
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query_embedding = np.random.random(size=5)
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query_embedding_2d = query_embedding.reshape((1, 5))
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embedding_list = np.random.random(size=(4, 5)).tolist()
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first = maximal_marginal_relevance(query_embedding, embedding_list)
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second = maximal_marginal_relevance(query_embedding_2d, embedding_list)
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assert first == second
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