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