mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
|
"""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
|