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https://github.com/hwchase17/langchain
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3796672c67
- [ ] **Packages affected**: - community: fix `cosine_similarity` to support simsimd beyond 3.7.7 - partners/milvus: fix `cosine_similarity` to support simsimd beyond 3.7.7 - partners/mongodb: fix `cosine_similarity` to support simsimd beyond 3.7.7 - partners/pinecone: fix `cosine_similarity` to support simsimd beyond 3.7.7 - partners/qdrant: fix `cosine_similarity` to support simsimd beyond 3.7.7 - [ ] **Broadcast operation failure while using simsimd beyond v3.7.7**: - **Description:** I was using simsimd 4.3.1 and the unsupported operand type issue popped up. When I checked out the repo and ran the tests, they failed as well (have attached a screenshot for that). Looks like it is a variant of https://github.com/langchain-ai/langchain/issues/18022 . Prior to 3.7.7, simd.cdist returned an ndarray but now it returns simsimd.DistancesTensor which is ineligible for a broadcast operation with numpy. With this change, it also remove the need to explicitly cast `Z` to numpy array - **Issue:** #19905 - **Dependencies:** No - **Twitter handle:** https://x.com/GetzJoydeep <img width="1622" alt="Screenshot 2024-05-29 at 2 50 00 PM" src="https://github.com/langchain-ai/langchain/assets/31132555/fb27b383-a9ae-4a6f-b355-6d503b72db56"> - [ ] **Considerations**: 1. I started with community but since similar changes were there in Milvus, MongoDB, Pinecone, and QDrant so I modified their files as well. If touching multiple packages in one PR is not the norm, then I can remove them from this PR and raise separate ones 2. I have run and verified that the tests work. Since, only MongoDB had tests, I ran theirs and verified it works as well. Screenshots attached : <img width="1573" alt="Screenshot 2024-05-29 at 2 52 13 PM" src="https://github.com/langchain-ai/langchain/assets/31132555/ce87d1ea-19b6-4900-9384-61fbc1a30de9"> <img width="1614" alt="Screenshot 2024-05-29 at 3 33 51 PM" src="https://github.com/langchain-ai/langchain/assets/31132555/6ce1d679-db4c-4291-8453-01028ab2dca5"> I have added a test for simsimd. I feel it may not go well with the CI/CD setup as installing simsimd is not a dependency requirement. I have just imported simsimd to ensure simsimd cosine similarity is invoked. However, its not a good approach. Suggestions are welcome and I can make the required changes on the PR. Please provide guidance on the same as I am new to the community. --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
"""Test math utility functions."""
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import importlib
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from typing import List
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import numpy as np
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import pytest
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from langchain_community.utils.math import cosine_similarity, cosine_similarity_top_k
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@pytest.fixture
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def X() -> List[List[float]]:
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return [[1.0, 2.0, 3.0], [0.0, 1.0, 0.0], [1.0, 2.0, 0.0]]
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@pytest.fixture
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def Y() -> List[List[float]]:
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return [[0.5, 1.0, 1.5], [1.0, 0.0, 0.0], [2.0, 5.0, 2.0], [0.0, 0.0, 0.0]]
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def test_cosine_similarity_zero() -> None:
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X = np.zeros((3, 3))
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Y = np.random.random((3, 3))
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expected = np.zeros((3, 3))
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actual = cosine_similarity(X, Y)
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assert np.allclose(expected, actual)
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def test_cosine_similarity_identity() -> None:
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X = np.random.random((4, 4))
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expected = np.ones(4)
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actual = np.diag(cosine_similarity(X, X))
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assert np.allclose(expected, actual)
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def test_cosine_similarity_empty() -> None:
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empty_list: List[List[float]] = []
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assert len(cosine_similarity(empty_list, empty_list)) == 0
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assert len(cosine_similarity(empty_list, np.random.random((3, 3)))) == 0
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def test_cosine_similarity(X: List[List[float]], Y: List[List[float]]) -> None:
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expected = [
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[1.0, 0.26726124, 0.83743579, 0.0],
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[0.53452248, 0.0, 0.87038828, 0.0],
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[0.5976143, 0.4472136, 0.93419873, 0.0],
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]
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actual = cosine_similarity(X, Y)
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assert np.allclose(expected, actual)
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def test_cosine_similarity_top_k(X: List[List[float]], Y: List[List[float]]) -> None:
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expected_idxs = [(0, 0), (2, 2), (1, 2), (0, 2), (2, 0)]
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expected_scores = [1.0, 0.93419873, 0.87038828, 0.83743579, 0.5976143]
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actual_idxs, actual_scores = cosine_similarity_top_k(X, Y)
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assert actual_idxs == expected_idxs
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assert np.allclose(expected_scores, actual_scores)
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def test_cosine_similarity_score_threshold(
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X: List[List[float]], Y: List[List[float]]
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) -> None:
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expected_idxs = [(0, 0), (2, 2)]
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expected_scores = [1.0, 0.93419873]
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actual_idxs, actual_scores = cosine_similarity_top_k(
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X, Y, top_k=None, score_threshold=0.9
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)
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assert actual_idxs == expected_idxs
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assert np.allclose(expected_scores, actual_scores)
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def invoke_cosine_similarity_top_k_score_threshold(
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X: List[List[float]], Y: List[List[float]]
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) -> None:
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expected_idxs = [(0, 0), (2, 2), (1, 2), (0, 2)]
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expected_scores = [1.0, 0.93419873, 0.87038828, 0.83743579]
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actual_idxs, actual_scores = cosine_similarity_top_k(X, Y, score_threshold=0.8)
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assert actual_idxs == expected_idxs
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assert np.allclose(expected_scores, actual_scores)
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def test_cosine_similarity_top_k_and_score_threshold(
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X: List[List[float]], Y: List[List[float]]
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) -> None:
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if importlib.util.find_spec("simsimd"):
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raise ValueError("test should be run without simsimd installed.")
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invoke_cosine_similarity_top_k_score_threshold(X, Y)
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@pytest.mark.requires("simsimd")
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def test_cosine_similarity_top_k_and_score_threshold_with_simsimd(
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X: List[List[float]], Y: List[List[float]]
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) -> None:
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# Same test, but ensuring simsimd is available in the project through the import.
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invoke_cosine_similarity_top_k_score_threshold(X, Y)
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