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feat: support bigquery.vector_search()
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407db5e
feat: supoort bigquery.vector_search()
ashleyxuu 86a26bd
minor fix
ashleyxuu 5e6fe30
address comments
ashleyxuu b7e3470
docstring fix
ashleyxuu 2a5de8e
address comments
ashleyxuu 2bbbaf4
small fix
ashleyxuu f2859f3
add docstring clarification
ashleyxuu 65ef138
Merge branch 'main' into ashleyxu-vector-search
ashleyxuu a2f3b1d
Update bigframes/bigquery/__init__.py
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Original file line number | Diff line number | Diff line change |
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# Copyright 2024 Google LLC | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
|
||
import numpy as np | ||
import pandas as pd | ||
|
||
import bigframes.bigquery as bbq | ||
import bigframes.pandas as bpd | ||
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def test_vector_search_basic_params_with_df(): | ||
search_query = bpd.DataFrame( | ||
{ | ||
"query_id": ["dog", "cat"], | ||
"embedding": [[1.0, 2.0], [3.0, 5.2]], | ||
} | ||
) | ||
vector_search_result = bbq.vector_search( | ||
base_table="bigframes-dev.bigframes_tests_sys.base_table", | ||
column_to_search="my_embedding", | ||
query=search_query, | ||
top_k=2, | ||
).to_pandas() # type:ignore | ||
expected = pd.DataFrame( | ||
{ | ||
"query_id": ["cat", "dog", "dog", "cat"], | ||
"embedding": [ | ||
np.array([3.0, 5.2]), | ||
np.array([1.0, 2.0]), | ||
np.array([1.0, 2.0]), | ||
np.array([3.0, 5.2]), | ||
], | ||
"id": [5, 1, 4, 2], | ||
"my_embedding": [ | ||
np.array([5.0, 5.4]), | ||
np.array([1.0, 2.0]), | ||
np.array([1.0, 3.2]), | ||
np.array([2.0, 4.0]), | ||
], | ||
"distance": [2.009975, 0.0, 1.2, 1.56205], | ||
}, | ||
index=pd.Index([1, 0, 0, 1], dtype="Int64"), | ||
) | ||
pd.testing.assert_frame_equal( | ||
vector_search_result, expected, check_dtype=False, rtol=0.1 | ||
) | ||
|
||
|
||
def test_vector_search_different_params_with_query(): | ||
search_query = bpd.Series([[1.0, 2.0], [3.0, 5.2]]) | ||
vector_search_result = bbq.vector_search( | ||
base_table="bigframes-dev.bigframes_tests_sys.base_table", | ||
column_to_search="my_embedding", | ||
query=search_query, | ||
distance_type="cosine", | ||
top_k=2, | ||
).to_pandas() # type:ignore | ||
expected = pd.DataFrame( | ||
{ | ||
"0": [ | ||
np.array([1.0, 2.0]), | ||
np.array([1.0, 2.0]), | ||
np.array([3.0, 5.2]), | ||
np.array([3.0, 5.2]), | ||
], | ||
"id": [2, 1, 1, 2], | ||
"my_embedding": [ | ||
np.array([2.0, 4.0]), | ||
np.array([1.0, 2.0]), | ||
np.array([1.0, 2.0]), | ||
np.array([2.0, 4.0]), | ||
], | ||
"distance": [0.0, 0.0, 0.001777, 0.001777], | ||
}, | ||
index=pd.Index([0, 0, 1, 1], dtype="Int64"), | ||
) | ||
pd.testing.assert_frame_equal( | ||
vector_search_result, expected, check_dtype=False, rtol=0.1 | ||
) | ||
|
||
|
||
def test_vector_search_df_with_query_column_to_search(): | ||
search_query = bpd.DataFrame( | ||
{ | ||
"query_id": ["dog", "cat"], | ||
"embedding": [[1.0, 2.0], [3.0, 5.2]], | ||
"another_embedding": [[1.0, 2.5], [3.3, 5.2]], | ||
} | ||
) | ||
vector_search_result = bbq.vector_search( | ||
base_table="bigframes-dev.bigframes_tests_sys.base_table", | ||
column_to_search="my_embedding", | ||
query=search_query, | ||
query_column_to_search="another_embedding", | ||
top_k=2, | ||
).to_pandas() # type:ignore | ||
expected = pd.DataFrame( | ||
{ | ||
"query_id": ["dog", "dog", "cat", "cat"], | ||
"embedding": [ | ||
np.array([1.0, 2.0]), | ||
np.array([1.0, 2.0]), | ||
np.array([3.0, 5.2]), | ||
np.array([3.0, 5.2]), | ||
], | ||
"another_embedding": [ | ||
np.array([1.0, 2.5]), | ||
np.array([1.0, 2.5]), | ||
np.array([3.3, 5.2]), | ||
np.array([3.3, 5.2]), | ||
], | ||
"id": [1, 4, 2, 5], | ||
"my_embedding": [ | ||
np.array([1.0, 2.0]), | ||
np.array([1.0, 3.2]), | ||
np.array([2.0, 4.0]), | ||
np.array([5.0, 5.4]), | ||
], | ||
"distance": [0.5, 0.7, 1.769181, 1.711724], | ||
}, | ||
index=pd.Index([0, 0, 1, 1], dtype="Int64"), | ||
) | ||
pd.testing.assert_frame_equal( | ||
vector_search_result, expected, check_dtype=False, rtol=0.1 | ||
) |
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