This repository was archived by the owner on Sep 9, 2026. It is now read-only.
Repository navigation
Expand file tree
/
Copy pathtest_sequence.py
More file actions
280 lines (241 loc) · 9 KB
/
Copy pathtest_sequence.py
File metadata and controls
280 lines (241 loc) · 9 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
import tempfile
import uuid
import numpy as np
import pytest
from docarray import Document, DocumentArray
from docarray.array.elastic import DocumentArrayElastic
from docarray.array.memory import DocumentArrayInMemory
from docarray.array.opensearch import DocumentArrayOpenSearch
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.redis import DocumentArrayRedis
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.storage.elastic import ElasticConfig
from docarray.array.storage.opensearch import OpenSearchConfig
from docarray.array.storage.qdrant import QdrantConfig
from docarray.array.storage.redis import RedisConfig
from docarray.array.storage.sqlite import SqliteConfig
from docarray.array.storage.weaviate import WeaviateConfig
from docarray.array.weaviate import DocumentArrayWeaviate
from docarray.array.milvus import DocumentArrayMilvus, MilvusConfig
from tests.conftest import tmpfile
@pytest.mark.parametrize(
'da_cls,config',
[
(DocumentArrayInMemory, lambda: None),
(DocumentArraySqlite, lambda: None),
(DocumentArrayWeaviate, lambda: WeaviateConfig(n_dim=1)),
(DocumentArrayQdrant, lambda: QdrantConfig(n_dim=1)),
(DocumentArrayElastic, lambda: ElasticConfig(n_dim=1)),
(DocumentArrayRedis, lambda: RedisConfig(n_dim=1)),
(DocumentArrayMilvus, lambda: MilvusConfig(n_dim=128)),
(DocumentArrayOpenSearch, lambda: OpenSearchConfig(n_dim=1)),
],
)
def test_insert(da_cls, config, start_storage):
da = da_cls(config=config())
assert not len(da)
da.insert(0, Document(text='hello', id="0"))
da.insert(0, Document(text='world', id="1"))
assert len(da) == 2
assert da[0].text == 'world'
assert da[1].text == 'hello'
assert da["1"].text == 'world'
assert da["0"].text == 'hello'
@pytest.mark.parametrize(
'da_cls,config',
[
(DocumentArrayInMemory, lambda: None),
(DocumentArraySqlite, lambda: None),
(DocumentArrayWeaviate, lambda: WeaviateConfig(n_dim=1)),
(DocumentArrayQdrant, lambda: QdrantConfig(n_dim=1)),
(DocumentArrayElastic, lambda: ElasticConfig(n_dim=1)),
(DocumentArrayRedis, lambda: RedisConfig(n_dim=1)),
(DocumentArrayMilvus, lambda: MilvusConfig(n_dim=128)),
],
)
def test_append_extend(da_cls, config, start_storage):
da = da_cls(config=config())
da.append(Document())
da.append(Document())
assert len(da) == 2
# assert len(da._offset2ids.ids) == 2 will not work unless used in a context manager
da.extend([Document(), Document()])
assert len(da) == 4
# assert len(da._offset2ids.ids) == 4 will not work unless used in a context manager
def update_config_inplace(config, tmpdir, tmpfile):
variable_names = ['table_name', 'connection', 'collection_name', 'index_name']
variable_names_db = ['connection']
for field in variable_names_db:
if field in config:
config[field] = str(tmpfile)
for field in variable_names:
if field in config:
config[field] = f'{config[field]}_{uuid.uuid4().hex}'
@pytest.mark.parametrize(
'storage, config',
[
('sqlite', {'table_name': 'Test', 'connection': 'sqlite'}),
('weaviate', {'n_dim': 3, 'name': 'Weaviate'}),
('qdrant', {'n_dim': 3, 'collection_name': 'qdrant'}),
('elasticsearch', {'n_dim': 3, 'index_name': 'elasticsearch'}),
('opensearch', {'n_dim': 3, 'index_name': 'opensearch'}),
('redis', {'n_dim': 3, 'index_name': 'redis'}),
('milvus', {'n_dim': 3, 'collection_name': 'redis'}),
],
)
def test_context_manager_from_disk(storage, config, start_storage, tmpdir, tmpfile):
config = config
update_config_inplace(config, tmpdir, tmpfile)
da = DocumentArray(storage=storage, config=config)
with da as da_open:
da_open.append(Document(embedding=np.random.random(3)))
da_open.append(Document(embedding=np.random.random(3)))
assert len(da) == 2
assert len(da._offset2ids.ids) == 2
da2 = DocumentArray(storage=storage, config=config)
assert len(da2) == 2
assert len(da2._offset2ids.ids) == 2
# Cleanup modifications made in test
with da:
del da[0]
del da[0]
@pytest.mark.parametrize(
'storage, config',
[
('memory', None),
('weaviate', {'n_dim': 3, 'distance': 'l2-squared'}),
('annlite', {'n_dim': 3, 'metric': 'Euclidean'}),
('qdrant', {'n_dim': 3, 'distance': 'euclidean'}),
('elasticsearch', {'n_dim': 3, 'distance': 'l2_norm'}),
('sqlite', dict()),
('redis', {'n_dim': 3, 'distance': 'L2'}),
('milvus', {'n_dim': 3, 'distance': 'L2'}),
('opensearch', {'n_dim': 3, 'distance': 'l2'}),
],
)
def test_extend_subindex(storage, config, start_storage):
n_dim = 3
subindex_configs = (
{'@c': dict()} if storage in ['sqlite', 'memory'] else {'@c': {'n_dim': 2}}
)
da = DocumentArray(
storage=storage,
config=config,
subindex_configs=subindex_configs,
)
with da:
da.extend(
[
Document(
id=str(i),
embedding=i * np.ones(n_dim),
chunks=[
Document(id=str(i) + '_0', embedding=np.array([i, i])),
Document(id=str(i) + '_1', embedding=np.array([i, i])),
],
)
for i in range(3)
]
)
assert len(da._subindices['@c']) == 6
for j in range(2):
for i in range(3):
assert (da._subindices['@c'][f'{i}_{j}'].embedding == [i, i]).all()
@pytest.mark.parametrize(
'storage, config',
[
('memory', None),
('weaviate', {'n_dim': 3, 'distance': 'l2-squared'}),
('annlite', {'n_dim': 3, 'metric': 'Euclidean'}),
('qdrant', {'n_dim': 3, 'distance': 'euclidean'}),
('elasticsearch', {'n_dim': 3, 'distance': 'l2_norm'}),
('sqlite', dict()),
('redis', {'n_dim': 3, 'distance': 'L2'}),
('milvus', {'n_dim': 3, 'distance': 'L2'}),
('opensearch', {'n_dim': 3, 'distance': 'l2'}),
],
)
def test_append_subindex(storage, config, start_storage):
n_dim = 3
subindex_configs = (
{'@c': dict()} if storage in ['sqlite', 'memory'] else {'@c': {'n_dim': 2}}
)
da = DocumentArray(
storage=storage,
config=config,
subindex_configs=subindex_configs,
)
with da:
da.append(
Document(
embedding=np.ones(n_dim),
chunks=[
Document(id='0', embedding=np.array([0, 0])),
Document(id='1', embedding=np.array([1, 1])),
],
)
)
with da:
assert len(da._subindices['@c']) == 2
for i in range(2):
assert embeddings_eq(da._subindices['@c'][f'{i}'].embedding, [i, i])
def embeddings_eq(emb1, emb2):
b = emb1 == emb2
if isinstance(b, bool):
return b
else:
return b.all()
@pytest.mark.parametrize(
'storage, config',
[
('memory', None),
('weaviate', {'n_dim': 3, 'distance': 'l2-squared'}),
('annlite', {'n_dim': 3, 'metric': 'Euclidean'}),
('qdrant', {'n_dim': 3, 'distance': 'euclidean'}),
('elasticsearch', {'n_dim': 3, 'distance': 'l2_norm'}),
('sqlite', dict()),
('redis', {'n_dim': 3, 'distance': 'L2'}),
('milvus', {'n_dim': 3, 'distance': 'L2'}),
('opensearch', {'n_dim': 3, 'distance': 'l2'}),
],
)
@pytest.mark.parametrize(
'index', [1, '1', slice(1, 2), [1], [False, True, False, False, False]]
)
def test_del_and_append(index, storage, config, start_storage):
da = DocumentArray(storage=storage, config=config)
with da:
da.extend([Document(id=str(i)) for i in range(5)])
with da:
del da[index]
da.append(Document(id='new'))
assert da[:, 'id'] == ['0', '2', '3', '4', 'new']
@pytest.mark.parametrize(
'storage, config',
[
('memory', None),
('weaviate', {'n_dim': 3, 'distance': 'l2-squared'}),
('annlite', {'n_dim': 3, 'metric': 'Euclidean'}),
('qdrant', {'n_dim': 3, 'distance': 'euclidean'}),
('elasticsearch', {'n_dim': 3, 'distance': 'l2_norm'}),
('sqlite', dict()),
('redis', {'n_dim': 3, 'distance': 'L2'}),
('milvus', {'n_dim': 3, 'distance': 'L2'}),
('opensearch', {'n_dim': 3, 'distance': 'l2'}),
],
)
@pytest.mark.parametrize(
'index', [1, '1', slice(1, 2), [1], [False, True, False, False, False]]
)
def test_set_and_append(index, storage, config, start_storage):
da = DocumentArray(storage=storage, config=config)
with da:
da.extend([Document(id=str(i)) for i in range(5)])
with da:
da[index] = (
Document(id='new')
if isinstance(index, int) or isinstance(index, str)
else [Document(id='new')]
)
da.append(Document(id='new_new'))
assert da[:, 'id'] == ['0', 'new', '2', '3', '4', 'new_new']