我的书签
添加书签
移除书签Methods
Methods
Embeddings
Embeddings databases are the engine that delivers semantic search. Data is transformed into embeddings vectors where similar concepts will produce similar vectors. Indexes both large and small are built with these vectors. The indexes are used to find results that have the same meaning, not necessarily the same keywords.
Source code in txtai/embeddings/base.py
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
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
``` | ```
class Embeddings:
“””
Embeddings databases are the engine that delivers semantic search. Data is transformed into embeddings vectors where similar concepts
will produce similar vectors. Indexes both large and small are built with these vectors. The indexes are used to find results
that have the same meaning, not necessarily the same keywords.
“””
# pylint: disable=W0231
def init(self, config=None, models=None, kwargs):
“””
Creates a new embeddings index. Embeddings indexes are thread-safe for read operations but writes must be synchronized.
Args:
config: embeddings configuration
models: models cache, used for model sharing between embeddings
kwargs: additional configuration as keyword args
“””
# Index configuration
self.config = None
# Dimensionality reduction - word vectors only
self.reducer = None
# Dense vector model - transforms data into similarity vectors
self.model = None
# Approximate nearest neighbor index
self.ann = None
# Index ids when content is disabled
self.ids = None
# Document database
self.database = None
# Resolvable functions
self.functions = None
# Graph network
self.graph = None
# Sparse vectors
self.scoring = None
# Query model
self.query = None
# Index archive
self.archive = None
# Subindexes for this embeddings instance
self.indexes = None
# Models cache
self.models = models
# Merge configuration into single dictionary
config = {config, kwargs} if config and kwargs else kwargs if kwargs else config
# Set initial configuration
self.configure(config)
def enter(self):
return self
def exit(self, *args):
self.close()
def score(self, documents):
“””
Builds a term weighting scoring index. Only used by word vectors models.
Args:
documents: iterable of (id, data, tags), (id, data) or data
“””
# Build scoring index for word vectors term weighting
if self.isweighted():
self.scoring.index(Stream(self)(documents))
def index(self, documents, reindex=False, checkpoint=None):
“””
Builds an embeddings index. This method overwrites an existing index.
Args:
documents: iterable of (id, data, tags), (id, data) or data
reindex: if this is a reindex operation in which case database creation is skipped, defaults to False
checkpoint: optional checkpoint directory, enables indexing restart
“””
# Initialize index
self.initindex(reindex)
# Create transform and stream
transform = Transform(self, Action.REINDEX if reindex else Action.INDEX, checkpoint)
stream = Stream(self, Action.REINDEX if reindex else Action.INDEX)
with tempfile.NamedTemporaryFile(mode=”wb”, suffix=”.npy”) as buffer:
# Load documents into database and transform to vectors
ids, dimensions, embeddings = transform(stream(documents), buffer)
if embeddings is not None:
# Build LSA model (if enabled). Remove principal components from embeddings.
if self.config.get(“pca”):
self.reducer = Reducer(embeddings, self.config[“pca”])
self.reducer(embeddings)
# Save index dimensions
self.config[“dimensions”] = dimensions
# Create approximate nearest neighbor index
self.ann = self.createann()
# Add embeddings to the index
self.ann.index(embeddings)
# Save indexids-ids mapping for indexes with no database, except when this is a reindex
if ids and not reindex and not self.database:
self.ids = self.createids(ids)
# Index scoring, if necessary
# This must occur before graph index in order to be available to the graph
if self.issparse():
self.scoring.index()
# Index subindexes, if necessary
if self.indexes:
self.indexes.index()
# Index graph, if necessary
if self.graph:
self.graph.index(Search(self, indexonly=True), Ids(self), self.batchsimilarity)
def upsert(self, documents, checkpoint=None):
“””
Runs an embeddings upsert operation. If the index exists, new data is
appended to the index, existing data is updated. If the index doesn’t exist,
this method runs a standard index operation.
Args:
documents: iterable of (id, data, tags), (id, data) or data
checkpoint: optional checkpoint directory, enables indexing restart
“””
# Run standard insert if index doesn’t exist or it has no records
if not self.count():
self.index(documents, checkpoint=checkpoint)
return
# Create transform and stream
transform = Transform(self, Action.UPSERT, checkpoint=checkpoint)
stream = Stream(self, Action.UPSERT)
with tempfile.NamedTemporaryFile(mode=”wb”, suffix=”.npy”) as buffer:
# Load documents into database and transform to vectors
ids, , embeddings = transform(stream(documents), buffer)
if embeddings is not None:
# Remove principal components from embeddings, if necessary
if self.reducer:
self.reducer(embeddings)
# Append embeddings to the index
self.ann.append(embeddings)
# Save indexids-ids mapping for indexes with no database
if ids and not self.database:
self.ids = self.createids(self.ids + ids)
# Scoring upsert, if necessary
# This must occur before graph upsert in order to be available to the graph
if self.issparse():
self.scoring.upsert()
# Subindexes upsert, if necessary
if self.indexes:
self.indexes.upsert()
# Graph upsert, if necessary
if self.graph:
self.graph.upsert(Search(self, indexonly=True), Ids(self), self.batchsimilarity)
def delete(self, ids):
“””
Deletes from an embeddings index. Returns list of ids deleted.
Args:
ids: list of ids to delete
Returns:
list of ids deleted
“””
# List of internal indices for each candidate id to delete
indices = []
# List of deleted ids
deletes = []
if self.database:
# Retrieve indexid-id mappings from database
ids = self.database.ids(ids)
# Parse out indices and ids to delete
indices = [i for i, in ids]
deletes = sorted(set(uid for _, uid in ids))
# Delete ids from database
self.database.delete(deletes)
elif self.ann or self.scoring:
# Find existing ids
for uid in ids:
indices.extend([index for index, value in enumerate(self.ids) if uid == value])
# Clear embeddings ids
for index in indices:
deletes.append(self.ids[index])
self.ids[index] = None
# Delete indices for all indexes and data stores
if indices:
# Delete ids from ann
if self.isdense():
self.ann.delete(indices)
# Delete ids from scoring
if self.issparse():
self.scoring.delete(indices)
# Delete ids from subindexes
if self.indexes:
self.indexes.delete(indices)
# Delete ids from graph
if self.graph:
self.graph.delete(indices)
return deletes
def reindex(self, config=None, function=None, kwargs):
“””
Recreates embeddings index using config. This method only works if document content storage is enabled.
Args:
config: new config
function: optional function to prepare content for indexing
kwargs: additional configuration as keyword args
“””
if self.database:
# Merge configuration into single dictionary
config = {config, kwargs} if config and kwargs else config if config else kwargs
# Keep content and objects parameters to ensure database is preserved
config[“content”] = self.config[“content”]
if “objects” in self.config:
config[“objects”] = self.config[“objects”]
# Reset configuration
self.configure(config)
# Reset function references
if self.functions:
self.functions.reset()
# Reindex
if function:
self.index(function(self.database.reindex(self.config)), True)
else:
self.index(self.database.reindex(self.config), True)
def transform(self, document, category=None, index=None):
“””
Transforms document into an embeddings vector.
Args:
documents: iterable of (id, data, tags), (id, data) or data
category: category for instruction-based embeddings
index: index name, if applicable
Returns:
embeddings vector
“””
return self.batchtransform([document], category, index)[0]
def batchtransform(self, documents, category=None, index=None):
“””
Transforms documents into embeddings vectors.
Args:
documents: iterable of (id, data, tags), (id, data) or data
category: category for instruction-based embeddings
index: index name, if applicable
Returns:
embeddings vectors
“””
# Initialize default parameters, if necessary
self.defaults()
# Get vector model
model = self.findmodel(index)
# Convert documents into embeddings
embeddings = model.batchtransform(Stream(self)(documents), category)
# Reduce the dimensionality of the embeddings. Scale the embeddings using this
# model to reduce the noise of common but less relevant terms.
if self.reducer:
self.reducer(embeddings)
return embeddings
def count(self):
“””
Total number of elements in this embeddings index.
Returns:
number of elements in this embeddings index
“””
if self.ann:
return self.ann.count()
if self.scoring:
return self.scoring.count()
if self.database:
return self.database.count()
if self.ids:
return len([uid for uid in self.ids if uid is not None])
# Default to 0 when no suitable method found
return 0
def search(self, query, limit=None, weights=None, index=None, parameters=None, graph=False):
“””
Finds documents most similar to the input query. This method runs an index search, index + database search
or a graph search, depending on the embeddings configuration and query.
Args:
query: input query
limit: maximum results
weights: hybrid score weights, if applicable
index: index name, if applicable
parameters: dict of named parameters to bind to placeholders
graph: return graph results if True
Returns:
list of (id, score) for index search
list of dict for an index + database search
graph when graph is set to True
“””
results = self.batchsearch([query], limit, weights, index, [parameters], graph)
return results[0] if results else results
def batchsearch(self, queries, limit=None, weights=None, index=None, parameters=None, graph=False):
“””
Finds documents most similar to the input query. This method runs an index search, index + database search
or a graph search, depending on the embeddings configuration and query.
Args:
queries: input queries
limit: maximum results
weights: hybrid score weights, if applicable
index: index name, if applicable
parameters: list of dicts of named parameters to bind to placeholders
graph: return graph results if True
Returns:
list of (id, score) per query for index search
list of dict per query for an index + database search
list of graph per query when graph is set to True
“””
# Determine if graphs should be returned
graph = graph if self.graph else False
# Execute search
results = Search(self, indexids=graph)(queries, limit, weights, index, parameters)
# Create subgraphs using results, if necessary
return [self.graph.filter(x) if isinstance(x, list) else x for x in results] if graph else results
def similarity(self, query, data):
“””
Computes the similarity between query and list of data. Returns a list of
(id, score) sorted by highest score, where id is the index in data.
Args:
query: input query
data: list of data
Returns:
list of (id, score)
“””
return self.batchsimilarity([query], data)[0]
def batchsimilarity(self, queries, data):
“””
Computes the similarity between list of queries and list of data. Returns a list
of (id, score) sorted by highest score per query, where id is the index in data.
Args:
queries: input queries
data: list of data
Returns:
list of (id, score) per query
“””
# Convert queries to embedding vectors
queries = self.batchtransform(((None, query, None) for query in queries), “query”)
data = self.batchtransform(((None, row, None) for row in data), “data”)
# Get vector model
model = self.findmodel()
# Dot product on normalized vectors is equal to cosine similarity
scores = model.dot(queries, data)
# Add index and sort desc based on score
return [sorted(enumerate(score), key=lambda x: x[1], reverse=True) for score in scores]
def explain(self, query, texts=None, limit=None):
“””
Explains the importance of each input token in text for a query. This method requires either content to be enabled
or texts to be provided.
Args:
query: input query
texts: optional list of (text|list of tokens), otherwise runs search query
limit: optional limit if texts is None
Returns:
list of dict per input text where a higher token scores represents higher importance relative to the query
“””
results = self.batchexplain([query], texts, limit)
return results[0] if results else results
def batchexplain(self, queries, texts=None, limit=None):
“””
Explains the importance of each input token in text for a list of queries. This method requires either content to be enabled
or texts to be provided.
Args:
queries: input queries
texts: optional list of (text|list of tokens), otherwise runs search queries
limit: optional limit if texts is None
Returns:
list of dict per input text per query where a higher token scores represents higher importance relative to the query
“””
return Explain(self)(queries, texts, limit)
def terms(self, query):
“””
Extracts keyword terms from a query.
Args:
query: input query
Returns:
query reduced down to keyword terms
“””
return self.batchterms([query])[0]
def batchterms(self, queries):
“””
Extracts keyword terms from a list of queries.
Args:
queries: list of queries
Returns:
list of queries reduced down to keyword term strings
“””
return Terms(self)(queries)
def exists(self, path=None, cloud=None, kwargs):
“””
Checks if an index exists at path.
Args:
path: input path
cloud: cloud storage configuration
kwargs: additional configuration as keyword args
Returns:
True if index exists, False otherwise
“””
# Check if this exists in a cloud instance
cloud = self.createcloud(cloud=cloud, kwargs)
if cloud:
return cloud.exists(path)
# Check if this is an archive file and exists
path, apath = self.checkarchive(path)
if apath:
return os.path.exists(apath)
# Return true if path has a config.json or config file with an offset set
return path and (os.path.exists(f”{path}/config.json”) or os.path.exists(f”{path}/config”)) and “offset” in Configuration().load(path)
def load(self, path=None, cloud=None, config=None, kwargs):
“””
Loads an existing index from path.
Args:
path: input path
cloud: cloud storage configuration
config: configuration overrides
kwargs: additional configuration as keyword args
Returns:
Embeddings
“””
# Load from cloud, if configured
cloud = self.createcloud(cloud=cloud, kwargs)
if cloud:
path = cloud.load(path)
# Check if this is an archive file and extract
path, apath = self.checkarchive(path)
if apath:
self.archive.load(apath)
# Load index configuration
self.config = Configuration().load(path)
# Apply config overrides
self.config = {self.config, config} if config else self.config
# Approximate nearest neighbor index - stores dense vectors
self.ann = self.createann()
if self.ann:
self.ann.load(f”{path}/embeddings”)
# Dimensionality reduction model - word vectors only
if self.config.get(“pca”):
self.reducer = Reducer()
self.reducer.load(f”{path}/lsa”)
# Index ids when content is disabled
self.ids = self.createids()
if self.ids:
self.ids.load(f”{path}/ids”)
# Document database - stores document content
self.database = self.createdatabase()
if self.database:
self.database.load(f”{path}/documents”)
# Sparse vectors - stores term sparse arrays
self.scoring = self.createscoring()
if self.scoring:
self.scoring.load(f”{path}/scoring”)
# Subindexes
self.indexes = self.createindexes()
if self.indexes:
self.indexes.load(f”{path}/indexes”)
# Graph network - stores relationships
self.graph = self.creategraph()
if self.graph:
self.graph.load(f”{path}/graph”)
# Dense vectors - transforms data to embeddings vectors
self.model = self.loadvectors()
# Query model
self.query = self.loadquery()
return self
def save(self, path, cloud=None, kwargs):
“””
Saves an index in a directory at path unless path ends with tar.gz, tar.bz2, tar.xz or zip.
In those cases, the index is stored as a compressed file.
Args:
path: output path
cloud: cloud storage configuration
kwargs: additional configuration as keyword args
“””
if self.config:
# Check if this is an archive file
path, apath = self.checkarchive(path)
# Create output directory, if necessary
os.makedirs(path, exist_ok=True)
# Save index configuration
Configuration().save(self.config, path)
# Save approximate nearest neighbor index
if self.ann:
self.ann.save(f”{path}/embeddings”)
# Save dimensionality reduction model (word vectors only)
if self.reducer:
self.reducer.save(f”{path}/lsa”)
# Save index ids
if self.ids:
self.ids.save(f”{path}/ids”)
# Save document database
if self.database:
self.database.save(f”{path}/documents”)
# Save scoring index
if self.scoring:
self.scoring.save(f”{path}/scoring”)
# Save subindexes
if self.indexes:
self.indexes.save(f”{path}/indexes”)
# Save graph
if self.graph:
self.graph.save(f”{path}/graph”)
# If this is an archive, save it
if apath:
self.archive.save(apath)
# Save to cloud, if configured
cloud = self.createcloud(cloud=cloud, kwargs)
if cloud:
cloud.save(apath if apath else path)
def close(self):
“””
Closes this embeddings index and frees all resources.
“””
self.config, self.archive = None, None
self.reducer, self.query = None, None
self.ids = None
# Close ANN
if self.ann:
self.ann.close()
self.ann = None
# Close database
if self.database:
self.database.close()
self.database, self.functions = None, None
# Close scoring
if self.scoring:
self.scoring.close()
self.scoring = None
# Close graph
if self.graph:
self.graph.close()
self.graph = None
# Close indexes
if self.indexes:
self.indexes.close()
self.indexes = None
# Close vectors model
if self.model:
self.model.close()
self.model = None
self.models = None
def info(self):
“””
Prints the current embeddings index configuration.
“””
if self.config:
# Print configuration
print(json.dumps(self.config, sort_keys=True, default=str, indent=2))
def issparse(self):
“””
Checks if this instance has an associated sparse keyword or sparse vectors scoring index.
Returns:
True if scoring has an associated sparse keyword/vector index, False otherwise
“””
return self.scoring and self.scoring.issparse()
def isdense(self):
“””
Checks if this instance has an associated ANN instance.
Returns:
True if this instance has an associated ANN, False otherwise
“””
return self.ann is not None
def isweighted(self):
“””
Checks if this instance has an associated scoring instance with term weighting enabled.
Returns:
True if term weighting is enabled, False otherwise
“””
return self.scoring and self.scoring.isweighted()
def findmodel(self, index=None):
“””
Finds the primary vector model used by this instance.
Returns:
Vectors
“””
return (
self.indexes.findmodel(index)
if index and self.indexes
else (
self.model
if self.model
else self.scoring.findmodel() if self.scoring and self.scoring.findmodel() else self.indexes.findmodel() if self.indexes else None
)
)
def configure(self, config):
“””
Sets the configuration for this embeddings index and loads config-driven models.
Args:
config: embeddings configuration
“””
# Configuration
self.config = config
# Dimensionality reduction model
self.reducer = None
# Create scoring instance for word vectors term weighting
scoring = self.config.get(“scoring”) if self.config else None
self.scoring = self.createscoring() if scoring and not self.hassparse() else None
# Dense vectors - transforms data to embeddings vectors
self.model = self.loadvectors() if self.config else None
# Query model
self.query = self.loadquery() if self.config else None
def initindex(self, reindex):
“””
Initialize new index.
Args:
reindex: if this is a reindex operation in which case database creation is skipped, defaults to False
“””
# Initialize default parameters, if necessary
self.defaults()
# Initialize index ids, only created when content is disabled
self.ids = None
# Create document database, if necessary
if not reindex:
self.database = self.createdatabase()
# Reset archive since this is a new index
self.archive = None
# Close existing ANN, if necessary
if self.ann:
self.ann.close()
# Initialize ANN, will be created after index transformations complete
self.ann = None
# Create scoring only if the scoring config is for a sparse index
if self.hassparse():
self.scoring = self.createscoring()
# Create subindexes, if necessary
self.indexes = self.createindexes()
# Create graph, if necessary
self.graph = self.creategraph()
def defaults(self):
“””
Apply default parameters to current configuration.
Returns:
configuration with default parameters set
“””
self.config = self.config if self.config else {}
# Expand sparse index shortcuts
if not self.config.get(“scoring”) and any(self.config.get(key) for key in [“keyword”, “sparse”, “hybrid”]):
self.defaultsparse()
# Expand graph shortcuts
if self.config.get(“graph”) is True:
self.config[“graph”] = {}
# Check if default model should be loaded
if not self.model and (self.defaultallowed() or self.config.get(“dense”)):
self.config[“path”] = “sentence-transformers/all-MiniLM-L6-v2”
# Load dense vectors model
self.model = self.loadvectors()
def defaultsparse(self):
“””
Logic to derive default sparse index configuration.
“””
# Check for keyword and hybrid parameters
method = None
for x in [“keyword”, “hybrid”]:
value = self.config.get(x)
if value:
method = value if isinstance(value, str) else “bm25”
# Enable dense index when hybrid enabled
if x == “hybrid”:
self.config[“dense”] = True
sparse = self.config.get(“sparse”, {})
if sparse or method == “sparse”:
# Sparse vector configuration
sparse = {“path”: self.config.get(“sparse”)} if isinstance(sparse, str) else {} if isinstance(sparse, bool) else sparse
sparse[“path”] = sparse.get(“path”, “opensearch-project/opensearch-neural-sparse-encoding-doc-v2-mini”)
# Merge in sparse parameters
self.config[“scoring”] = {{“method”: “sparse”}, sparse}
elif method:
# Sparse keyword configuration
self.config[“scoring”] = {“method”: method, “terms”: True, “normalize”: True}
def defaultallowed(self):
“””
Tests if this embeddings instance can use a default model if not otherwise provided.
Returns:
True if a default model is allowed, False otherwise
“””
params = [(“keyword”, False), (“sparse”, False), (“defaults”, True)]
return all(self.config.get(key, default) == default for key, default in params)
def loadvectors(self):
“””
Loads a vector model set in config.
Returns:
vector model
“””
# Create model cache if subindexes are enabled
if “indexes” in self.config and self.models is None:
self.models = {}
# Support path via dense parameter
dense = self.config.get(“dense”)
if not self.config.get(“path”) and dense and isinstance(dense, str):
self.config[“path”] = dense
# Load vector model
return VectorsFactory.create(self.config, self.scoring, self.models)
def loadquery(self):
“””
Loads a query model set in config.
Returns:
query model
“””
if “query” in self.config:
return Query(self.config[“query”])
return None
def checkarchive(self, path):
“””
Checks if path is an archive file.
Args:
path: path to check
Returns:
(working directory, current path) if this is an archive, original path otherwise
“””
# Create archive instance, if necessary
self.archive = ArchiveFactory.create()
# Check if path is an archive file
if self.archive.isarchive(path):
# Return temporary archive working directory and original path
return self.archive.path(), path
return path, None
def createcloud(self, cloud):
“””
Creates a cloud instance from config.
Args:
cloud: cloud configuration
“””
# Merge keyword args and keys under the cloud parameter
config = cloud
if “cloud” in config and config[“cloud”]:
config.update(config.pop(“cloud”))
# Create cloud instance from config and return
return CloudFactory.create(config) if config else None
def createann(self):
“””
Creates an ANN from config.
Returns:
new ANN, if enabled in config
“””
# Free existing resources
if self.ann:
self.ann.close()
return ANNFactory.create(self.config) if self.config.get(“path”) or self.defaultallowed() else None
def createdatabase(self):
“””
Creates a database from config. This method will also close any existing database connection.
Returns:
new database, if enabled in config
“””
# Free existing resources
if self.database:
self.database.close()
config = self.config.copy()
# Create references to callable functions
self.functions = Functions(self) if “functions” in config else None
if self.functions:
config[“functions”] = self.functions(config)
# Create database from config and return
return DatabaseFactory.create(config)
def creategraph(self):
“””
Creates a graph from config.
Returns:
new graph, if enabled in config
“””
# Free existing resources
if self.graph:
self.graph.close()
if “graph” in self.config:
# Get or create graph configuration
config = self.config[“graph”] if “graph” in self.config else {}
# Create configuration with custom columns, if necessary
config = self.columns(config)
return GraphFactory.create(config)
return None
def createids(self, ids=None):
“””
Creates indexids when content is disabled.
Args:
ids: optional ids to add
Returns:
new indexids, if content disabled
“””
# Load index ids when content is disabled
return IndexIds(self, ids) if not self.config.get(“content”) else None
def createindexes(self):
“””
Creates subindexes from config.
Returns:
list of subindexes
“””
# Free existing resources
if self.indexes:
self.indexes.close()
# Load subindexes
if “indexes” in self.config:
indexes = {}
for index, config in self.config[“indexes”].items():
# Create index with shared model cache
indexes[index] = Embeddings(config, models=self.models)
# Wrap as Indexes object
return Indexes(self, indexes)
return None
def createscoring(self):
“””
Creates a scoring from config.
Returns:
new scoring, if enabled in config
“””
# Free existing resources
if self.scoring:
self.scoring.close()
if “scoring” in self.config:
# Expand scoring to a dictionary, if necessary
config = self.config[“scoring”]
config = config if isinstance(config, dict) else {“method”: config}
# Create configuration with custom columns, if necessary
config = self.columns(config)
return ScoringFactory.create(config, self.models)
return None
def hassparse(self):
“””
Checks is this embeddings database has an associated sparse index.
Returns:
True if this embeddings has an associated scoring index
“””
# Create scoring only if scoring is a sparse keyword/vector index
return ScoringFactory.issparse(self.config.get(“scoring”))
def columns(self, config):
“””
Adds custom text/object column information if it’s provided.
Args:
config: input configuration
Returns:
config with column information added
“””
# Add text/object columns if custom
if “columns” in self.config:
# Work on copy of configuration
config = config.copy()
# Copy columns to config
config[“columns”] = self.config[“columns”]
return config
---|---
__init__(config=None, models=None, **kwargs)
Creates a new embeddings index. Embeddings indexes are thread-safe for read operations but writes must be synchronized.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
config |
embeddings configuration | None |
|
models |
models cache, used for model sharing between embeddings | None |
|
kwargs |
additional configuration as keyword args | {} |
Source code in txtai/embeddings/base.py
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
``` | ```
def init(self, config=None, models=None, kwargs):
“””
Creates a new embeddings index. Embeddings indexes are thread-safe for read operations but writes must be synchronized.
Args:
config: embeddings configuration
models: models cache, used for model sharing between embeddings
kwargs: additional configuration as keyword args
“””
# Index configuration
self.config = None
# Dimensionality reduction - word vectors only
self.reducer = None
# Dense vector model - transforms data into similarity vectors
self.model = None
# Approximate nearest neighbor index
self.ann = None
# Index ids when content is disabled
self.ids = None
# Document database
self.database = None
# Resolvable functions
self.functions = None
# Graph network
self.graph = None
# Sparse vectors
self.scoring = None
# Query model
self.query = None
# Index archive
self.archive = None
# Subindexes for this embeddings instance
self.indexes = None
# Models cache
self.models = models
# Merge configuration into single dictionary
config = {config, **kwargs} if config and kwargs else kwargs if kwargs else config
# Set initial configuration
self.configure(config)
---|---
batchexplain(queries, texts=None, limit=None)
Explains the importance of each input token in text for a list of queries. This method requires either content to be enabled or texts to be provided.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
queries |
input queries | required | |
texts |
optional list of (text | list of tokens), otherwise runs search queries | |
limit |
optional limit if texts is None | None |
Returns:
| Type | Description |
|---|---|
| list of dict per input text per query where a higher token scores represents higher importance relative to the query |
Source code in txtai/embeddings/base.py
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
``` | ```
def batchexplain(self, queries, texts=None, limit=None):
“””
Explains the importance of each input token in text for a list of queries. This method requires either content to be enabled
or texts to be provided.
Args:
queries: input queries
texts: optional list of (text|list of tokens), otherwise runs search queries
limit: optional limit if texts is None
Returns:
list of dict per input text per query where a higher token scores represents higher importance relative to the query
“””
return Explain(self)(queries, texts, limit)
---|---
（本文超出站点单文档体积上限，已按小节截断）
