forked from openai/openai-node
-
Notifications
You must be signed in to change notification settings - Fork 0
/
api.ts
3083 lines (2926 loc) · 133 KB
/
api.ts
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
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
/* tslint:disable */
/* eslint-disable */
/**
* OpenAI API
* APIs for sampling from and fine-tuning language models
*
* The version of the OpenAPI document: 1.0.6
*
*
* NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).
* https://openapi-generator.tech
* Do not edit the class manually.
*/
import { Configuration } from './configuration';
import globalAxios, { AxiosPromise, AxiosInstance, AxiosRequestConfig } from 'axios';
// Some imports not used depending on template conditions
// @ts-ignore
import { DUMMY_BASE_URL, assertParamExists, setApiKeyToObject, setBasicAuthToObject, setBearerAuthToObject, setOAuthToObject, setSearchParams, serializeDataIfNeeded, toPathString, createRequestFunction } from './common';
// @ts-ignore
import { BASE_PATH, COLLECTION_FORMATS, RequestArgs, BaseAPI, RequiredError } from './base';
/**
*
* @export
* @interface CreateAnswerRequest
*/
export interface CreateAnswerRequest {
/**
* ID of the model to use for completion. You can select one of `ada`, `babbage`, `curie`, or `davinci`.
* @type {string}
* @memberof CreateAnswerRequest
*/
'model': string;
/**
* Question to get answered.
* @type {string}
* @memberof CreateAnswerRequest
*/
'question': string;
/**
* List of (question, answer) pairs that will help steer the model towards the tone and answer format you\'d like. We recommend adding 2 to 3 examples.
* @type {Array<any>}
* @memberof CreateAnswerRequest
*/
'examples': Array<any>;
/**
* A text snippet containing the contextual information used to generate the answers for the `examples` you provide.
* @type {string}
* @memberof CreateAnswerRequest
*/
'examples_context': string;
/**
* List of documents from which the answer for the input `question` should be derived. If this is an empty list, the question will be answered based on the question-answer examples. You should specify either `documents` or a `file`, but not both.
* @type {Array<string>}
* @memberof CreateAnswerRequest
*/
'documents'?: Array<string> | null;
/**
* The ID of an uploaded file that contains documents to search over. See [upload file](/docs/api-reference/files/upload) for how to upload a file of the desired format and purpose. You should specify either `documents` or a `file`, but not both.
* @type {string}
* @memberof CreateAnswerRequest
*/
'file'?: string | null;
/**
* ID of the model to use for [Search](/docs/api-reference/searches/create). You can select one of `ada`, `babbage`, `curie`, or `davinci`.
* @type {string}
* @memberof CreateAnswerRequest
*/
'search_model'?: string | null;
/**
* The maximum number of documents to be ranked by [Search](/docs/api-reference/searches/create) when using `file`. Setting it to a higher value leads to improved accuracy but with increased latency and cost.
* @type {number}
* @memberof CreateAnswerRequest
*/
'max_rerank'?: number | null;
/**
* What [sampling temperature](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277) to use. Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer.
* @type {number}
* @memberof CreateAnswerRequest
*/
'temperature'?: number | null;
/**
* Include the log probabilities on the `logprobs` most likely tokens, as well the chosen tokens. For example, if `logprobs` is 5, the API will return a list of the 5 most likely tokens. The API will always return the `logprob` of the sampled token, so there may be up to `logprobs+1` elements in the response. The maximum value for `logprobs` is 5. If you need more than this, please contact us through our [Help center](https://help.openai.com) and describe your use case. When `logprobs` is set, `completion` will be automatically added into `expand` to get the logprobs.
* @type {number}
* @memberof CreateAnswerRequest
*/
'logprobs'?: number | null;
/**
* The maximum number of tokens allowed for the generated answer
* @type {number}
* @memberof CreateAnswerRequest
*/
'max_tokens'?: number | null;
/**
*
* @type {CreateAnswerRequestStop}
* @memberof CreateAnswerRequest
*/
'stop'?: CreateAnswerRequestStop | null;
/**
* How many answers to generate for each question.
* @type {number}
* @memberof CreateAnswerRequest
*/
'n'?: number | null;
/**
* Modify the likelihood of specified tokens appearing in the completion. Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this [tokenizer tool](/tokenizer?view=bpe) (which works for both GPT-2 and GPT-3) to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. As an example, you can pass `{\"50256\": -100}` to prevent the <|endoftext|> token from being generated.
* @type {object}
* @memberof CreateAnswerRequest
*/
'logit_bias'?: object | null;
/**
* A special boolean flag for showing metadata. If set to `true`, each document entry in the returned JSON will contain a \"metadata\" field. This flag only takes effect when `file` is set.
* @type {boolean}
* @memberof CreateAnswerRequest
*/
'return_metadata'?: boolean | null;
/**
* If set to `true`, the returned JSON will include a \"prompt\" field containing the final prompt that was used to request a completion. This is mainly useful for debugging purposes.
* @type {boolean}
* @memberof CreateAnswerRequest
*/
'return_prompt'?: boolean | null;
/**
* If an object name is in the list, we provide the full information of the object; otherwise, we only provide the object ID. Currently we support `completion` and `file` objects for expansion.
* @type {Array<any>}
* @memberof CreateAnswerRequest
*/
'expand'?: Array<any> | null;
/**
* A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse.
* @type {string}
* @memberof CreateAnswerRequest
*/
'user'?: string;
}
/**
* @type CreateAnswerRequestStop
* Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
* @export
*/
export type CreateAnswerRequestStop = Array<string> | string;
/**
*
* @export
* @interface CreateAnswerResponse
*/
export interface CreateAnswerResponse {
/**
*
* @type {string}
* @memberof CreateAnswerResponse
*/
'object'?: string;
/**
*
* @type {string}
* @memberof CreateAnswerResponse
*/
'model'?: string;
/**
*
* @type {string}
* @memberof CreateAnswerResponse
*/
'search_model'?: string;
/**
*
* @type {string}
* @memberof CreateAnswerResponse
*/
'completion'?: string;
/**
*
* @type {Array<string>}
* @memberof CreateAnswerResponse
*/
'answers'?: Array<string>;
/**
*
* @type {Array<CreateAnswerResponseSelectedDocumentsInner>}
* @memberof CreateAnswerResponse
*/
'selected_documents'?: Array<CreateAnswerResponseSelectedDocumentsInner>;
}
/**
*
* @export
* @interface CreateAnswerResponseSelectedDocumentsInner
*/
export interface CreateAnswerResponseSelectedDocumentsInner {
/**
*
* @type {number}
* @memberof CreateAnswerResponseSelectedDocumentsInner
*/
'document'?: number;
/**
*
* @type {string}
* @memberof CreateAnswerResponseSelectedDocumentsInner
*/
'text'?: string;
}
/**
*
* @export
* @interface CreateClassificationRequest
*/
export interface CreateClassificationRequest {
/**
* ID of the model to use. You can use the [List models](/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](/docs/models/overview) for descriptions of them.
* @type {string}
* @memberof CreateClassificationRequest
*/
'model': string;
/**
* Query to be classified.
* @type {string}
* @memberof CreateClassificationRequest
*/
'query': string;
/**
* A list of examples with labels, in the following format: `[[\"The movie is so interesting.\", \"Positive\"], [\"It is quite boring.\", \"Negative\"], ...]` All the label strings will be normalized to be capitalized. You should specify either `examples` or `file`, but not both.
* @type {Array<any>}
* @memberof CreateClassificationRequest
*/
'examples'?: Array<any> | null;
/**
* The ID of the uploaded file that contains training examples. See [upload file](/docs/api-reference/files/upload) for how to upload a file of the desired format and purpose. You should specify either `examples` or `file`, but not both.
* @type {string}
* @memberof CreateClassificationRequest
*/
'file'?: string | null;
/**
* The set of categories being classified. If not specified, candidate labels will be automatically collected from the examples you provide. All the label strings will be normalized to be capitalized.
* @type {Array<string>}
* @memberof CreateClassificationRequest
*/
'labels'?: Array<string> | null;
/**
* ID of the model to use for [Search](/docs/api-reference/searches/create). You can select one of `ada`, `babbage`, `curie`, or `davinci`.
* @type {string}
* @memberof CreateClassificationRequest
*/
'search_model'?: string | null;
/**
* What sampling `temperature` to use. Higher values mean the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
* @type {number}
* @memberof CreateClassificationRequest
*/
'temperature'?: number | null;
/**
* Include the log probabilities on the `logprobs` most likely tokens, as well the chosen tokens. For example, if `logprobs` is 5, the API will return a list of the 5 most likely tokens. The API will always return the `logprob` of the sampled token, so there may be up to `logprobs+1` elements in the response. The maximum value for `logprobs` is 5. If you need more than this, please contact us through our [Help center](https://help.openai.com) and describe your use case. When `logprobs` is set, `completion` will be automatically added into `expand` to get the logprobs.
* @type {number}
* @memberof CreateClassificationRequest
*/
'logprobs'?: number | null;
/**
* The maximum number of examples to be ranked by [Search](/docs/api-reference/searches/create) when using `file`. Setting it to a higher value leads to improved accuracy but with increased latency and cost.
* @type {number}
* @memberof CreateClassificationRequest
*/
'max_examples'?: number | null;
/**
* Modify the likelihood of specified tokens appearing in the completion. Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this [tokenizer tool](/tokenizer?view=bpe) (which works for both GPT-2 and GPT-3) to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. As an example, you can pass `{\"50256\": -100}` to prevent the <|endoftext|> token from being generated.
* @type {object}
* @memberof CreateClassificationRequest
*/
'logit_bias'?: object | null;
/**
* If set to `true`, the returned JSON will include a \"prompt\" field containing the final prompt that was used to request a completion. This is mainly useful for debugging purposes.
* @type {boolean}
* @memberof CreateClassificationRequest
*/
'return_prompt'?: boolean | null;
/**
* A special boolean flag for showing metadata. If set to `true`, each document entry in the returned JSON will contain a \"metadata\" field. This flag only takes effect when `file` is set.
* @type {boolean}
* @memberof CreateClassificationRequest
*/
'return_metadata'?: boolean | null;
/**
* If an object name is in the list, we provide the full information of the object; otherwise, we only provide the object ID. Currently we support `completion` and `file` objects for expansion.
* @type {Array<any>}
* @memberof CreateClassificationRequest
*/
'expand'?: Array<any> | null;
/**
* A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse.
* @type {string}
* @memberof CreateClassificationRequest
*/
'user'?: string;
}
/**
*
* @export
* @interface CreateClassificationResponse
*/
export interface CreateClassificationResponse {
/**
*
* @type {string}
* @memberof CreateClassificationResponse
*/
'object'?: string;
/**
*
* @type {string}
* @memberof CreateClassificationResponse
*/
'model'?: string;
/**
*
* @type {string}
* @memberof CreateClassificationResponse
*/
'search_model'?: string;
/**
*
* @type {string}
* @memberof CreateClassificationResponse
*/
'completion'?: string;
/**
*
* @type {string}
* @memberof CreateClassificationResponse
*/
'label'?: string;
/**
*
* @type {Array<CreateClassificationResponseSelectedExamplesInner>}
* @memberof CreateClassificationResponse
*/
'selected_examples'?: Array<CreateClassificationResponseSelectedExamplesInner>;
}
/**
*
* @export
* @interface CreateClassificationResponseSelectedExamplesInner
*/
export interface CreateClassificationResponseSelectedExamplesInner {
/**
*
* @type {number}
* @memberof CreateClassificationResponseSelectedExamplesInner
*/
'document'?: number;
/**
*
* @type {string}
* @memberof CreateClassificationResponseSelectedExamplesInner
*/
'text'?: string;
/**
*
* @type {string}
* @memberof CreateClassificationResponseSelectedExamplesInner
*/
'label'?: string;
}
/**
*
* @export
* @interface CreateCompletionRequest
*/
export interface CreateCompletionRequest {
/**
* ID of the model to use. You can use the [List models](/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](/docs/models/overview) for descriptions of them.
* @type {string}
* @memberof CreateCompletionRequest
*/
'model': string;
/**
*
* @type {CreateCompletionRequestPrompt}
* @memberof CreateCompletionRequest
*/
'prompt'?: CreateCompletionRequestPrompt | null;
/**
* The suffix that comes after a completion of inserted text.
* @type {string}
* @memberof CreateCompletionRequest
*/
'suffix'?: string | null;
/**
* The maximum number of [tokens](/tokenizer) to generate in the completion. The token count of your prompt plus `max_tokens` cannot exceed the model\'s context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096).
* @type {number}
* @memberof CreateCompletionRequest
*/
'max_tokens'?: number | null;
/**
* What [sampling temperature](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277) to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend altering this or `top_p` but not both.
* @type {number}
* @memberof CreateCompletionRequest
*/
'temperature'?: number | null;
/**
* An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or `temperature` but not both.
* @type {number}
* @memberof CreateCompletionRequest
*/
'top_p'?: number | null;
/**
* How many completions to generate for each prompt. **Note:** Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for `max_tokens` and `stop`.
* @type {number}
* @memberof CreateCompletionRequest
*/
'n'?: number | null;
/**
* Whether to stream back partial progress. If set, tokens will be sent as data-only [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format) as they become available, with the stream terminated by a `data: [DONE]` message.
* @type {boolean}
* @memberof CreateCompletionRequest
*/
'stream'?: boolean | null;
/**
* Include the log probabilities on the `logprobs` most likely tokens, as well the chosen tokens. For example, if `logprobs` is 5, the API will return a list of the 5 most likely tokens. The API will always return the `logprob` of the sampled token, so there may be up to `logprobs+1` elements in the response. The maximum value for `logprobs` is 5. If you need more than this, please contact us through our [Help center](https://help.openai.com) and describe your use case.
* @type {number}
* @memberof CreateCompletionRequest
*/
'logprobs'?: number | null;
/**
* Echo back the prompt in addition to the completion
* @type {boolean}
* @memberof CreateCompletionRequest
*/
'echo'?: boolean | null;
/**
*
* @type {CreateCompletionRequestStop}
* @memberof CreateCompletionRequest
*/
'stop'?: CreateCompletionRequestStop | null;
/**
* Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model\'s likelihood to talk about new topics. [See more information about frequency and presence penalties.](/docs/api-reference/parameter-details)
* @type {number}
* @memberof CreateCompletionRequest
*/
'presence_penalty'?: number | null;
/**
* Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model\'s likelihood to repeat the same line verbatim. [See more information about frequency and presence penalties.](/docs/api-reference/parameter-details)
* @type {number}
* @memberof CreateCompletionRequest
*/
'frequency_penalty'?: number | null;
/**
* Generates `best_of` completions server-side and returns the \"best\" (the one with the highest log probability per token). Results cannot be streamed. When used with `n`, `best_of` controls the number of candidate completions and `n` specifies how many to return – `best_of` must be greater than `n`. **Note:** Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for `max_tokens` and `stop`.
* @type {number}
* @memberof CreateCompletionRequest
*/
'best_of'?: number | null;
/**
* Modify the likelihood of specified tokens appearing in the completion. Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this [tokenizer tool](/tokenizer?view=bpe) (which works for both GPT-2 and GPT-3) to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. As an example, you can pass `{\"50256\": -100}` to prevent the <|endoftext|> token from being generated.
* @type {object}
* @memberof CreateCompletionRequest
*/
'logit_bias'?: object | null;
/**
* A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse.
* @type {string}
* @memberof CreateCompletionRequest
*/
'user'?: string;
}
/**
* @type CreateCompletionRequestPrompt
* The prompt(s) to generate completions for, encoded as a string, array of strings, array of tokens, or array of token arrays. Note that <|endoftext|> is the document separator that the model sees during training, so if a prompt is not specified the model will generate as if from the beginning of a new document.
* @export
*/
export type CreateCompletionRequestPrompt = Array<any> | Array<number> | Array<string> | string;
/**
* @type CreateCompletionRequestStop
* Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
* @export
*/
export type CreateCompletionRequestStop = Array<string> | string;
/**
*
* @export
* @interface CreateCompletionResponse
*/
export interface CreateCompletionResponse {
/**
*
* @type {string}
* @memberof CreateCompletionResponse
*/
'id': string;
/**
*
* @type {string}
* @memberof CreateCompletionResponse
*/
'object': string;
/**
*
* @type {number}
* @memberof CreateCompletionResponse
*/
'created': number;
/**
*
* @type {string}
* @memberof CreateCompletionResponse
*/
'model': string;
/**
*
* @type {Array<CreateCompletionResponseChoicesInner>}
* @memberof CreateCompletionResponse
*/
'choices': Array<CreateCompletionResponseChoicesInner>;
/**
*
* @type {CreateCompletionResponseUsage}
* @memberof CreateCompletionResponse
*/
'usage'?: CreateCompletionResponseUsage;
}
/**
*
* @export
* @interface CreateCompletionResponseChoicesInner
*/
export interface CreateCompletionResponseChoicesInner {
/**
*
* @type {string}
* @memberof CreateCompletionResponseChoicesInner
*/
'text'?: string;
/**
*
* @type {number}
* @memberof CreateCompletionResponseChoicesInner
*/
'index'?: number;
/**
*
* @type {CreateCompletionResponseChoicesInnerLogprobs}
* @memberof CreateCompletionResponseChoicesInner
*/
'logprobs'?: CreateCompletionResponseChoicesInnerLogprobs | null;
/**
*
* @type {string}
* @memberof CreateCompletionResponseChoicesInner
*/
'finish_reason'?: string;
}
/**
*
* @export
* @interface CreateCompletionResponseChoicesInnerLogprobs
*/
export interface CreateCompletionResponseChoicesInnerLogprobs {
/**
*
* @type {Array<string>}
* @memberof CreateCompletionResponseChoicesInnerLogprobs
*/
'tokens'?: Array<string>;
/**
*
* @type {Array<number>}
* @memberof CreateCompletionResponseChoicesInnerLogprobs
*/
'token_logprobs'?: Array<number>;
/**
*
* @type {Array<object>}
* @memberof CreateCompletionResponseChoicesInnerLogprobs
*/
'top_logprobs'?: Array<object>;
/**
*
* @type {Array<number>}
* @memberof CreateCompletionResponseChoicesInnerLogprobs
*/
'text_offset'?: Array<number>;
}
/**
*
* @export
* @interface CreateCompletionResponseUsage
*/
export interface CreateCompletionResponseUsage {
/**
*
* @type {number}
* @memberof CreateCompletionResponseUsage
*/
'prompt_tokens': number;
/**
*
* @type {number}
* @memberof CreateCompletionResponseUsage
*/
'completion_tokens': number;
/**
*
* @type {number}
* @memberof CreateCompletionResponseUsage
*/
'total_tokens': number;
}
/**
*
* @export
* @interface CreateEditRequest
*/
export interface CreateEditRequest {
/**
* ID of the model to use. You can use the [List models](/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](/docs/models/overview) for descriptions of them.
* @type {string}
* @memberof CreateEditRequest
*/
'model': string;
/**
* The input text to use as a starting point for the edit.
* @type {string}
* @memberof CreateEditRequest
*/
'input'?: string | null;
/**
* The instruction that tells the model how to edit the prompt.
* @type {string}
* @memberof CreateEditRequest
*/
'instruction': string;
/**
* How many edits to generate for the input and instruction.
* @type {number}
* @memberof CreateEditRequest
*/
'n'?: number | null;
/**
* What [sampling temperature](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277) to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend altering this or `top_p` but not both.
* @type {number}
* @memberof CreateEditRequest
*/
'temperature'?: number | null;
/**
* An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or `temperature` but not both.
* @type {number}
* @memberof CreateEditRequest
*/
'top_p'?: number | null;
}
/**
*
* @export
* @interface CreateEditResponse
*/
export interface CreateEditResponse {
/**
*
* @type {string}
* @memberof CreateEditResponse
*/
'id': string;
/**
*
* @type {string}
* @memberof CreateEditResponse
*/
'object': string;
/**
*
* @type {number}
* @memberof CreateEditResponse
*/
'created': number;
/**
*
* @type {string}
* @memberof CreateEditResponse
*/
'model': string;
/**
*
* @type {Array<CreateCompletionResponseChoicesInner>}
* @memberof CreateEditResponse
*/
'choices': Array<CreateCompletionResponseChoicesInner>;
/**
*
* @type {CreateCompletionResponseUsage}
* @memberof CreateEditResponse
*/
'usage': CreateCompletionResponseUsage;
}
/**
*
* @export
* @interface CreateEmbeddingRequest
*/
export interface CreateEmbeddingRequest {
/**
* ID of the model to use. You can use the [List models](/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](/docs/models/overview) for descriptions of them.
* @type {string}
* @memberof CreateEmbeddingRequest
*/
'model': string;
/**
*
* @type {CreateEmbeddingRequestInput}
* @memberof CreateEmbeddingRequest
*/
'input': CreateEmbeddingRequestInput;
/**
* A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse.
* @type {string}
* @memberof CreateEmbeddingRequest
*/
'user'?: string;
}
/**
* @type CreateEmbeddingRequestInput
* Input text to get embeddings for, encoded as a string or array of tokens. To get embeddings for multiple inputs in a single request, pass an array of strings or array of token arrays. Each input must not exceed 2048 tokens in length. Unless you are embedding code, we suggest replacing newlines (`\\n`) in your input with a single space, as we have observed inferior results when newlines are present.
* @export
*/
export type CreateEmbeddingRequestInput = Array<any> | Array<number> | Array<string> | string;
/**
*
* @export
* @interface CreateEmbeddingResponse
*/
export interface CreateEmbeddingResponse {
/**
*
* @type {string}
* @memberof CreateEmbeddingResponse
*/
'object': string;
/**
*
* @type {string}
* @memberof CreateEmbeddingResponse
*/
'model': string;
/**
*
* @type {Array<CreateEmbeddingResponseDataInner>}
* @memberof CreateEmbeddingResponse
*/
'data': Array<CreateEmbeddingResponseDataInner>;
/**
*
* @type {CreateEmbeddingResponseUsage}
* @memberof CreateEmbeddingResponse
*/
'usage': CreateEmbeddingResponseUsage;
}
/**
*
* @export
* @interface CreateEmbeddingResponseDataInner
*/
export interface CreateEmbeddingResponseDataInner {
/**
*
* @type {number}
* @memberof CreateEmbeddingResponseDataInner
*/
'index': number;
/**
*
* @type {string}
* @memberof CreateEmbeddingResponseDataInner
*/
'object': string;
/**
*
* @type {Array<number>}
* @memberof CreateEmbeddingResponseDataInner
*/
'embedding': Array<number>;
}
/**
*
* @export
* @interface CreateEmbeddingResponseUsage
*/
export interface CreateEmbeddingResponseUsage {
/**
*
* @type {number}
* @memberof CreateEmbeddingResponseUsage
*/
'prompt_tokens': number;
/**
*
* @type {number}
* @memberof CreateEmbeddingResponseUsage
*/
'total_tokens': number;
}
/**
*
* @export
* @interface CreateFineTuneRequest
*/
export interface CreateFineTuneRequest {
/**
* The ID of an uploaded file that contains training data. See [upload file](/docs/api-reference/files/upload) for how to upload a file. Your dataset must be formatted as a JSONL file, where each training example is a JSON object with the keys \"prompt\" and \"completion\". Additionally, you must upload your file with the purpose `fine-tune`. See the [fine-tuning guide](/docs/guides/fine-tuning/creating-training-data) for more details.
* @type {string}
* @memberof CreateFineTuneRequest
*/
'training_file': string;
/**
* The ID of an uploaded file that contains validation data. If you provide this file, the data is used to generate validation metrics periodically during fine-tuning. These metrics can be viewed in the [fine-tuning results file](/docs/guides/fine-tuning/analyzing-your-fine-tuned-model). Your train and validation data should be mutually exclusive. Your dataset must be formatted as a JSONL file, where each validation example is a JSON object with the keys \"prompt\" and \"completion\". Additionally, you must upload your file with the purpose `fine-tune`. See the [fine-tuning guide](/docs/guides/fine-tuning/creating-training-data) for more details.
* @type {string}
* @memberof CreateFineTuneRequest
*/
'validation_file'?: string | null;
/**
* The name of the base model to fine-tune. You can select one of \"ada\", \"babbage\", \"curie\", \"davinci\", or a fine-tuned model created after 2022-04-21. To learn more about these models, see the [Models](https://beta.openai.com/docs/models) documentation.
* @type {string}
* @memberof CreateFineTuneRequest
*/
'model'?: string | null;
/**
* The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
* @type {number}
* @memberof CreateFineTuneRequest
*/
'n_epochs'?: number | null;
/**
* The batch size to use for training. The batch size is the number of training examples used to train a single forward and backward pass. By default, the batch size will be dynamically configured to be ~0.2% of the number of examples in the training set, capped at 256 - in general, we\'ve found that larger batch sizes tend to work better for larger datasets.
* @type {number}
* @memberof CreateFineTuneRequest
*/
'batch_size'?: number | null;
/**
* The learning rate multiplier to use for training. The fine-tuning learning rate is the original learning rate used for pretraining multiplied by this value. By default, the learning rate multiplier is the 0.05, 0.1, or 0.2 depending on final `batch_size` (larger learning rates tend to perform better with larger batch sizes). We recommend experimenting with values in the range 0.02 to 0.2 to see what produces the best results.
* @type {number}
* @memberof CreateFineTuneRequest
*/
'learning_rate_multiplier'?: number | null;
/**
* The weight to use for loss on the prompt tokens. This controls how much the model tries to learn to generate the prompt (as compared to the completion which always has a weight of 1.0), and can add a stabilizing effect to training when completions are short. If prompts are extremely long (relative to completions), it may make sense to reduce this weight so as to avoid over-prioritizing learning the prompt.
* @type {number}
* @memberof CreateFineTuneRequest
*/
'prompt_loss_weight'?: number | null;
/**
* If set, we calculate classification-specific metrics such as accuracy and F-1 score using the validation set at the end of every epoch. These metrics can be viewed in the [results file](/docs/guides/fine-tuning/analyzing-your-fine-tuned-model). In order to compute classification metrics, you must provide a `validation_file`. Additionally, you must specify `classification_n_classes` for multiclass classification or `classification_positive_class` for binary classification.
* @type {boolean}
* @memberof CreateFineTuneRequest
*/
'compute_classification_metrics'?: boolean | null;
/**
* The number of classes in a classification task. This parameter is required for multiclass classification.
* @type {number}
* @memberof CreateFineTuneRequest
*/
'classification_n_classes'?: number | null;
/**
* The positive class in binary classification. This parameter is needed to generate precision, recall, and F1 metrics when doing binary classification.
* @type {string}
* @memberof CreateFineTuneRequest
*/
'classification_positive_class'?: string | null;
/**
* If this is provided, we calculate F-beta scores at the specified beta values. The F-beta score is a generalization of F-1 score. This is only used for binary classification. With a beta of 1 (i.e. the F-1 score), precision and recall are given the same weight. A larger beta score puts more weight on recall and less on precision. A smaller beta score puts more weight on precision and less on recall.
* @type {Array<number>}
* @memberof CreateFineTuneRequest
*/
'classification_betas'?: Array<number> | null;
/**
* A string of up to 40 characters that will be added to your fine-tuned model name. For example, a `suffix` of \"custom-model-name\" would produce a model name like `ada:ft-your-org:custom-model-name-2022-02-15-04-21-04`.
* @type {string}
* @memberof CreateFineTuneRequest
*/
'suffix'?: string | null;
}
/**
*
* @export
* @interface CreateModerationRequest
*/
export interface CreateModerationRequest {
/**
*
* @type {CreateModerationRequestInput}
* @memberof CreateModerationRequest
*/
'input': CreateModerationRequestInput;
/**
* Two content moderations models are available: `text-moderation-stable` and `text-moderation-latest`. The default is `text-moderation-latest` which will be automatically upgraded over time. This ensures you are always using our most accurate model. If you use `text-moderation-stable`, we will provide advanced notice before updating the model. Accuracy of `text-moderation-stable` may be slightly lower than for `text-moderation-latest`.
* @type {string}
* @memberof CreateModerationRequest
*/
'model'?: string;
}
/**
* @type CreateModerationRequestInput
* The input text to classify
* @export
*/
export type CreateModerationRequestInput = Array<string> | string;
/**
*
* @export
* @interface CreateModerationResponse
*/
export interface CreateModerationResponse {
/**
*
* @type {string}
* @memberof CreateModerationResponse
*/
'id': string;
/**
*
* @type {string}
* @memberof CreateModerationResponse
*/
'model': string;
/**
*
* @type {Array<CreateModerationResponseResultsInner>}
* @memberof CreateModerationResponse
*/
'results': Array<CreateModerationResponseResultsInner>;
}
/**
*
* @export
* @interface CreateModerationResponseResultsInner
*/
export interface CreateModerationResponseResultsInner {
/**
*
* @type {boolean}
* @memberof CreateModerationResponseResultsInner
*/
'flagged': boolean;
/**
*
* @type {CreateModerationResponseResultsInnerCategories}
* @memberof CreateModerationResponseResultsInner
*/
'categories': CreateModerationResponseResultsInnerCategories;
/**
*
* @type {CreateModerationResponseResultsInnerCategoryScores}
* @memberof CreateModerationResponseResultsInner
*/
'category_scores': CreateModerationResponseResultsInnerCategoryScores;
}
/**
*
* @export
* @interface CreateModerationResponseResultsInnerCategories
*/
export interface CreateModerationResponseResultsInnerCategories {
/**
*
* @type {boolean}
* @memberof CreateModerationResponseResultsInnerCategories
*/
'hate': boolean;
/**
*
* @type {boolean}
* @memberof CreateModerationResponseResultsInnerCategories
*/
'hate/threatening': boolean;
/**
*
* @type {boolean}
* @memberof CreateModerationResponseResultsInnerCategories
*/
'self-harm': boolean;
/**
*
* @type {boolean}
* @memberof CreateModerationResponseResultsInnerCategories
*/
'sexual': boolean;
/**
*
* @type {boolean}
* @memberof CreateModerationResponseResultsInnerCategories
*/
'sexual/minors': boolean;
/**
*
* @type {boolean}