// coverage:ignore-file
// GENERATED CODE - DO NOT MODIFY BY HAND
// ignore_for_file: type=lint
// ignore_for_file: invalid_annotation_target
part of openai_schema;
// ==========================================
// CLASS: CreateCompletionRequest
// ==========================================
/// No Description
@freezed
class CreateCompletionRequest with _$CreateCompletionRequest {
const CreateCompletionRequest._();
/// Factory constructor for CreateCompletionRequest
const factory 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.
@_CompletionModelConverter() required CompletionModel model,
/// 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.
@_CompletionPromptConverter() required CompletionPrompt? prompt,
/// 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`.
@JsonKey(name: 'best_of', includeIfNull: false) @Default(1) int? bestOf,
/// Echo back the prompt in addition to the completion
@JsonKey(includeIfNull: false) @Default(false) bool? echo,
/// 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/guides/gpt/parameter-details)
@JsonKey(name: 'frequency_penalty', includeIfNull: false)
@Default(0.0)
double? frequencyPenalty,
/// 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.
@JsonKey(name: 'logit_bias', includeIfNull: false)
Map<String, int>? logitBias,
/// 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.
@JsonKey(includeIfNull: false) int? logprobs,
/// 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. [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens.
@JsonKey(name: 'max_tokens', includeIfNull: false)
@Default(16)
int? maxTokens,
/// 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`.
@JsonKey(includeIfNull: false) @Default(1) int? n,
/// 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/guides/gpt/parameter-details)
@JsonKey(name: 'presence_penalty', includeIfNull: false)
@Default(0.0)
double? presencePenalty,
/// Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
@_CompletionStopConverter()
@JsonKey(includeIfNull: false)
CompletionStop? stop,
/// 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. [Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
@JsonKey(includeIfNull: false) @Default(false) bool? stream,
/// The suffix that comes after a completion of inserted text.
@JsonKey(includeIfNull: false) String? suffix,
/// What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
///
/// We generally recommend altering this or `top_p` but not both.
@JsonKey(includeIfNull: false) @Default(1.0) double? temperature,
/// 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.
@JsonKey(name: 'top_p', includeIfNull: false) @Default(1.0) double? topP,
/// A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](/docs/guides/safety-best-practices/end-user-ids).
@JsonKey(includeIfNull: false) String? user,
}) = _CreateCompletionRequest;
/// Object construction from a JSON representation
factory CreateCompletionRequest.fromJson(Map<String, dynamic> json) =>
_$CreateCompletionRequestFromJson(json);
/// List of all property names of schema
static const List<String> propertyNames = [
'model',
'prompt',
'best_of',
'echo',
'frequency_penalty',
'logit_bias',
'logprobs',
'max_tokens',
'n',
'presence_penalty',
'stop',
'stream',
'suffix',
'temperature',
'top_p',
'user'
];
/// Validation constants
static const bestOfDefaultValue = 1;
static const bestOfMinValue = 0;
static const bestOfMaxValue = 20;
static const frequencyPenaltyDefaultValue = 0.0;
static const frequencyPenaltyMinValue = -2.0;
static const frequencyPenaltyMaxValue = 2.0;
static const logprobsMinValue = 0;
static const logprobsMaxValue = 5;
static const maxTokensDefaultValue = 16;
static const maxTokensMinValue = 0;
static const nDefaultValue = 1;
static const nMinValue = 1;
static const nMaxValue = 128;
static const presencePenaltyDefaultValue = 0.0;
static const presencePenaltyMinValue = -2.0;
static const presencePenaltyMaxValue = 2.0;
static const temperatureDefaultValue = 1.0;
static const temperatureMinValue = 0.0;
static const temperatureMaxValue = 2.0;
static const topPDefaultValue = 1.0;
static const topPMinValue = 0.0;
static const topPMaxValue = 1.0;
/// Perform validations on the schema property values
String? validateSchema() {
if (bestOf != null && bestOf! < bestOfMinValue) {
return "The value of 'bestOf' cannot be < $bestOfMinValue";
}
if (bestOf != null && bestOf! > bestOfMaxValue) {
return "The value of 'bestOf' cannot be > $bestOfMaxValue";
}
if (frequencyPenalty != null &&
frequencyPenalty! < frequencyPenaltyMinValue) {
return "The value of 'frequencyPenalty' cannot be < $frequencyPenaltyMinValue";
}
if (frequencyPenalty != null &&
frequencyPenalty! > frequencyPenaltyMaxValue) {
return "The value of 'frequencyPenalty' cannot be > $frequencyPenaltyMaxValue";
}
if (logprobs != null && logprobs! < logprobsMinValue) {
return "The value of 'logprobs' cannot be < $logprobsMinValue";
}
if (logprobs != null && logprobs! > logprobsMaxValue) {
return "The value of 'logprobs' cannot be > $logprobsMaxValue";
}
if (maxTokens != null && maxTokens! < maxTokensMinValue) {
return "The value of 'maxTokens' cannot be < $maxTokensMinValue";
}
if (n != null && n! < nMinValue) {
return "The value of 'n' cannot be < $nMinValue";
}
if (n != null && n! > nMaxValue) {
return "The value of 'n' cannot be > $nMaxValue";
}
if (presencePenalty != null && presencePenalty! < presencePenaltyMinValue) {
return "The value of 'presencePenalty' cannot be < $presencePenaltyMinValue";
}
if (presencePenalty != null && presencePenalty! > presencePenaltyMaxValue) {
return "The value of 'presencePenalty' cannot be > $presencePenaltyMaxValue";
}
if (temperature != null && temperature! < temperatureMinValue) {
return "The value of 'temperature' cannot be < $temperatureMinValue";
}
if (temperature != null && temperature! > temperatureMaxValue) {
return "The value of 'temperature' cannot be > $temperatureMaxValue";
}
if (topP != null && topP! < topPMinValue) {
return "The value of 'topP' cannot be < $topPMinValue";
}
if (topP != null && topP! > topPMaxValue) {
return "The value of 'topP' cannot be > $topPMaxValue";
}
return null;
}
/// Map representation of object (not serialized)
Map<String, dynamic> toMap() {
return {
'model': model,
'prompt': prompt,
'best_of': bestOf,
'echo': echo,
'frequency_penalty': frequencyPenalty,
'logit_bias': logitBias,
'logprobs': logprobs,
'max_tokens': maxTokens,
'n': n,
'presence_penalty': presencePenalty,
'stop': stop,
'stream': stream,
'suffix': suffix,
'temperature': temperature,
'top_p': topP,
'user': user,
};
}
}
// ==========================================
// ENUM: CompletionModelCatalog
// ==========================================
/// No Description
enum CompletionModelCatalog {
@JsonValue('babbage-002')
babbage002,
@JsonValue('davinci-002')
davinci002,
@JsonValue('gpt-3.5-turbo-instruct')
gpt35TurboInstruct,
@JsonValue('text-davinci-003')
textDavinci003,
@JsonValue('text-davinci-002')
textDavinci002,
@JsonValue('text-davinci-001')
textDavinci001,
@JsonValue('code-davinci-002')
codeDavinci002,
@JsonValue('text-curie-001')
textCurie001,
@JsonValue('text-babbage-001')
textBabbage001,
@JsonValue('text-ada-001')
textAda001,
}
// ==========================================
// CLASS: CompletionModel
// ==========================================
/// ID of the model to use. You can use the [List models](/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](/docs/models/overview) for descriptions of them.
@freezed
sealed class CompletionModel with _$CompletionModel {
const CompletionModel._();
const factory CompletionModel.string(
String value,
) = _UnionCompletionModelString;
const factory CompletionModel.enumeration(
CompletionModelCatalog value,
) = _UnionCompletionModelEnum;
/// Object construction from a JSON representation
factory CompletionModel.fromJson(Map<String, dynamic> json) =>
_$CompletionModelFromJson(json);
}
/// Custom JSON converter for [CompletionModel]
class _CompletionModelConverter
implements JsonConverter<CompletionModel, Object?> {
const _CompletionModelConverter();
@override
CompletionModel fromJson(Object? data) {
if (data is String &&
_$CompletionModelCatalogEnumMap.values.contains(data)) {
return CompletionModel.enumeration(
_$CompletionModelCatalogEnumMap.keys.elementAt(
_$CompletionModelCatalogEnumMap.values.toList().indexOf(data),
),
);
}
if (data is String) {
return CompletionModel.string(data);
}
throw Exception('Unexpected value for CompletionModel: $data');
}
@override
Object? toJson(CompletionModel data) {
return switch (data) {
_UnionCompletionModelString(value: final v) => v,
_UnionCompletionModelEnum(value: final v) =>
_$CompletionModelCatalogEnumMap[v]!,
};
}
}
// ==========================================
// CLASS: CompletionPrompt
// ==========================================
/// 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.
@freezed
sealed class CompletionPrompt with _$CompletionPrompt {
const CompletionPrompt._();
const factory CompletionPrompt.string(
String value,
) = _UnionCompletionPromptString;
const factory CompletionPrompt.arrayString(
List<String> value,
) = _UnionCompletionPromptArrayString;
const factory CompletionPrompt.arrayInteger(
List<int> value,
) = _UnionCompletionPromptArrayInteger;
const factory CompletionPrompt.array(
List<List<int>> value,
) = _UnionCompletionPromptArray;
/// Object construction from a JSON representation
factory CompletionPrompt.fromJson(Map<String, dynamic> json) =>
_$CompletionPromptFromJson(json);
}
/// Custom JSON converter for [CompletionPrompt]
class _CompletionPromptConverter
implements JsonConverter<CompletionPrompt?, Object?> {
const _CompletionPromptConverter();
@override
CompletionPrompt? fromJson(Object? data) {
if (data == null) {
return null;
}
if (data is String) {
return CompletionPrompt.string(data);
}
if (data is List<String>) {
return CompletionPrompt.arrayString(data);
}
if (data is List<int>) {
return CompletionPrompt.arrayInteger(data);
}
if (data is List<List<int>>) {
return CompletionPrompt.array(data);
}
return CompletionPrompt.string('<|endoftext|>');
}
@override
Object? toJson(CompletionPrompt? data) {
return switch (data) {
_UnionCompletionPromptString(value: final v) => v,
_UnionCompletionPromptArrayString(value: final v) => v,
_UnionCompletionPromptArrayInteger(value: final v) => v,
_UnionCompletionPromptArray(value: final v) => v,
null => null,
};
}
}
// ==========================================
// CLASS: CompletionStop
// ==========================================
/// Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
@freezed
sealed class CompletionStop with _$CompletionStop {
const CompletionStop._();
const factory CompletionStop.string(
String value,
) = _UnionCompletionStopString;
const factory CompletionStop.arrayString(
List<String> value,
) = _UnionCompletionStopArrayString;
/// Object construction from a JSON representation
factory CompletionStop.fromJson(Map<String, dynamic> json) =>
_$CompletionStopFromJson(json);
}
/// Custom JSON converter for [CompletionStop]
class _CompletionStopConverter
implements JsonConverter<CompletionStop?, Object?> {
const _CompletionStopConverter();
@override
CompletionStop? fromJson(Object? data) {
if (data == null) {
return null;
}
if (data is String) {
return CompletionStop.string(data);
}
if (data is List<String>) {
return CompletionStop.arrayString(data);
}
throw Exception('Unexpected value for CompletionStop: $data');
}
@override
Object? toJson(CompletionStop? data) {
return switch (data) {
_UnionCompletionStopString(value: final v) => v,
_UnionCompletionStopArrayString(value: final v) => v,
null => null,
};
}
}
YAML
Having said that, for the
CreateCompletionRequestexample we have been using, a few small mods to the spectitleproperty of the parameters:Dart