329 lines
13 KiB
TypeScript
329 lines
13 KiB
TypeScript
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import { APIResource } from "../core/resource.mjs";
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import * as CompletionsAPI from "./completions.mjs";
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import * as CompletionsCompletionsAPI from "./chat/completions/completions.mjs";
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import { APIPromise } from "../core/api-promise.mjs";
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import { Stream } from "../core/streaming.mjs";
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import { RequestOptions } from "../internal/request-options.mjs";
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export declare class Completions extends APIResource {
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/**
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* Creates a completion for the provided prompt and parameters.
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*
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* @example
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* ```ts
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* const completion = await client.completions.create({
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* model: 'string',
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* prompt: 'This is a test.',
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* });
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* ```
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*/
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create(body: CompletionCreateParamsNonStreaming, options?: RequestOptions): APIPromise<Completion>;
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create(body: CompletionCreateParamsStreaming, options?: RequestOptions): APIPromise<Stream<Completion>>;
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create(body: CompletionCreateParamsBase, options?: RequestOptions): APIPromise<Stream<Completion> | Completion>;
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}
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/**
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* Represents a completion response from the API. Note: both the streamed and
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* non-streamed response objects share the same shape (unlike the chat endpoint).
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*/
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export interface Completion {
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/**
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* A unique identifier for the completion.
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*/
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id: string;
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/**
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* The list of completion choices the model generated for the input prompt.
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*/
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choices: Array<CompletionChoice>;
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/**
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* The Unix timestamp (in seconds) of when the completion was created.
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*/
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created: number;
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/**
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* The model used for completion.
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*/
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model: string;
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/**
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* The object type, which is always "text_completion"
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*/
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object: 'text_completion';
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/**
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* This fingerprint represents the backend configuration that the model runs with.
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*
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* Can be used in conjunction with the `seed` request parameter to understand when
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* backend changes have been made that might impact determinism.
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*/
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system_fingerprint?: string;
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/**
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* Usage statistics for the completion request.
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*/
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usage?: CompletionUsage;
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}
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export interface CompletionChoice {
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/**
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* The reason the model stopped generating tokens. This will be `stop` if the model
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* hit a natural stop point or a provided stop sequence, `length` if the maximum
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* number of tokens specified in the request was reached, or `content_filter` if
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* content was omitted due to a flag from our content filters.
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*/
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finish_reason: 'stop' | 'length' | 'content_filter';
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index: number;
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logprobs: CompletionChoice.Logprobs | null;
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text: string;
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}
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export declare namespace CompletionChoice {
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interface Logprobs {
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text_offset?: Array<number>;
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token_logprobs?: Array<number>;
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tokens?: Array<string>;
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top_logprobs?: Array<{
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[key: string]: number;
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}>;
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}
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}
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/**
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* Usage statistics for the completion request.
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*/
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export interface CompletionUsage {
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/**
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* Number of tokens in the generated completion.
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*/
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completion_tokens: number;
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/**
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* Number of tokens in the prompt.
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*/
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prompt_tokens: number;
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/**
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* Total number of tokens used in the request (prompt + completion).
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*/
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total_tokens: number;
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/**
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* Breakdown of tokens used in a completion.
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*/
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completion_tokens_details?: CompletionUsage.CompletionTokensDetails;
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/**
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* Breakdown of tokens used in the prompt.
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*/
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prompt_tokens_details?: CompletionUsage.PromptTokensDetails;
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}
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export declare namespace CompletionUsage {
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/**
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* Breakdown of tokens used in a completion.
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*/
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interface CompletionTokensDetails {
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/**
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* When using Predicted Outputs, the number of tokens in the prediction that
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* appeared in the completion.
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*/
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accepted_prediction_tokens?: number;
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/**
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* Audio input tokens generated by the model.
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*/
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audio_tokens?: number;
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/**
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* Tokens generated by the model for reasoning.
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*/
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reasoning_tokens?: number;
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/**
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* When using Predicted Outputs, the number of tokens in the prediction that did
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* not appear in the completion. However, like reasoning tokens, these tokens are
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* still counted in the total completion tokens for purposes of billing, output,
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* and context window limits.
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*/
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rejected_prediction_tokens?: number;
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}
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/**
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* Breakdown of tokens used in the prompt.
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*/
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interface PromptTokensDetails {
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/**
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* Audio input tokens present in the prompt.
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*/
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audio_tokens?: number;
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/**
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* Cached tokens present in the prompt.
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*/
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cached_tokens?: number;
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}
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}
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export type CompletionCreateParams = CompletionCreateParamsNonStreaming | CompletionCreateParamsStreaming;
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export interface CompletionCreateParamsBase {
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/**
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* ID of the model to use. You can use the
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* [List models](https://platform.openai.com/docs/api-reference/models/list) API to
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* see all of your available models, or see our
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* [Model overview](https://platform.openai.com/docs/models) for descriptions of
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* them.
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*/
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model: (string & {}) | 'gpt-3.5-turbo-instruct' | 'davinci-002' | 'babbage-002';
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/**
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* The prompt(s) to generate completions for, encoded as a string, array of
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* strings, array of tokens, or array of token arrays.
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*
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* Note that <|endoftext|> is the document separator that the model sees during
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* training, so if a prompt is not specified the model will generate as if from the
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* beginning of a new document.
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*/
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prompt: string | Array<string> | Array<number> | Array<Array<number>> | null;
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/**
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* Generates `best_of` completions server-side and returns the "best" (the one with
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* the highest log probability per token). Results cannot be streamed.
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*
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* When used with `n`, `best_of` controls the number of candidate completions and
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* `n` specifies how many to return – `best_of` must be greater than `n`.
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*
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* **Note:** Because this parameter generates many completions, it can quickly
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* consume your token quota. Use carefully and ensure that you have reasonable
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* settings for `max_tokens` and `stop`.
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*/
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best_of?: number | null;
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/**
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* Echo back the prompt in addition to the completion
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*/
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echo?: boolean | null;
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/**
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* Number between -2.0 and 2.0. Positive values penalize new tokens based on their
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* existing frequency in the text so far, decreasing the model's likelihood to
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* repeat the same line verbatim.
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*
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* [See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation)
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*/
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frequency_penalty?: number | null;
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/**
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* Modify the likelihood of specified tokens appearing in the completion.
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*
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* Accepts a JSON object that maps tokens (specified by their token ID in the GPT
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* tokenizer) to an associated bias value from -100 to 100. You can use this
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* [tokenizer tool](/tokenizer?view=bpe) to convert text to token IDs.
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* Mathematically, the bias is added to the logits generated by the model prior to
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* sampling. The exact effect will vary per model, but values between -1 and 1
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* should decrease or increase likelihood of selection; values like -100 or 100
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* should result in a ban or exclusive selection of the relevant token.
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*
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* As an example, you can pass `{"50256": -100}` to prevent the <|endoftext|> token
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* from being generated.
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*/
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logit_bias?: {
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[key: string]: number;
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} | null;
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/**
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* Include the log probabilities on the `logprobs` most likely output tokens, as
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* well the chosen tokens. For example, if `logprobs` is 5, the API will return a
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* list of the 5 most likely tokens. The API will always return the `logprob` of
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* the sampled token, so there may be up to `logprobs+1` elements in the response.
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*
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* The maximum value for `logprobs` is 5.
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*/
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logprobs?: number | null;
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/**
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* The maximum number of [tokens](/tokenizer) that can be generated in the
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* completion.
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*
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* The token count of your prompt plus `max_tokens` cannot exceed the model's
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* context length.
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* [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
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* for counting tokens.
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*/
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max_tokens?: number | null;
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/**
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* How many completions to generate for each prompt.
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*
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* **Note:** Because this parameter generates many completions, it can quickly
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* consume your token quota. Use carefully and ensure that you have reasonable
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* settings for `max_tokens` and `stop`.
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*/
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n?: number | null;
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/**
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* Number between -2.0 and 2.0. Positive values penalize new tokens based on
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* whether they appear in the text so far, increasing the model's likelihood to
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* talk about new topics.
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*
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* [See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation)
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*/
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presence_penalty?: number | null;
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/**
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* If specified, our system will make a best effort to sample deterministically,
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* such that repeated requests with the same `seed` and parameters should return
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* the same result.
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*
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* Determinism is not guaranteed, and you should refer to the `system_fingerprint`
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* response parameter to monitor changes in the backend.
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*/
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seed?: number | null;
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/**
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* Not supported with latest reasoning models `o3` and `o4-mini`.
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*
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* Up to 4 sequences where the API will stop generating further tokens. The
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* returned text will not contain the stop sequence.
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*/
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stop?: string | null | Array<string>;
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/**
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* Whether to stream back partial progress. If set, tokens will be sent as
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* data-only
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* [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
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* as they become available, with the stream terminated by a `data: [DONE]`
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* message.
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* [Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
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*/
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stream?: boolean | null;
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/**
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* Options for streaming response. Only set this when you set `stream: true`.
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*/
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stream_options?: CompletionsCompletionsAPI.ChatCompletionStreamOptions | null;
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/**
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* The suffix that comes after a completion of inserted text.
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*
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* This parameter is only supported for `gpt-3.5-turbo-instruct`.
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*/
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suffix?: string | null;
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/**
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* What sampling temperature to use, between 0 and 2. Higher values like 0.8 will
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* make the output more random, while lower values like 0.2 will make it more
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* focused and deterministic.
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*
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* We generally recommend altering this or `top_p` but not both.
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*/
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temperature?: number | null;
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/**
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* An alternative to sampling with temperature, called nucleus sampling, where the
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* model considers the results of the tokens with top_p probability mass. So 0.1
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* means only the tokens comprising the top 10% probability mass are considered.
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*
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* We generally recommend altering this or `temperature` but not both.
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*/
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top_p?: number | null;
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/**
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* A unique identifier representing your end-user, which can help OpenAI to monitor
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* and detect abuse.
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* [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#end-user-ids).
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*/
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user?: string;
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}
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export declare namespace CompletionCreateParams {
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type CompletionCreateParamsNonStreaming = CompletionsAPI.CompletionCreateParamsNonStreaming;
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type CompletionCreateParamsStreaming = CompletionsAPI.CompletionCreateParamsStreaming;
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}
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export interface CompletionCreateParamsNonStreaming extends CompletionCreateParamsBase {
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/**
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* Whether to stream back partial progress. If set, tokens will be sent as
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* data-only
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* [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
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* as they become available, with the stream terminated by a `data: [DONE]`
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* message.
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* [Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
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*/
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stream?: false | null;
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}
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export interface CompletionCreateParamsStreaming extends CompletionCreateParamsBase {
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/**
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* Whether to stream back partial progress. If set, tokens will be sent as
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* data-only
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* [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
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* as they become available, with the stream terminated by a `data: [DONE]`
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* message.
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* [Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
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*/
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stream: true;
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}
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export declare namespace Completions {
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export { type Completion as Completion, type CompletionChoice as CompletionChoice, type CompletionUsage as CompletionUsage, type CompletionCreateParams as CompletionCreateParams, type CompletionCreateParamsNonStreaming as CompletionCreateParamsNonStreaming, type CompletionCreateParamsStreaming as CompletionCreateParamsStreaming, };
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}
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//# sourceMappingURL=completions.d.mts.map
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