llmatic/src/types/create-completion.ts

Use self-hosted LLMs with an OpenAI compatible API

export type CreateCompletionRequest = {
  /**
   * 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`.
   */
  best_of?: number;
  /**
   * Echo back the prompt in addition to the completion
   */
  echo?: boolean;
  /**
   * 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)
   */
  frequency_penalty?: number;
  /**
   * 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.
   */
  logit_bias?: { [key: string]: any };
  /**
   * 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.
   */
  logprobs?: number;
  /**
   * 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).
   */
  max_tokens?: number;
  /**
   * 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.
   */
  model: string;
  /**
   * 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`.
   */
  n?: number;
  /**
   * 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)
   */
  presence_penalty?: number;
  /**
   * 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.
   */
  prompt?: Array<number[] | number | string> | string;
  /**
   * Up to 4 sequences where the API will stop generating further tokens. The returned text
   * will not contain the stop sequence.
   */
  stop?: string[] | string;
  /**
   * 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.
   */
  stream?: boolean;
  /**
   * The suffix that comes after a completion of inserted text.
   */
  suffix?: string;
  /**
   * 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.
   */
  temperature?: number;
  /**
   * 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.
   */
  top_p?: number;
  /**
   * 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).
   */
  user?: string;
  [property: string]: any;
};

export type CreateCompletionOkResponse = {
  choices: Choice[];
  created: number;
  id: string;
  model: string;
  object: string;
  usage?: Usage;
  [property: string]: any;
};

export type Choice = {
  finish_reason?: string;
  index?: number;
  logprobs?: Logprobs;
  text?: string;
  [property: string]: any;
};

export type Logprobs = {
  text_offset?: number[];
  token_logprobs?: number[];
  tokens?: string[];
  top_logprobs?: { [key: string]: any }[];
  [property: string]: any;
};

export type Usage = {
  completion_tokens: number;
  prompt_tokens: number;
  total_tokens: number;
  [property: string]: any;
};