[chatgpt](../readme.md) / [Exports](../modules.md) / openai

# Namespace: openai

## Table of contents

### Type Aliases

- [CompletionParams](openai.md#completionparams)
- [CompletionResponse](openai.md#completionresponse)
- [CompletionResponseChoices](openai.md#completionresponsechoices)
- [CompletionResponseUsage](openai.md#completionresponseusage)

## Type Aliases

### CompletionParams

Ƭ **CompletionParams**: `Object`

#### Type declaration

| Name | Type | Description |
| :------ | :------ | :------ |
| `best_of?` | `number` | 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`. |
| `echo?` | `boolean` | Echo back the prompt in addition to the completion |
| `frequency_penalty?` | `number` | 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) |
| `logit_bias?` | `Record`<`string`, `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. |
| `logprobs?` | `number` | 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. |
| `max_tokens?` | `number` | The maximum number of tokens 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). |
| `model` | `string` | ID of the model to use. |
| `presence_penalty?` | `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) |
| `prompt` | `string` | The string prompt to generate a completion for. |
| `stop?` | `string`[] | Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence. |
| `suffix?` | `string` | The suffix that comes after a completion of inserted text. |
| `temperature?` | `number` | 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. |
| `top_p?` | `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. |
| `user?` | `string` | A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse. [Learn more](/docs/usage-policies/end-user-ids). |

#### Defined in

[src/types.ts:35](https://github.com/transitive-bullshit/chatgpt-api/blob/531e180/src/types.ts#L35)

___

### CompletionResponse

Ƭ **CompletionResponse**: `Object`

#### Type declaration

| Name | Type |
| :------ | :------ |
| `choices` | [`CompletionResponseChoices`](openai.md#completionresponsechoices) |
| `created` | `number` |
| `id` | `string` |
| `model` | `string` |
| `object` | `string` |
| `usage?` | [`CompletionResponseUsage`](openai.md#completionresponseusage) |

#### Defined in

[src/types.ts:117](https://github.com/transitive-bullshit/chatgpt-api/blob/531e180/src/types.ts#L117)

___

### CompletionResponseChoices

Ƭ **CompletionResponseChoices**: { `finish_reason?`: `string` ; `index?`: `number` ; `logprobs?`: { `text_offset?`: `number`[] ; `token_logprobs?`: `number`[] ; `tokens?`: `string`[] ; `top_logprobs?`: `object`[]  } \| ``null`` ; `text?`: `string`  }[]

#### Defined in

[src/types.ts:126](https://github.com/transitive-bullshit/chatgpt-api/blob/531e180/src/types.ts#L126)

___

### CompletionResponseUsage

Ƭ **CompletionResponseUsage**: `Object`

#### Type declaration

| Name | Type |
| :------ | :------ |
| `completion_tokens` | `number` |
| `prompt_tokens` | `number` |
| `total_tokens` | `number` |

#### Defined in

[src/types.ts:138](https://github.com/transitive-bullshit/chatgpt-api/blob/531e180/src/types.ts#L138)