/*
* OpenAI API
*
* The OpenAI REST API. Please see pub https://platform.openai.com/docs/api-reference for more details.
*
* OpenAPI spec pub version: 2.3.0
*
* Generated pub by: https://github.com/swagger-api/swagger-codegen.git
*/
#[allow(unused_imports)]
use serde_json::Value;
#[derive(Debug, Serialize, Deserialize)]
pub struct 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`.
#[serde(rename = "best_of")]
pub best_of: Option<i32>,
/// Echo back the prompt in addition to the completion
#[serde(rename = "echo")]
pub echo: Option<bool>,
/// 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/text-generation)
#[serde(rename = "frequency_penalty")]
pub frequency_penalty: Option<f32>,
/// 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) 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.
#[serde(rename = "logit_bias")]
pub logit_bias: Option<::std::collections::HashMap<String, i32>>,
/// Include the log probabilities on the `logprobs` most likely output 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.
#[serde(rename = "logprobs")]
pub logprobs: Option<i32>,
/// The maximum number of [tokens](/tokenizer) that can be generated 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.
#[serde(rename = "max_tokens")]
pub max_tokens: Option<i32>,
/// 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) for descriptions of them.
#[serde(rename = "model")]
pub model: Value,
/// 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`.
#[serde(rename = "n")]
pub n: Option<i32>,
/// 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/text-generation)
#[serde(rename = "presence_penalty")]
pub presence_penalty: Option<f32>,
/// 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.
#[serde(rename = "prompt")]
pub prompt: Value,
/// If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same `seed` and parameters should return the same result. Determinism is not guaranteed, and you should refer to the `system_fingerprint` response parameter to monitor changes in the backend.
#[serde(rename = "seed")]
pub seed: Option<i64>,
#[serde(rename = "stop")]
pub stop: Option<crate::models::StopConfiguration>,
/// 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).
#[serde(rename = "stream")]
pub stream: Option<bool>,
#[serde(rename = "stream_options")]
pub stream_options: Option<crate::models::ChatCompletionStreamOptions>,
/// The suffix that comes after a completion of inserted text. This parameter is only supported for `gpt-3.5-turbo-instruct`.
#[serde(rename = "suffix")]
pub suffix: Option<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.
#[serde(rename = "temperature")]
pub temperature: Option<f32>,
/// 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.
#[serde(rename = "top_p")]
pub top_p: Option<f32>,
/// 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).
#[serde(rename = "user")]
pub user: Option<String>,
}