/*
* OpenAI API
*
* The OpenAI REST API. Please see https://platform.openai.com/docs/api-reference for more details.
*
* The version of the OpenAPI document: 2.3.0
*
* Generated by: https://openapi-generator.tech
*/
use crate::models;
use serde::{Deserialize, Serialize};
#[derive(Clone, Default, Debug, PartialEq, Serialize, Deserialize, bon::Builder)]
pub struct 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) for descriptions of them.
#[serde(rename = "model")]
pub model: String,
#[serde(rename = "prompt")]
pub prompt: String,
/// 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", skip_serializing_if = "Option::is_none")]
pub best_of: Option<i32>,
/// Echo back the prompt in addition to the completion
#[serde(rename = "echo", skip_serializing_if = "Option::is_none")]
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", skip_serializing_if = "Option::is_none")]
pub frequency_penalty: Option<f64>,
/// 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", skip_serializing_if = "Option::is_none")]
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", skip_serializing_if = "Option::is_none")]
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", skip_serializing_if = "Option::is_none")]
pub max_tokens: Option<i32>,
/// 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", skip_serializing_if = "Option::is_none")]
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", skip_serializing_if = "Option::is_none")]
pub presence_penalty: Option<f64>,
/// 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", skip_serializing_if = "Option::is_none")]
pub seed: Option<i64>,
#[serde(rename = "stop", skip_serializing_if = "Option::is_none")]
pub stop: Option<Box<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", skip_serializing_if = "Option::is_none")]
pub stream: Option<bool>,
#[serde(
rename = "stream_options",
default,
with = "::serde_with::rust::double_option",
skip_serializing_if = "Option::is_none"
)]
pub stream_options: Option<Option<Box<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", skip_serializing_if = "Option::is_none")]
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", skip_serializing_if = "Option::is_none")]
pub temperature: Option<f64>,
/// 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", skip_serializing_if = "Option::is_none")]
pub top_p: Option<f64>,
/// 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", skip_serializing_if = "Option::is_none")]
pub user: Option<String>,
}
impl CreateCompletionRequest {
pub fn new(model: String, prompt: String) -> CreateCompletionRequest {
CreateCompletionRequest {
model,
prompt,
best_of: None,
echo: None,
frequency_penalty: None,
logit_bias: None,
logprobs: None,
max_tokens: None,
n: None,
presence_penalty: None,
seed: None,
stop: None,
stream: None,
stream_options: None,
suffix: None,
temperature: None,
top_p: None,
user: None,
}
}
}
impl std::fmt::Display for CreateCompletionRequest {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
match serde_json::to_string(self) {
Ok(s) => write!(f, "{}", s),
Err(_) => Err(std::fmt::Error),
}
}
}