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use crate::shared::response_wrapper::OpenAIError;
use crate::shared::types::Stop;
use derive_builder::Builder;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
#[derive(Debug, Clone, Serialize)]
#[serde(untagged)]
pub enum Prompt {
String(String),
ArrayOfString(Vec<String>),
ArrayOfTokens(Vec<u16>),
ArrayOfTokenArrays(Vec<Vec<u16>>),
}
#[derive(Builder, Clone, Debug, Default, Serialize)]
#[builder(name = "CreateCompletionRequestBuilder")]
#[builder(pattern = "mutable")]
#[builder(setter(into, strip_option), default)]
#[builder(derive(Debug))]
#[builder(build_fn(error = "OpenAIError"))]
pub struct CreateCompletionRequest {
/// ID of the model to use.
/// You can use the [List models](https://platform.openai.com/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](https://platform.openai.com/docs/models/overview) for descriptions of them.
pub model: String,
/// 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(skip_serializing_if = "Option::is_none")]
pub prompt: Option<Prompt>, // default: <|endoftext|>
/// The suffix that comes after a completion of inserted text.
#[serde(skip_serializing_if = "Option::is_none")]
pub suffix: Option<String>, // default: null
/// The maximum number of [token](https://platform.openai.com/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).
#[serde(skip_serializing_if = "Option::is_none")]
pub max_tokens: Option<u16>, // default: 16
/// 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(skip_serializing_if = "Option::is_none")]
pub temperature: Option<f32>, // min: 0, max: 2, default: 1
/// 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(skip_serializing_if = "Option::is_none")]
pub top_p: Option<f32>, // default: 1
/// 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(skip_serializing_if = "Option::is_none")]
pub n: Option<u8>, // default: 1
/// 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.
///
/// For streamed progress, use [`create_with_stream`](Completions::create_with_stream).
#[serde(skip_serializing_if = "Option::is_none")]
pub stream: Option<bool>, // default: false
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
pub logprobs: Option<u8>, // default: null
/// Echo back the prompt in addition to the completion.
#[serde(skip_serializing_if = "Option::is_none")]
pub echo: Option<bool>, // default: false
/// Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
#[serde(skip_serializing_if = "Option::is_none")]
pub stop: Option<Stop>, // default: null
/// 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.](https://platform.openai.com/docs/api-reference/parameter-details)
#[serde(skip_serializing_if = "Option::is_none")]
pub presence_penalty: Option<f32>, // min: -2.0, max: 2.0, default: 0
/// 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.](https://platform.openai.com/docs/api-reference/parameter-details)
#[serde(skip_serializing_if = "Option::is_none")]
pub frequency_penalty: Option<f32>, // min: -2.0, max: 2.0, default: 0
/// 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(skip_serializing_if = "Option::is_none")]
pub best_of: Option<f32>, // default: 1
/// 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](https://platform.openai.com/tokenizer?view=bpe) tool (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.
#[serde(skip_serializing_if = "Option::is_none")]
pub logit_bias: Option<HashMap<String, serde_json::Value>>, // default: null
/// A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
#[serde(skip_serializing_if = "Option::is_none")]
pub user: Option<String>,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct CompletionResponse {
pub id: String,
pub object: String,
pub created: u32,
pub model: String,
pub choices: Vec<CompletionChoice>,
pub usage: Usage,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct CompletionChoice {
pub text: String,
pub index: u32,
pub logprobs: Option<u8>,
pub finish_reason: String,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct Usage {
pub prompt_tokens: u32,
pub completion_tokens: u32,
pub total_tokens: u32,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct CompletionChoiceStream {
pub text: String,
pub index: usize,
pub logprobs: Option<u8>,
pub finish_reason: Option<String>,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct CompletionStreamResponse {
pub id: String,
pub object: String,
pub created: u32,
pub model: String,
pub choices: Vec<CompletionChoiceStream>,
}