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//! Given a prompt, the model will return one or more predicted completions,
//! and can also return the probabilities of alternative tokens at each position.
use super::{openai_post, ApiResponseOrError, Credentials, Usage};
use derive_builder::Builder;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
#[derive(Deserialize, Clone)]
pub struct Completion {
pub id: String,
pub created: u32,
pub model: String,
pub choices: Vec<CompletionChoice>,
pub usage: Usage,
}
#[derive(Deserialize, Clone)]
pub struct CompletionChoice {
pub text: String,
pub index: u16,
pub logprobs: Option<u16>,
pub finish_reason: String,
}
#[derive(Serialize, Builder, Debug, Clone)]
#[builder(pattern = "owned")]
#[builder(name = "CompletionBuilder")]
#[builder(setter(strip_option, into))]
pub struct CompletionRequest {
/// ID of the model to use.
/// You can use the [List models](https://beta.openai.com/docs/api-reference/models/list)
/// API to see all of your available models,
/// or see our [Model overview](https://beta.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")]
#[builder(default)]
pub prompt: Option<String>,
/// The suffix that comes after a completion of inserted text.
#[serde(skip_serializing_if = "Option::is_none")]
#[builder(default)]
pub suffix: Option<String>,
/// The maximum number of [tokens](https://beta.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")]
#[builder(setter(into = false), default)]
pub max_tokens: Option<u16>,
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
#[builder(default)]
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(skip_serializing_if = "Option::is_none")]
#[builder(default)]
pub top_p: Option<f32>,
/// 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")]
#[builder(default)]
pub n: Option<u16>,
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
#[builder(setter(skip), default)] // skipped until properly implemented
pub stream: Option<bool>,
/// 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 and describe your use case.
#[serde(skip_serializing_if = "Option::is_none")]
#[builder(default)]
pub logprobs: Option<u8>,
/// Echo back the prompt in addition to the completion
#[serde(skip_serializing_if = "Option::is_none")]
#[builder(default)]
pub echo: Option<bool>,
/// 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 = "Vec::is_empty")]
#[builder(default)]
pub stop: Vec<String>,
/// 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://beta.openai.com/docs/api-reference/parameter-details).
#[serde(skip_serializing_if = "Option::is_none")]
#[builder(default)]
pub presence_penalty: Option<f32>,
/// 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://beta.openai.com/docs/api-reference/parameter-details).
#[serde(skip_serializing_if = "Option::is_none")]
#[builder(default)]
pub frequency_penalty: Option<f32>,
/// 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")]
#[builder(default)]
pub best_of: Option<u16>,
/// 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](https://beta.openai.com/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.
#[serde(skip_serializing_if = "HashMap::is_empty")]
#[builder(default)]
pub logit_bias: HashMap<String, i16>,
/// A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
/// [Learn more](https://beta.openai.com/docs/guides/safety-best-practices/end-user-ids).
#[serde(skip_serializing_if = "Option::is_none")]
#[builder(default)]
pub user: Option<String>,
/// The credentials to use for this request.
#[serde(skip_serializing)]
#[builder(default)]
pub credentials: Option<Credentials>,
}
impl Completion {
/// Creates a completion for the provided prompt and parameters
async fn create(request: CompletionRequest) -> ApiResponseOrError<Self> {
let credentials_opt = request.credentials.clone();
openai_post("completions", &request, credentials_opt).await
}
pub fn builder(model: &str) -> CompletionBuilder {
CompletionBuilder::create_empty().model(model)
}
}
impl CompletionBuilder {
pub async fn create(self) -> ApiResponseOrError<Completion> {
Completion::create(self.build().unwrap()).await
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::tests::DEFAULT_LEGACY_MODEL;
use dotenvy::dotenv;
#[tokio::test]
async fn completion() {
dotenv().ok();
let credentials = Credentials::from_env();
let completion = Completion::builder(DEFAULT_LEGACY_MODEL)
.prompt("Say this is a test")
.max_tokens(7)
.temperature(0.0)
.credentials(credentials)
.create()
.await
.unwrap();
assert_eq!(
completion.choices.first().unwrap().text,
"\n\nThis is a test."
);
}
}