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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.
//!
//! Source: OpenAI documentation
////////////////////////////////////////////////////////////////////////////////
use std::collections::HashMap;
use crate::openai::{
endpoint::{
endpoint_filter, request_endpoint, request_endpoint_stream, Endpoint, EndpointVariant,
},
types::{
common::Error,
completion::{Chunk, CompletionResponse},
model::Model,
},
};
use log::{debug, warn};
use serde::{Deserialize, Serialize};
use serde_with::serde_as;
/// Given a prompt, the model will return one or more predicted completions,
/// and can also return the probabilities of alternative tokens at each
/// position.
#[serde_as]
#[derive(Serialize, Deserialize, Debug)]
pub struct Completion {
pub model: Model,
#[serde(skip_serializing_if = "Option::is_none")]
pub prompt: Option<Vec<String>>,
#[serde(skip_serializing_if = "Option::is_none")]
pub stream: Option<bool>,
#[serde(skip_serializing_if = "Option::is_none")]
pub suffix: Option<String>,
#[serde(skip_serializing_if = "Option::is_none")]
pub temperature: Option<f32>,
#[serde(skip_serializing_if = "Option::is_none")]
pub top_p: Option<f32>,
#[serde(skip_serializing_if = "Option::is_none")]
pub n: Option<u32>,
#[serde(skip_serializing_if = "Option::is_none")]
pub logprobs: Option<u32>,
#[serde(skip_serializing_if = "Option::is_none")]
pub echo: Option<Vec<bool>>,
#[serde(skip_serializing_if = "Option::is_none")]
pub stop: Option<Vec<String>>,
#[serde(skip_serializing_if = "Option::is_none")]
pub max_tokens: Option<u32>,
#[serde(skip_serializing_if = "Option::is_none")]
pub presence_penalty: Option<f32>,
#[serde(skip_serializing_if = "Option::is_none")]
pub frequency_penalty: Option<f32>,
#[serde_as(as = "Option<Vec<(_,_)>>")]
#[serde(skip_serializing_if = "Option::is_none")]
pub best_of: Option<HashMap<String, u32>>,
#[serde_as(as = "Option<Vec<(_,_)>>")]
#[serde(skip_serializing_if = "Option::is_none")]
pub logit_bias: Option<HashMap<String, f32>>,
#[serde(skip_serializing_if = "Option::is_none")]
pub user: Option<String>,
}
impl Default for Completion {
fn default() -> Self {
Self {
model: Model::TEXT_DAVINCI_003,
prompt: None,
stream: Some(false),
temperature: None,
top_p: None,
n: None,
stop: None,
max_tokens: None,
presence_penalty: None,
frequency_penalty: None,
logit_bias: None,
user: None,
suffix: None,
logprobs: None,
echo: None,
best_of: None,
}
}
}
impl Completion {
/// ID of the model to use. You can use the [List models API](https://platform.openai.com/docs/api-reference/models/list) to see all of
/// your available models, or see our [Model overview](https://platform.openai.com/docs/models/overview) for descriptions of
/// them.
pub fn model(self, model: Model) -> Self {
Self { model, ..self }
}
/// Add message to prompt.
/// 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.
pub fn prompt(self, content: &str) -> Self {
let mut prompt = vec![];
if let Some(prmp) = self.prompt {
prompt.extend(prmp);
}
prompt.push(String::from(content));
Self {
prompt: Some(prompt),
..self
}
}
/// The suffix that comes after a completion of inserted text.
pub fn suffix(self, suffix: String) -> Self {
Self {
suffix: Some(suffix),
..self
}
}
/// 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.
pub fn temperature(self, temperature: f32) -> Self {
Self {
temperature: Some(temperature),
..self
}
}
/// 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.
pub fn top_p(self, top_p: f32) -> Self {
Self {
top_p: Some(top_p),
..self
}
}
/// 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`.
pub fn n(self, n: u32) -> Self {
Self { n: Some(n), ..self }
}
/// 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.
pub fn logprobs(self, logprobs: u32) -> Self {
Self {
logprobs: Some(logprobs),
..self
}
}
/// Echo back the prompt in addition to the completion
pub fn echo(self, echo: Vec<bool>) -> Self {
Self {
echo: Some(echo),
..self
}
}
/// Up to 4 sequences where the API will stop generating further tokens.
/// The returned text will not contain the stop sequence.
pub fn stop(self, stop: Vec<String>) -> Self {
Self {
stop: Some(stop),
..self
}
}
/// The maximum number of [tokens](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).
pub fn max_tokens(self, max_tokens: u32) -> Self {
Self {
max_tokens: Some(max_tokens),
..self
}
}
/// 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)
pub fn presence_penalty(self, presence_penalty: f32) -> Self {
Self {
presence_penalty: Some(presence_penalty),
..self
}
}
/// 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)
pub fn frequency_penalty(self, frequency_penalty: f32) -> Self {
Self {
frequency_penalty: Some(frequency_penalty),
..self
}
}
/// 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`.
pub fn best_of(self, best_of: HashMap<String, u32>) -> Self {
Self {
best_of: Some(best_of),
..self
}
}
/// 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://platform.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.
pub fn logit_bias(self, logit_bias: HashMap<String, f32>) -> Self {
Self {
logit_bias: Some(logit_bias),
..self
}
}
/// 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).
pub fn user(self, user: &str) -> Self {
Self {
user: Some(user.into()),
..self
}
}
/// Send completion request to OpenAI using streamed method.
///
/// 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.
pub async fn stream_completion<F>(
self,
mut cb: Option<F>,
) -> Result<Vec<Chunk>, Box<dyn std::error::Error>>
where
F: FnMut(Chunk),
{
let data = Self {
stream: Some(true),
..self
};
if !endpoint_filter(&data.model, &Endpoint::Completion_v1) {
return Err("Model not compatible with this endpoint".into());
}
let mut ret_val: Vec<Chunk> = vec![];
request_endpoint_stream(&data, &Endpoint::Completion_v1, EndpointVariant::None,|res| {
if let Ok(chunk_data_raw) = res {
chunk_data_raw.split("\n").for_each(|chunk_data| {
let chunk_data = chunk_data.trim().to_string();
if &chunk_data == "data: [DONE]" {
debug!(target: "openai", "Last chunk received.");
return;
}
if chunk_data.starts_with("data: ") {
// Strip response content:
let stripped_chunk = &chunk_data.trim()[6..];
if let Ok(message_chunk) = serde_json::from_str::<Chunk>(stripped_chunk) {
ret_val.push(message_chunk.clone());
if let Some(cb) = &mut cb {
cb(message_chunk);
}
} else {
if let Ok(response_error) = serde_json::from_str::<Error>(&stripped_chunk) {
warn!(target: "openai",
"OpenAI error code {}: `{:?}`",
response_error.error.code.unwrap_or(0),
stripped_chunk
);
} else {
warn!(target: "openai", "Completion response not deserializable.");
}
}
}
});
}
})
.await?;
Ok(ret_val)
}
/// Send completion request to OpenAI.
pub async fn completion(self) -> Result<CompletionResponse, Box<dyn std::error::Error>> {
let data = Self {
stream: None,
..self
};
if !endpoint_filter(&data.model, &Endpoint::Completion_v1) {
return Err("Model not compatible with this endpoint".into());
}
let mut completion_response: Option<CompletionResponse> = None;
request_endpoint(&data, &Endpoint::Completion_v1, EndpointVariant::None, |res| {
if let Ok(text) = res {
if let Ok(response_data) = serde_json::from_str::<CompletionResponse>(&text) {
debug!(target: "openai", "Response parsed, completion response deserialized.");
completion_response = Some(response_data);
} else {
if let Ok(response_error) = serde_json::from_str::<Error>(&text) {
warn!(target: "openai",
"OpenAI error code {}: `{:?}`",
response_error.error.code.unwrap_or(0),
text
);
} else {
warn!(target: "openai", "Completion response not deserializable.");
}
}
}
})
.await?;
if let Some(response_data) = completion_response {
Ok(response_data)
} else {
Err("No response or error parsing response".into())
}
}
}