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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, Serialize, Deserialize, Clone, Default, strum::Display)]
#[serde(rename_all = "lowercase")]
pub enum Role {
#[strum(serialize = "system")]
System,
#[default]
#[strum(serialize = "user")]
User,
#[strum(serialize = "assistant")]
Assistant,
}
#[derive(Builder, Default, Debug, Clone, Deserialize, Serialize)]
#[builder(name = "ChatCompletionMessageRequestBuilder")]
#[builder(pattern = "mutable")]
#[builder(setter(into, strip_option), default)]
#[builder(derive(Debug))]
#[builder(build_fn(error = "OpenAIError"))]
pub struct ChatCompletionMessage {
/// The role of the author of this message. One of `system`, `user`, or `assistant`.
pub role: Role,
/// The contents of the message.
pub content: String,
/// The name of the author of this message. May contain a-z, A-Z, 0-9, and underscores, with a maximum length of 64 characters.
pub name: Option<String>,
}
#[derive(Builder, Clone, Debug, Default, Serialize)]
#[builder(name = "CreateChatRequestBuilder")]
#[builder(pattern = "mutable")]
#[builder(setter(into, strip_option), default)]
#[builder(derive(Debug))]
#[builder(build_fn(error = "OpenAIError"))]
pub struct CreateChatRequest {
/// ID of the model to use.
/// See the [model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility) table for details on which models work with the Chat API.
pub model: String,
/// A list of messages describing the conversation so far.
pub messages: Vec<ChatCompletionMessage>,
/// 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 chat completion choices to generate for each input message.
#[serde(skip_serializing_if = "Option::is_none")]
pub n: Option<u8>, // default: 1
/// If set, partial message deltas will be sent, like in ChatGPT.
/// 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.
/// See the OpenAI Cookbook for [example code](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_stream_completions.ipynb).
///
/// For streamed progress, use [`create_with_stream`](Chat::create_with_stream).
#[serde(skip_serializing_if = "Option::is_none")]
pub stream: Option<bool>, // default: false
/// Up to 4 sequences where the API will stop generating further tokens.
#[serde(skip_serializing_if = "Option::is_none")]
pub stop: Option<Stop>, // default: null
/// The maximum number of tokens to generate in the chat completion.
///
/// The total length of input tokens and generated tokens is limited by the model's context length.
#[serde(skip_serializing_if = "Option::is_none")]
pub max_tokens: Option<u32>,
/// 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
/// Modify the likelihood of specified tokens appearing in the completion.
///
/// Accepts a json object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100.
/// 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.
#[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 Message {
pub role: String,
pub content: String,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct ChatUsage {
pub prompt_tokens: u32,
pub completion_tokens: u32,
pub total_tokens: u32,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct ChatChoice {
pub message: ChatCompletionMessage,
pub finish_reason: String,
pub index: u32,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct ChatResponse {
pub id: String,
pub object: String,
pub created: u32,
pub choices: Vec<ChatChoice>,
pub usage: ChatUsage,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct Delta {
pub content: Option<String>,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct ChatChoiceStream {
pub delta: Delta,
pub finish_reason: Option<String>,
pub index: u32,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct ChatStreamResponse {
pub id: String,
pub object: String,
pub model: String,
pub created: u32,
pub choices: Vec<ChatChoiceStream>,
}