Pub chat body

Author: Guantong

src/apis/chat.rs

@@ -18,54 +18,54 @@ use super::{
 pub struct ChatBody {
 	/// ID of the model to use.
 	/// See the model endpoint compatibility table for details on which models work with the Chat API.
-	model: String,
+	pub model: String,
 	/// The messages to generate chat completions for, in the chat format.
-	messages: Vec<Message>,
+	pub messages: Vec<Message>,
 	/// 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.
 	/// Defaults to 1
 	#[serde(skip_serializing_if = "Option::is_none")]
-	temperature: Option<f32>,
+	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.
 	/// Defaults to 1
 	#[serde(skip_serializing_if = "Option::is_none")]
-	top_p: Option<f32>,
+	pub top_p: Option<f32>,
 	/// How many chat completion choices to generate for each input message.
 	/// Defaults to 1
 	#[serde(skip_serializing_if = "Option::is_none")]
-	n: Option<i32>,
+	pub n: Option<i32>,
 	/// If set, partial message deltas will be sent, like in ChatGPT.
 	/// Tokens will be sent as data-only server-sent events as they become available,
 	/// with the stream terminated by a data: [DONE] message. See the OpenAI Cookbook for example code.
 	/// Defaults to false
 	#[serde(skip_serializing_if = "Option::is_none")]
-	stream: Option<bool>,
+	pub stream: Option<bool>,
 	/// Up to 4 sequences where the API will stop generating further tokens.
 	/// Defaults to null
 	#[serde(skip_serializing_if = "Option::is_none")]
-	stop: Option<Vec<String>>,
+	pub stop: Option<Vec<String>>,
 	/// 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.
 	/// Defaults to inf
 	#[serde(skip_serializing_if = "Option::is_none")]
-	max_tokens: Option<i32>,
+	pub max_tokens: 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.
 	/// Defaults to 0
 	#[serde(skip_serializing_if = "Option::is_none")]
-	presence_penalty: Option<f32>,
+	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.
 	/// Defaults to 0
 	#[serde(skip_serializing_if = "Option::is_none")]
-	frequency_penalty: Option<f32>,
+	pub frequency_penalty: Option<f32>,
 	/// 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,
@@ -75,11 +75,11 @@ pub struct ChatBody {
 	/// values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
 	/// Defaults to null
 	#[serde(skip_serializing_if = "Option::is_none")]
-	logit_bias: Option<HashMap<String, String>>,
+	pub logit_bias: Option<HashMap<String, String>>,
 	/// A unique identifier representing your end-user,
 	/// which can help OpenAI to monitor and detect abuse. Learn more.
 	#[serde(skip_serializing_if = "Option::is_none")]
-	user: Option<String>,
+	pub user: Option<String>,
 }
 
 pub trait ChatApi {