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Basic java objects for the OpenAI GPT APIs
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package com.launchableinc.openai.completion.chat;

import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;

import java.util.List;
import java.util.Map;

@Data
@Builder
@AllArgsConstructor
@NoArgsConstructor
public class ChatCompletionRequest {

	/**
	 * ID of the model to use.
	 */
	String model;

	/**
	 * The messages to generate chat completions for, in the chat format.
 see
	 * {@link ChatMessage}
	 */
	List messages;

	/**
	 * An object specifying the format that the model must output. Compatible with GPT-4 Turbo and all
	 * GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106.
	 * https://platform.openai.com/docs/api-reference/chat/create#chat-create-response_format
	 */
	@JsonProperty("response_format")
	ChatResponseFormat responseFormat;

	/**
	 * 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.
	 */
	Double temperature;

	/**
	 * 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.
	 */
	@JsonProperty("top_p")
	Double topP;

	/**
	 * How many chat completion chatCompletionChoices to generate for each input message.
	 */
	Integer n;

	/**
	 * 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.
	 */
	Boolean stream;

	/**
	 * Up to 4 sequences where the API will stop generating further tokens.
	 */
	List stop;

	/**
	 * The maximum number of tokens allowed for the generated answer. By default, the number of tokens
	 * the model can return will be (4096 - prompt tokens).
	 */
	@JsonProperty("max_tokens")
	Integer maxTokens;

	/**
	 * 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.
	 */
	@JsonProperty("presence_penalty")
	Double presencePenalty;

	/**
	 * 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.
	 */
	@JsonProperty("frequency_penalty")
	Double frequencyPenalty;

	/**
	 * 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.
	 */
	@JsonProperty("logit_bias")
	Map logitBias;


	/**
	 * A unique identifier representing your end-user, which will help OpenAI to monitor and detect
	 * abuse.
	 */
	String user;

	/**
	 * A list of the available functions.
	 */
	List functions;

	/**
	 * Controls how the model responds to function calls, as specified in the OpenAI
	 * documentation.
	 */
	@JsonProperty("function_call")
	ChatCompletionRequestFunctionCall functionCall;

	@Data
	@Builder
	@AllArgsConstructor
	@NoArgsConstructor
	public static class ChatCompletionRequestFunctionCall {

		String name;

		public static ChatCompletionRequestFunctionCall of(String name) {
			return new ChatCompletionRequestFunctionCall(name);
		}

	}
}