// File generated from our OpenAPI spec by Stainless.

package com.openai.models

import com.fasterxml.jackson.annotation.JsonAnyGetter
import com.fasterxml.jackson.annotation.JsonAnySetter
import com.fasterxml.jackson.annotation.JsonCreator
import com.fasterxml.jackson.annotation.JsonProperty
import com.fasterxml.jackson.core.JsonGenerator
import com.fasterxml.jackson.core.ObjectCodec
import com.fasterxml.jackson.databind.JsonNode
import com.fasterxml.jackson.databind.SerializerProvider
import com.fasterxml.jackson.databind.annotation.JsonDeserialize
import com.fasterxml.jackson.databind.annotation.JsonSerialize
import com.fasterxml.jackson.module.kotlin.jacksonTypeRef
import com.openai.core.BaseDeserializer
import com.openai.core.BaseSerializer
import com.openai.core.Enum
import com.openai.core.ExcludeMissing
import com.openai.core.JsonField
import com.openai.core.JsonMissing
import com.openai.core.JsonValue
import com.openai.core.NoAutoDetect
import com.openai.core.getOrThrow
import com.openai.core.http.Headers
import com.openai.core.http.QueryParams
import com.openai.core.immutableEmptyMap
import com.openai.core.toImmutable
import com.openai.errors.OpenAIInvalidDataException
import java.util.Objects
import java.util.Optional

/**
 * Creates a model response for the given chat conversation. Learn more in the
 * [text generation](https://platform.openai.com/docs/guides/text-generation),
 * [vision](https://platform.openai.com/docs/guides/vision), and
 * [audio](https://platform.openai.com/docs/guides/audio) guides.
 *
 * Parameter support can differ depending on the model used to generate the response, particularly
 * for newer reasoning models. Parameters that are only supported for reasoning models are noted
 * below. For the current state of unsupported parameters in reasoning models,
 * [refer to the reasoning guide](https://platform.openai.com/docs/guides/reasoning).
 */
class ChatCompletionCreateParams
constructor(
    private val body: ChatCompletionCreateBody,
    private val additionalHeaders: Headers,
    private val additionalQueryParams: QueryParams,
) {

    /**
     * A list of messages comprising the conversation so far. Depending on the
     * [model](https://platform.openai.com/docs/models) you use, different message types
     * (modalities) are supported, like
     * [text](https://platform.openai.com/docs/guides/text-generation),
     * [images](https://platform.openai.com/docs/guides/vision), and
     * [audio](https://platform.openai.com/docs/guides/audio).
     */
    fun messages(): List<ChatCompletionMessageParam> = body.messages()

    /**
     * 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.
     */
    fun model(): ChatModel = body.model()

    /**
     * Parameters for audio output. Required when audio output is requested with `modalities:
     * ["audio"]`. [Learn more](https://platform.openai.com/docs/guides/audio).
     */
    fun audio(): Optional<ChatCompletionAudioParam> = body.audio()

    /**
     * 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.
     */
    fun frequencyPenalty(): Optional<Double> = body.frequencyPenalty()

    /**
     * Deprecated in favor of `tool_choice`.
     *
     * Controls which (if any) function is called by the model.
     *
     * `none` means the model will not call a function and instead generates a message.
     *
     * `auto` means the model can pick between generating a message or calling a function.
     *
     * Specifying a particular function via `{"name": "my_function"}` forces the model to call that
     * function.
     *
     * `none` is the default when no functions are present. `auto` is the default if functions are
     * present.
     */
    fun functionCall(): Optional<FunctionCall> = body.functionCall()

    /**
     * Deprecated in favor of `tools`.
     *
     * A list of functions the model may generate JSON inputs for.
     */
    fun functions(): Optional<List<Function>> = body.functions()

    /**
     * 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.
     */
    fun logitBias(): Optional<LogitBias> = body.logitBias()

    /**
     * Whether to return log probabilities of the output tokens or not. If true, returns the log
     * probabilities of each output token returned in the `content` of `message`.
     */
    fun logprobs(): Optional<Boolean> = body.logprobs()

    /**
     * An upper bound for the number of tokens that can be generated for a completion, including
     * visible output tokens and
     * [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
     */
    fun maxCompletionTokens(): Optional<Long> = body.maxCompletionTokens()

    /**
     * The maximum number of [tokens](/tokenizer) that can be generated in the chat completion. This
     * value can be used to control [costs](https://openai.com/api/pricing/) for text generated via
     * API.
     *
     * This value is now deprecated in favor of `max_completion_tokens`, and is not compatible with
     * [o1 series models](https://platform.openai.com/docs/guides/reasoning).
     */
    fun maxTokens(): Optional<Long> = body.maxTokens()

    /**
     * Developer-defined tags and values used for filtering completions in the
     * [dashboard](https://platform.openai.com/chat-completions).
     */
    fun metadata(): Optional<Metadata> = body.metadata()

    /**
     * Output types that you would like the model to generate for this request. Most models are
     * capable of generating text, which is the default:
     *
     * `["text"]`
     *
     * The `gpt-4o-audio-preview` model can also be used to
     * [generate audio](https://platform.openai.com/docs/guides/audio). To request that this model
     * generate both text and audio responses, you can use:
     *
     * `["text", "audio"]`
     */
    fun modalities(): Optional<List<ChatCompletionModality>> = body.modalities()

    /**
     * How many chat completion choices to generate for each input message. Note that you will be
     * charged based on the number of generated tokens across all of the choices. Keep `n` as `1` to
     * minimize costs.
     */
    fun n(): Optional<Long> = body.n()

    /**
     * Whether to enable
     * [parallel function calling](https://platform.openai.com/docs/guides/function-calling#configuring-parallel-function-calling)
     * during tool use.
     */
    fun parallelToolCalls(): Optional<Boolean> = body.parallelToolCalls()

    /**
     * Static predicted output content, such as the content of a text file that is being
     * regenerated.
     */
    fun prediction(): Optional<ChatCompletionPredictionContent> = body.prediction()

    /**
     * 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.
     */
    fun presencePenalty(): Optional<Double> = body.presencePenalty()

    /**
     * **o1 models only**
     *
     * Constrains effort on reasoning for
     * [reasoning models](https://platform.openai.com/docs/guides/reasoning). Currently supported
     * values are `low`, `medium`, and `high`. Reducing reasoning effort can result in faster
     * responses and fewer tokens used on reasoning in a response.
     */
    fun reasoningEffort(): Optional<ChatCompletionReasoningEffort> = body.reasoningEffort()

    /**
     * An object specifying the format that the model must output.
     *
     * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured Outputs which
     * ensures the model will match your supplied JSON schema. Learn more in the
     * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
     *
     * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message the model
     * generates is valid JSON.
     *
     * **Important:** when using JSON mode, you **must** also instruct the model to produce JSON
     * yourself via a system or user message. Without this, the model may generate an unending
     * stream of whitespace until the generation reaches the token limit, resulting in a
     * long-running and seemingly "stuck" request. Also note that the message content may be
     * partially cut off if `finish_reason="length"`, which indicates the generation exceeded
     * `max_tokens` or the conversation exceeded the max context length.
     */
    fun responseFormat(): Optional<ResponseFormat> = body.responseFormat()

    /**
     * This feature is in Beta. If specified, our system will make a best effort to sample
     * deterministically, such that repeated requests with the same `seed` and parameters should
     * return the same result. Determinism is not guaranteed, and you should refer to the
     * `system_fingerprint` response parameter to monitor changes in the backend.
     */
    fun seed(): Optional<Long> = body.seed()

    /**
     * Specifies the latency tier to use for processing the request. This parameter is relevant for
     * customers subscribed to the scale tier service:
     * - If set to 'auto', and the Project is Scale tier enabled, the system will utilize scale tier
     *   credits until they are exhausted.
     * - If set to 'auto', and the Project is not Scale tier enabled, the request will be processed
     *   using the default service tier with a lower uptime SLA and no latency guarentee.
     * - If set to 'default', the request will be processed using the default service tier with a
     *   lower uptime SLA and no latency guarentee.
     * - When not set, the default behavior is 'auto'.
     *
     * When this parameter is set, the response body will include the `service_tier` utilized.
     */
    fun serviceTier(): Optional<ServiceTier> = body.serviceTier()

    /** Up to 4 sequences where the API will stop generating further tokens. */
    fun stop(): Optional<Stop> = body.stop()

    /**
     * Whether or not to store the output of this chat completion request for use in our
     * [model distillation](https://platform.openai.com/docs/guides/distillation) or
     * [evals](https://platform.openai.com/docs/guides/evals) products.
     */
    fun store(): Optional<Boolean> = body.store()

    /** Options for streaming response. Only set this when you set `stream: true`. */
    fun streamOptions(): Optional<ChatCompletionStreamOptions> = body.streamOptions()

    /**
     * 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.
     */
    fun temperature(): Optional<Double> = body.temperature()

    /**
     * Controls which (if any) tool is called by the model. `none` means the model will not call any
     * tool and instead generates a message. `auto` means the model can pick between generating a
     * message or calling one or more tools. `required` means the model must call one or more tools.
     * Specifying a particular tool via `{"type": "function", "function": {"name": "my_function"}}`
     * forces the model to call that tool.
     *
     * `none` is the default when no tools are present. `auto` is the default if tools are present.
     */
    fun toolChoice(): Optional<ChatCompletionToolChoiceOption> = body.toolChoice()

    /**
     * A list of tools the model may call. Currently, only functions are supported as a tool. Use
     * this to provide a list of functions the model may generate JSON inputs for. A max of 128
     * functions are supported.
     */
    fun tools(): Optional<List<ChatCompletionTool>> = body.tools()

    /**
     * An integer between 0 and 20 specifying the number of most likely tokens to return at each
     * token position, each with an associated log probability. `logprobs` must be set to `true` if
     * this parameter is used.
     */
    fun topLogprobs(): Optional<Long> = body.topLogprobs()

    /**
     * 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.
     */
    fun topP(): Optional<Double> = body.topP()

    /**
     * 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).
     */
    fun user(): Optional<String> = body.user()

    /**
     * A list of messages comprising the conversation so far. Depending on the
     * [model](https://platform.openai.com/docs/models) you use, different message types
     * (modalities) are supported, like
     * [text](https://platform.openai.com/docs/guides/text-generation),
     * [images](https://platform.openai.com/docs/guides/vision), and
     * [audio](https://platform.openai.com/docs/guides/audio).
     */
    fun _messages(): JsonField<List<ChatCompletionMessageParam>> = body._messages()

    /**
     * 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.
     */
    fun _model(): JsonField<ChatModel> = body._model()

    /**
     * Parameters for audio output. Required when audio output is requested with `modalities:
     * ["audio"]`. [Learn more](https://platform.openai.com/docs/guides/audio).
     */
    fun _audio(): JsonField<ChatCompletionAudioParam> = body._audio()

    /**
     * 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.
     */
    fun _frequencyPenalty(): JsonField<Double> = body._frequencyPenalty()

    /**
     * Deprecated in favor of `tool_choice`.
     *
     * Controls which (if any) function is called by the model.
     *
     * `none` means the model will not call a function and instead generates a message.
     *
     * `auto` means the model can pick between generating a message or calling a function.
     *
     * Specifying a particular function via `{"name": "my_function"}` forces the model to call that
     * function.
     *
     * `none` is the default when no functions are present. `auto` is the default if functions are
     * present.
     */
    fun _functionCall(): JsonField<FunctionCall> = body._functionCall()

    /**
     * Deprecated in favor of `tools`.
     *
     * A list of functions the model may generate JSON inputs for.
     */
    fun _functions(): JsonField<List<Function>> = body._functions()

    /**
     * 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.
     */
    fun _logitBias(): JsonField<LogitBias> = body._logitBias()

    /**
     * Whether to return log probabilities of the output tokens or not. If true, returns the log
     * probabilities of each output token returned in the `content` of `message`.
     */
    fun _logprobs(): JsonField<Boolean> = body._logprobs()

    /**
     * An upper bound for the number of tokens that can be generated for a completion, including
     * visible output tokens and
     * [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
     */
    fun _maxCompletionTokens(): JsonField<Long> = body._maxCompletionTokens()

    /**
     * The maximum number of [tokens](/tokenizer) that can be generated in the chat completion. This
     * value can be used to control [costs](https://openai.com/api/pricing/) for text generated via
     * API.
     *
     * This value is now deprecated in favor of `max_completion_tokens`, and is not compatible with
     * [o1 series models](https://platform.openai.com/docs/guides/reasoning).
     */
    fun _maxTokens(): JsonField<Long> = body._maxTokens()

    /**
     * Developer-defined tags and values used for filtering completions in the
     * [dashboard](https://platform.openai.com/chat-completions).
     */
    fun _metadata(): JsonField<Metadata> = body._metadata()

    /**
     * Output types that you would like the model to generate for this request. Most models are
     * capable of generating text, which is the default:
     *
     * `["text"]`
     *
     * The `gpt-4o-audio-preview` model can also be used to
     * [generate audio](https://platform.openai.com/docs/guides/audio). To request that this model
     * generate both text and audio responses, you can use:
     *
     * `["text", "audio"]`
     */
    fun _modalities(): JsonField<List<ChatCompletionModality>> = body._modalities()

    /**
     * How many chat completion choices to generate for each input message. Note that you will be
     * charged based on the number of generated tokens across all of the choices. Keep `n` as `1` to
     * minimize costs.
     */
    fun _n(): JsonField<Long> = body._n()

    /**
     * Whether to enable
     * [parallel function calling](https://platform.openai.com/docs/guides/function-calling#configuring-parallel-function-calling)
     * during tool use.
     */
    fun _parallelToolCalls(): JsonField<Boolean> = body._parallelToolCalls()

    /**
     * Static predicted output content, such as the content of a text file that is being
     * regenerated.
     */
    fun _prediction(): JsonField<ChatCompletionPredictionContent> = body._prediction()

    /**
     * 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.
     */
    fun _presencePenalty(): JsonField<Double> = body._presencePenalty()

    /**
     * **o1 models only**
     *
     * Constrains effort on reasoning for
     * [reasoning models](https://platform.openai.com/docs/guides/reasoning). Currently supported
     * values are `low`, `medium`, and `high`. Reducing reasoning effort can result in faster
     * responses and fewer tokens used on reasoning in a response.
     */
    fun _reasoningEffort(): JsonField<ChatCompletionReasoningEffort> = body._reasoningEffort()

    /**
     * An object specifying the format that the model must output.
     *
     * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured Outputs which
     * ensures the model will match your supplied JSON schema. Learn more in the
     * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
     *
     * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message the model
     * generates is valid JSON.
     *
     * **Important:** when using JSON mode, you **must** also instruct the model to produce JSON
     * yourself via a system or user message. Without this, the model may generate an unending
     * stream of whitespace until the generation reaches the token limit, resulting in a
     * long-running and seemingly "stuck" request. Also note that the message content may be
     * partially cut off if `finish_reason="length"`, which indicates the generation exceeded
     * `max_tokens` or the conversation exceeded the max context length.
     */
    fun _responseFormat(): JsonField<ResponseFormat> = body._responseFormat()

    /**
     * This feature is in Beta. If specified, our system will make a best effort to sample
     * deterministically, such that repeated requests with the same `seed` and parameters should
     * return the same result. Determinism is not guaranteed, and you should refer to the
     * `system_fingerprint` response parameter to monitor changes in the backend.
     */
    fun _seed(): JsonField<Long> = body._seed()

    /**
     * Specifies the latency tier to use for processing the request. This parameter is relevant for
     * customers subscribed to the scale tier service:
     * - If set to 'auto', and the Project is Scale tier enabled, the system will utilize scale tier
     *   credits until they are exhausted.
     * - If set to 'auto', and the Project is not Scale tier enabled, the request will be processed
     *   using the default service tier with a lower uptime SLA and no latency guarentee.
     * - If set to 'default', the request will be processed using the default service tier with a
     *   lower uptime SLA and no latency guarentee.
     * - When not set, the default behavior is 'auto'.
     *
     * When this parameter is set, the response body will include the `service_tier` utilized.
     */
    fun _serviceTier(): JsonField<ServiceTier> = body._serviceTier()

    /** Up to 4 sequences where the API will stop generating further tokens. */
    fun _stop(): JsonField<Stop> = body._stop()

    /**
     * Whether or not to store the output of this chat completion request for use in our
     * [model distillation](https://platform.openai.com/docs/guides/distillation) or
     * [evals](https://platform.openai.com/docs/guides/evals) products.
     */
    fun _store(): JsonField<Boolean> = body._store()

    /** Options for streaming response. Only set this when you set `stream: true`. */
    fun _streamOptions(): JsonField<ChatCompletionStreamOptions> = body._streamOptions()

    /**
     * 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.
     */
    fun _temperature(): JsonField<Double> = body._temperature()

    /**
     * Controls which (if any) tool is called by the model. `none` means the model will not call any
     * tool and instead generates a message. `auto` means the model can pick between generating a
     * message or calling one or more tools. `required` means the model must call one or more tools.
     * Specifying a particular tool via `{"type": "function", "function": {"name": "my_function"}}`
     * forces the model to call that tool.
     *
     * `none` is the default when no tools are present. `auto` is the default if tools are present.
     */
    fun _toolChoice(): JsonField<ChatCompletionToolChoiceOption> = body._toolChoice()

    /**
     * A list of tools the model may call. Currently, only functions are supported as a tool. Use
     * this to provide a list of functions the model may generate JSON inputs for. A max of 128
     * functions are supported.
     */
    fun _tools(): JsonField<List<ChatCompletionTool>> = body._tools()

    /**
     * An integer between 0 and 20 specifying the number of most likely tokens to return at each
     * token position, each with an associated log probability. `logprobs` must be set to `true` if
     * this parameter is used.
     */
    fun _topLogprobs(): JsonField<Long> = body._topLogprobs()

    /**
     * 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.
     */
    fun _topP(): JsonField<Double> = body._topP()

    /**
     * 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).
     */
    fun _user(): JsonField<String> = body._user()

    fun _additionalBodyProperties(): Map<String, JsonValue> = body._additionalProperties()

    fun _additionalHeaders(): Headers = additionalHeaders

    fun _additionalQueryParams(): QueryParams = additionalQueryParams

    @JvmSynthetic internal fun getBody(): ChatCompletionCreateBody = body

    @JvmSynthetic internal fun getHeaders(): Headers = additionalHeaders

    @JvmSynthetic internal fun getQueryParams(): QueryParams = additionalQueryParams

    @NoAutoDetect
    class ChatCompletionCreateBody
    @JsonCreator
    internal constructor(
        @JsonProperty("messages")
        @ExcludeMissing
        private val messages: JsonField<List<ChatCompletionMessageParam>> = JsonMissing.of(),
        @JsonProperty("model")
        @ExcludeMissing
        private val model: JsonField<ChatModel> = JsonMissing.of(),
        @JsonProperty("audio")
        @ExcludeMissing
        private val audio: JsonField<ChatCompletionAudioParam> = JsonMissing.of(),
        @JsonProperty("frequency_penalty")
        @ExcludeMissing
        private val frequencyPenalty: JsonField<Double> = JsonMissing.of(),
        @JsonProperty("function_call")
        @ExcludeMissing
        private val functionCall: JsonField<FunctionCall> = JsonMissing.of(),
        @JsonProperty("functions")
        @ExcludeMissing
        private val functions: JsonField<List<Function>> = JsonMissing.of(),
        @JsonProperty("logit_bias")
        @ExcludeMissing
        private val logitBias: JsonField<LogitBias> = JsonMissing.of(),
        @JsonProperty("logprobs")
        @ExcludeMissing
        private val logprobs: JsonField<Boolean> = JsonMissing.of(),
        @JsonProperty("max_completion_tokens")
        @ExcludeMissing
        private val maxCompletionTokens: JsonField<Long> = JsonMissing.of(),
        @JsonProperty("max_tokens")
        @ExcludeMissing
        private val maxTokens: JsonField<Long> = JsonMissing.of(),
        @JsonProperty("metadata")
        @ExcludeMissing
        private val metadata: JsonField<Metadata> = JsonMissing.of(),
        @JsonProperty("modalities")
        @ExcludeMissing
        private val modalities: JsonField<List<ChatCompletionModality>> = JsonMissing.of(),
        @JsonProperty("n") @ExcludeMissing private val n: JsonField<Long> = JsonMissing.of(),
        @JsonProperty("parallel_tool_calls")
        @ExcludeMissing
        private val parallelToolCalls: JsonField<Boolean> = JsonMissing.of(),
        @JsonProperty("prediction")
        @ExcludeMissing
        private val prediction: JsonField<ChatCompletionPredictionContent> = JsonMissing.of(),
        @JsonProperty("presence_penalty")
        @ExcludeMissing
        private val presencePenalty: JsonField<Double> = JsonMissing.of(),
        @JsonProperty("reasoning_effort")
        @ExcludeMissing
        private val reasoningEffort: JsonField<ChatCompletionReasoningEffort> = JsonMissing.of(),
        @JsonProperty("response_format")
        @ExcludeMissing
        private val responseFormat: JsonField<ResponseFormat> = JsonMissing.of(),
        @JsonProperty("seed") @ExcludeMissing private val seed: JsonField<Long> = JsonMissing.of(),
        @JsonProperty("service_tier")
        @ExcludeMissing
        private val serviceTier: JsonField<ServiceTier> = JsonMissing.of(),
        @JsonProperty("stop") @ExcludeMissing private val stop: JsonField<Stop> = JsonMissing.of(),
        @JsonProperty("store")
        @ExcludeMissing
        private val store: JsonField<Boolean> = JsonMissing.of(),
        @JsonProperty("stream_options")
        @ExcludeMissing
        private val streamOptions: JsonField<ChatCompletionStreamOptions> = JsonMissing.of(),
        @JsonProperty("temperature")
        @ExcludeMissing
        private val temperature: JsonField<Double> = JsonMissing.of(),
        @JsonProperty("tool_choice")
        @ExcludeMissing
        private val toolChoice: JsonField<ChatCompletionToolChoiceOption> = JsonMissing.of(),
        @JsonProperty("tools")
        @ExcludeMissing
        private val tools: JsonField<List<ChatCompletionTool>> = JsonMissing.of(),
        @JsonProperty("top_logprobs")
        @ExcludeMissing
        private val topLogprobs: JsonField<Long> = JsonMissing.of(),
        @JsonProperty("top_p")
        @ExcludeMissing
        private val topP: JsonField<Double> = JsonMissing.of(),
        @JsonProperty("user")
        @ExcludeMissing
        private val user: JsonField<String> = JsonMissing.of(),
        @JsonAnySetter
        private val additionalProperties: Map<String, JsonValue> = immutableEmptyMap(),
    ) {

        /**
         * A list of messages comprising the conversation so far. Depending on the
         * [model](https://platform.openai.com/docs/models) you use, different message types
         * (modalities) are supported, like
         * [text](https://platform.openai.com/docs/guides/text-generation),
         * [images](https://platform.openai.com/docs/guides/vision), and
         * [audio](https://platform.openai.com/docs/guides/audio).
         */
        fun messages(): List<ChatCompletionMessageParam> = messages.getRequired("messages")

        /**
         * 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.
         */
        fun model(): ChatModel = model.getRequired("model")

        /**
         * Parameters for audio output. Required when audio output is requested with `modalities:
         * ["audio"]`. [Learn more](https://platform.openai.com/docs/guides/audio).
         */
        fun audio(): Optional<ChatCompletionAudioParam> =
            Optional.ofNullable(audio.getNullable("audio"))

        /**
         * 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.
         */
        fun frequencyPenalty(): Optional<Double> =
            Optional.ofNullable(frequencyPenalty.getNullable("frequency_penalty"))

        /**
         * Deprecated in favor of `tool_choice`.
         *
         * Controls which (if any) function is called by the model.
         *
         * `none` means the model will not call a function and instead generates a message.
         *
         * `auto` means the model can pick between generating a message or calling a function.
         *
         * Specifying a particular function via `{"name": "my_function"}` forces the model to call
         * that function.
         *
         * `none` is the default when no functions are present. `auto` is the default if functions
         * are present.
         */
        fun functionCall(): Optional<FunctionCall> =
            Optional.ofNullable(functionCall.getNullable("function_call"))

        /**
         * Deprecated in favor of `tools`.
         *
         * A list of functions the model may generate JSON inputs for.
         */
        fun functions(): Optional<List<Function>> =
            Optional.ofNullable(functions.getNullable("functions"))

        /**
         * 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.
         */
        fun logitBias(): Optional<LogitBias> =
            Optional.ofNullable(logitBias.getNullable("logit_bias"))

        /**
         * Whether to return log probabilities of the output tokens or not. If true, returns the log
         * probabilities of each output token returned in the `content` of `message`.
         */
        fun logprobs(): Optional<Boolean> = Optional.ofNullable(logprobs.getNullable("logprobs"))

        /**
         * An upper bound for the number of tokens that can be generated for a completion, including
         * visible output tokens and
         * [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
         */
        fun maxCompletionTokens(): Optional<Long> =
            Optional.ofNullable(maxCompletionTokens.getNullable("max_completion_tokens"))

        /**
         * The maximum number of [tokens](/tokenizer) that can be generated in the chat completion.
         * This value can be used to control [costs](https://openai.com/api/pricing/) for text
         * generated via API.
         *
         * This value is now deprecated in favor of `max_completion_tokens`, and is not compatible
         * with [o1 series models](https://platform.openai.com/docs/guides/reasoning).
         */
        fun maxTokens(): Optional<Long> = Optional.ofNullable(maxTokens.getNullable("max_tokens"))

        /**
         * Developer-defined tags and values used for filtering completions in the
         * [dashboard](https://platform.openai.com/chat-completions).
         */
        fun metadata(): Optional<Metadata> = Optional.ofNullable(metadata.getNullable("metadata"))

        /**
         * Output types that you would like the model to generate for this request. Most models are
         * capable of generating text, which is the default:
         *
         * `["text"]`
         *
         * The `gpt-4o-audio-preview` model can also be used to
         * [generate audio](https://platform.openai.com/docs/guides/audio). To request that this
         * model generate both text and audio responses, you can use:
         *
         * `["text", "audio"]`
         */
        fun modalities(): Optional<List<ChatCompletionModality>> =
            Optional.ofNullable(modalities.getNullable("modalities"))

        /**
         * How many chat completion choices to generate for each input message. Note that you will
         * be charged based on the number of generated tokens across all of the choices. Keep `n` as
         * `1` to minimize costs.
         */
        fun n(): Optional<Long> = Optional.ofNullable(n.getNullable("n"))

        /**
         * Whether to enable
         * [parallel function calling](https://platform.openai.com/docs/guides/function-calling#configuring-parallel-function-calling)
         * during tool use.
         */
        fun parallelToolCalls(): Optional<Boolean> =
            Optional.ofNullable(parallelToolCalls.getNullable("parallel_tool_calls"))

        /**
         * Static predicted output content, such as the content of a text file that is being
         * regenerated.
         */
        fun prediction(): Optional<ChatCompletionPredictionContent> =
            Optional.ofNullable(prediction.getNullable("prediction"))

        /**
         * 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.
         */
        fun presencePenalty(): Optional<Double> =
            Optional.ofNullable(presencePenalty.getNullable("presence_penalty"))

        /**
         * **o1 models only**
         *
         * Constrains effort on reasoning for
         * [reasoning models](https://platform.openai.com/docs/guides/reasoning). Currently
         * supported values are `low`, `medium`, and `high`. Reducing reasoning effort can result in
         * faster responses and fewer tokens used on reasoning in a response.
         */
        fun reasoningEffort(): Optional<ChatCompletionReasoningEffort> =
            Optional.ofNullable(reasoningEffort.getNullable("reasoning_effort"))

        /**
         * An object specifying the format that the model must output.
         *
         * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured Outputs
         * which ensures the model will match your supplied JSON schema. Learn more in the
         * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
         *
         * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message the
         * model generates is valid JSON.
         *
         * **Important:** when using JSON mode, you **must** also instruct the model to produce JSON
         * yourself via a system or user message. Without this, the model may generate an unending
         * stream of whitespace until the generation reaches the token limit, resulting in a
         * long-running and seemingly "stuck" request. Also note that the message content may be
         * partially cut off if `finish_reason="length"`, which indicates the generation exceeded
         * `max_tokens` or the conversation exceeded the max context length.
         */
        fun responseFormat(): Optional<ResponseFormat> =
            Optional.ofNullable(responseFormat.getNullable("response_format"))

        /**
         * This feature is in Beta. If specified, our system will make a best effort to sample
         * deterministically, such that repeated requests with the same `seed` and parameters should
         * return the same result. Determinism is not guaranteed, and you should refer to the
         * `system_fingerprint` response parameter to monitor changes in the backend.
         */
        fun seed(): Optional<Long> = Optional.ofNullable(seed.getNullable("seed"))

        /**
         * Specifies the latency tier to use for processing the request. This parameter is relevant
         * for customers subscribed to the scale tier service:
         * - If set to 'auto', and the Project is Scale tier enabled, the system will utilize scale
         *   tier credits until they are exhausted.
         * - If set to 'auto', and the Project is not Scale tier enabled, the request will be
         *   processed using the default service tier with a lower uptime SLA and no latency
         *   guarentee.
         * - If set to 'default', the request will be processed using the default service tier with
         *   a lower uptime SLA and no latency guarentee.
         * - When not set, the default behavior is 'auto'.
         *
         * When this parameter is set, the response body will include the `service_tier` utilized.
         */
        fun serviceTier(): Optional<ServiceTier> =
            Optional.ofNullable(serviceTier.getNullable("service_tier"))

        /** Up to 4 sequences where the API will stop generating further tokens. */
        fun stop(): Optional<Stop> = Optional.ofNullable(stop.getNullable("stop"))

        /**
         * Whether or not to store the output of this chat completion request for use in our
         * [model distillation](https://platform.openai.com/docs/guides/distillation) or
         * [evals](https://platform.openai.com/docs/guides/evals) products.
         */
        fun store(): Optional<Boolean> = Optional.ofNullable(store.getNullable("store"))

        /** Options for streaming response. Only set this when you set `stream: true`. */
        fun streamOptions(): Optional<ChatCompletionStreamOptions> =
            Optional.ofNullable(streamOptions.getNullable("stream_options"))

        /**
         * 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.
         */
        fun temperature(): Optional<Double> =
            Optional.ofNullable(temperature.getNullable("temperature"))

        /**
         * Controls which (if any) tool is called by the model. `none` means the model will not call
         * any tool and instead generates a message. `auto` means the model can pick between
         * generating a message or calling one or more tools. `required` means the model must call
         * one or more tools. Specifying a particular tool via `{"type": "function", "function":
         * {"name": "my_function"}}` forces the model to call that tool.
         *
         * `none` is the default when no tools are present. `auto` is the default if tools are
         * present.
         */
        fun toolChoice(): Optional<ChatCompletionToolChoiceOption> =
            Optional.ofNullable(toolChoice.getNullable("tool_choice"))

        /**
         * A list of tools the model may call. Currently, only functions are supported as a tool.
         * Use this to provide a list of functions the model may generate JSON inputs for. A max of
         * 128 functions are supported.
         */
        fun tools(): Optional<List<ChatCompletionTool>> =
            Optional.ofNullable(tools.getNullable("tools"))

        /**
         * An integer between 0 and 20 specifying the number of most likely tokens to return at each
         * token position, each with an associated log probability. `logprobs` must be set to `true`
         * if this parameter is used.
         */
        fun topLogprobs(): Optional<Long> =
            Optional.ofNullable(topLogprobs.getNullable("top_logprobs"))

        /**
         * 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.
         */
        fun topP(): Optional<Double> = Optional.ofNullable(topP.getNullable("top_p"))

        /**
         * 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).
         */
        fun user(): Optional<String> = Optional.ofNullable(user.getNullable("user"))

        /**
         * A list of messages comprising the conversation so far. Depending on the
         * [model](https://platform.openai.com/docs/models) you use, different message types
         * (modalities) are supported, like
         * [text](https://platform.openai.com/docs/guides/text-generation),
         * [images](https://platform.openai.com/docs/guides/vision), and
         * [audio](https://platform.openai.com/docs/guides/audio).
         */
        @JsonProperty("messages")
        @ExcludeMissing
        fun _messages(): JsonField<List<ChatCompletionMessageParam>> = messages

        /**
         * 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.
         */
        @JsonProperty("model") @ExcludeMissing fun _model(): JsonField<ChatModel> = model

        /**
         * Parameters for audio output. Required when audio output is requested with `modalities:
         * ["audio"]`. [Learn more](https://platform.openai.com/docs/guides/audio).
         */
        @JsonProperty("audio")
        @ExcludeMissing
        fun _audio(): JsonField<ChatCompletionAudioParam> = audio

        /**
         * 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")
        @ExcludeMissing
        fun _frequencyPenalty(): JsonField<Double> = frequencyPenalty

        /**
         * Deprecated in favor of `tool_choice`.
         *
         * Controls which (if any) function is called by the model.
         *
         * `none` means the model will not call a function and instead generates a message.
         *
         * `auto` means the model can pick between generating a message or calling a function.
         *
         * Specifying a particular function via `{"name": "my_function"}` forces the model to call
         * that function.
         *
         * `none` is the default when no functions are present. `auto` is the default if functions
         * are present.
         */
        @JsonProperty("function_call")
        @ExcludeMissing
        fun _functionCall(): JsonField<FunctionCall> = functionCall

        /**
         * Deprecated in favor of `tools`.
         *
         * A list of functions the model may generate JSON inputs for.
         */
        @JsonProperty("functions")
        @ExcludeMissing
        fun _functions(): JsonField<List<Function>> = functions

        /**
         * 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.
         */
        @JsonProperty("logit_bias")
        @ExcludeMissing
        fun _logitBias(): JsonField<LogitBias> = logitBias

        /**
         * Whether to return log probabilities of the output tokens or not. If true, returns the log
         * probabilities of each output token returned in the `content` of `message`.
         */
        @JsonProperty("logprobs") @ExcludeMissing fun _logprobs(): JsonField<Boolean> = logprobs

        /**
         * An upper bound for the number of tokens that can be generated for a completion, including
         * visible output tokens and
         * [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
         */
        @JsonProperty("max_completion_tokens")
        @ExcludeMissing
        fun _maxCompletionTokens(): JsonField<Long> = maxCompletionTokens

        /**
         * The maximum number of [tokens](/tokenizer) that can be generated in the chat completion.
         * This value can be used to control [costs](https://openai.com/api/pricing/) for text
         * generated via API.
         *
         * This value is now deprecated in favor of `max_completion_tokens`, and is not compatible
         * with [o1 series models](https://platform.openai.com/docs/guides/reasoning).
         */
        @JsonProperty("max_tokens") @ExcludeMissing fun _maxTokens(): JsonField<Long> = maxTokens

        /**
         * Developer-defined tags and values used for filtering completions in the
         * [dashboard](https://platform.openai.com/chat-completions).
         */
        @JsonProperty("metadata") @ExcludeMissing fun _metadata(): JsonField<Metadata> = metadata

        /**
         * Output types that you would like the model to generate for this request. Most models are
         * capable of generating text, which is the default:
         *
         * `["text"]`
         *
         * The `gpt-4o-audio-preview` model can also be used to
         * [generate audio](https://platform.openai.com/docs/guides/audio). To request that this
         * model generate both text and audio responses, you can use:
         *
         * `["text", "audio"]`
         */
        @JsonProperty("modalities")
        @ExcludeMissing
        fun _modalities(): JsonField<List<ChatCompletionModality>> = modalities

        /**
         * How many chat completion choices to generate for each input message. Note that you will
         * be charged based on the number of generated tokens across all of the choices. Keep `n` as
         * `1` to minimize costs.
         */
        @JsonProperty("n") @ExcludeMissing fun _n(): JsonField<Long> = n

        /**
         * Whether to enable
         * [parallel function calling](https://platform.openai.com/docs/guides/function-calling#configuring-parallel-function-calling)
         * during tool use.
         */
        @JsonProperty("parallel_tool_calls")
        @ExcludeMissing
        fun _parallelToolCalls(): JsonField<Boolean> = parallelToolCalls

        /**
         * Static predicted output content, such as the content of a text file that is being
         * regenerated.
         */
        @JsonProperty("prediction")
        @ExcludeMissing
        fun _prediction(): JsonField<ChatCompletionPredictionContent> = prediction

        /**
         * 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")
        @ExcludeMissing
        fun _presencePenalty(): JsonField<Double> = presencePenalty

        /**
         * **o1 models only**
         *
         * Constrains effort on reasoning for
         * [reasoning models](https://platform.openai.com/docs/guides/reasoning). Currently
         * supported values are `low`, `medium`, and `high`. Reducing reasoning effort can result in
         * faster responses and fewer tokens used on reasoning in a response.
         */
        @JsonProperty("reasoning_effort")
        @ExcludeMissing
        fun _reasoningEffort(): JsonField<ChatCompletionReasoningEffort> = reasoningEffort

        /**
         * An object specifying the format that the model must output.
         *
         * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured Outputs
         * which ensures the model will match your supplied JSON schema. Learn more in the
         * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
         *
         * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message the
         * model generates is valid JSON.
         *
         * **Important:** when using JSON mode, you **must** also instruct the model to produce JSON
         * yourself via a system or user message. Without this, the model may generate an unending
         * stream of whitespace until the generation reaches the token limit, resulting in a
         * long-running and seemingly "stuck" request. Also note that the message content may be
         * partially cut off if `finish_reason="length"`, which indicates the generation exceeded
         * `max_tokens` or the conversation exceeded the max context length.
         */
        @JsonProperty("response_format")
        @ExcludeMissing
        fun _responseFormat(): JsonField<ResponseFormat> = responseFormat

        /**
         * This feature is in Beta. If specified, our system will make a best effort to sample
         * deterministically, such that repeated requests with the same `seed` and parameters should
         * return the same result. Determinism is not guaranteed, and you should refer to the
         * `system_fingerprint` response parameter to monitor changes in the backend.
         */
        @JsonProperty("seed") @ExcludeMissing fun _seed(): JsonField<Long> = seed

        /**
         * Specifies the latency tier to use for processing the request. This parameter is relevant
         * for customers subscribed to the scale tier service:
         * - If set to 'auto', and the Project is Scale tier enabled, the system will utilize scale
         *   tier credits until they are exhausted.
         * - If set to 'auto', and the Project is not Scale tier enabled, the request will be
         *   processed using the default service tier with a lower uptime SLA and no latency
         *   guarentee.
         * - If set to 'default', the request will be processed using the default service tier with
         *   a lower uptime SLA and no latency guarentee.
         * - When not set, the default behavior is 'auto'.
         *
         * When this parameter is set, the response body will include the `service_tier` utilized.
         */
        @JsonProperty("service_tier")
        @ExcludeMissing
        fun _serviceTier(): JsonField<ServiceTier> = serviceTier

        /** Up to 4 sequences where the API will stop generating further tokens. */
        @JsonProperty("stop") @ExcludeMissing fun _stop(): JsonField<Stop> = stop

        /**
         * Whether or not to store the output of this chat completion request for use in our
         * [model distillation](https://platform.openai.com/docs/guides/distillation) or
         * [evals](https://platform.openai.com/docs/guides/evals) products.
         */
        @JsonProperty("store") @ExcludeMissing fun _store(): JsonField<Boolean> = store

        /** Options for streaming response. Only set this when you set `stream: true`. */
        @JsonProperty("stream_options")
        @ExcludeMissing
        fun _streamOptions(): JsonField<ChatCompletionStreamOptions> = streamOptions

        /**
         * 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.
         */
        @JsonProperty("temperature")
        @ExcludeMissing
        fun _temperature(): JsonField<Double> = temperature

        /**
         * Controls which (if any) tool is called by the model. `none` means the model will not call
         * any tool and instead generates a message. `auto` means the model can pick between
         * generating a message or calling one or more tools. `required` means the model must call
         * one or more tools. Specifying a particular tool via `{"type": "function", "function":
         * {"name": "my_function"}}` forces the model to call that tool.
         *
         * `none` is the default when no tools are present. `auto` is the default if tools are
         * present.
         */
        @JsonProperty("tool_choice")
        @ExcludeMissing
        fun _toolChoice(): JsonField<ChatCompletionToolChoiceOption> = toolChoice

        /**
         * A list of tools the model may call. Currently, only functions are supported as a tool.
         * Use this to provide a list of functions the model may generate JSON inputs for. A max of
         * 128 functions are supported.
         */
        @JsonProperty("tools")
        @ExcludeMissing
        fun _tools(): JsonField<List<ChatCompletionTool>> = tools

        /**
         * An integer between 0 and 20 specifying the number of most likely tokens to return at each
         * token position, each with an associated log probability. `logprobs` must be set to `true`
         * if this parameter is used.
         */
        @JsonProperty("top_logprobs")
        @ExcludeMissing
        fun _topLogprobs(): JsonField<Long> = topLogprobs

        /**
         * 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") @ExcludeMissing fun _topP(): JsonField<Double> = topP

        /**
         * 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).
         */
        @JsonProperty("user") @ExcludeMissing fun _user(): JsonField<String> = user

        @JsonAnyGetter
        @ExcludeMissing
        fun _additionalProperties(): Map<String, JsonValue> = additionalProperties

        private var validated: Boolean = false

        fun validate(): ChatCompletionCreateBody = apply {
            if (validated) {
                return@apply
            }

            messages().forEach { it.validate() }
            model()
            audio().ifPresent { it.validate() }
            frequencyPenalty()
            functionCall().ifPresent { it.validate() }
            functions().ifPresent { it.forEach { it.validate() } }
            logitBias().ifPresent { it.validate() }
            logprobs()
            maxCompletionTokens()
            maxTokens()
            metadata().ifPresent { it.validate() }
            modalities()
            n()
            parallelToolCalls()
            prediction().ifPresent { it.validate() }
            presencePenalty()
            reasoningEffort()
            responseFormat().ifPresent { it.validate() }
            seed()
            serviceTier()
            stop().ifPresent { it.validate() }
            store()
            streamOptions().ifPresent { it.validate() }
            temperature()
            toolChoice().ifPresent { it.validate() }
            tools().ifPresent { it.forEach { it.validate() } }
            topLogprobs()
            topP()
            user()
            validated = true
        }

        fun toBuilder() = Builder().from(this)

        companion object {

            @JvmStatic fun builder() = Builder()
        }

        class Builder {

            private var messages: JsonField<MutableList<ChatCompletionMessageParam>>? = null
            private var model: JsonField<ChatModel>? = null
            private var audio: JsonField<ChatCompletionAudioParam> = JsonMissing.of()
            private var frequencyPenalty: JsonField<Double> = JsonMissing.of()
            private var functionCall: JsonField<FunctionCall> = JsonMissing.of()
            private var functions: JsonField<MutableList<Function>>? = null
            private var logitBias: JsonField<LogitBias> = JsonMissing.of()
            private var logprobs: JsonField<Boolean> = JsonMissing.of()
            private var maxCompletionTokens: JsonField<Long> = JsonMissing.of()
            private var maxTokens: JsonField<Long> = JsonMissing.of()
            private var metadata: JsonField<Metadata> = JsonMissing.of()
            private var modalities: JsonField<MutableList<ChatCompletionModality>>? = null
            private var n: JsonField<Long> = JsonMissing.of()
            private var parallelToolCalls: JsonField<Boolean> = JsonMissing.of()
            private var prediction: JsonField<ChatCompletionPredictionContent> = JsonMissing.of()
            private var presencePenalty: JsonField<Double> = JsonMissing.of()
            private var reasoningEffort: JsonField<ChatCompletionReasoningEffort> = JsonMissing.of()
            private var responseFormat: JsonField<ResponseFormat> = JsonMissing.of()
            private var seed: JsonField<Long> = JsonMissing.of()
            private var serviceTier: JsonField<ServiceTier> = JsonMissing.of()
            private var stop: JsonField<Stop> = JsonMissing.of()
            private var store: JsonField<Boolean> = JsonMissing.of()
            private var streamOptions: JsonField<ChatCompletionStreamOptions> = JsonMissing.of()
            private var temperature: JsonField<Double> = JsonMissing.of()
            private var toolChoice: JsonField<ChatCompletionToolChoiceOption> = JsonMissing.of()
            private var tools: JsonField<MutableList<ChatCompletionTool>>? = null
            private var topLogprobs: JsonField<Long> = JsonMissing.of()
            private var topP: JsonField<Double> = JsonMissing.of()
            private var user: JsonField<String> = JsonMissing.of()
            private var additionalProperties: MutableMap<String, JsonValue> = mutableMapOf()

            @JvmSynthetic
            internal fun from(chatCompletionCreateBody: ChatCompletionCreateBody) = apply {
                messages = chatCompletionCreateBody.messages.map { it.toMutableList() }
                model = chatCompletionCreateBody.model
                audio = chatCompletionCreateBody.audio
                frequencyPenalty = chatCompletionCreateBody.frequencyPenalty
                functionCall = chatCompletionCreateBody.functionCall
                functions = chatCompletionCreateBody.functions.map { it.toMutableList() }
                logitBias = chatCompletionCreateBody.logitBias
                logprobs = chatCompletionCreateBody.logprobs
                maxCompletionTokens = chatCompletionCreateBody.maxCompletionTokens
                maxTokens = chatCompletionCreateBody.maxTokens
                metadata = chatCompletionCreateBody.metadata
                modalities = chatCompletionCreateBody.modalities.map { it.toMutableList() }
                n = chatCompletionCreateBody.n
                parallelToolCalls = chatCompletionCreateBody.parallelToolCalls
                prediction = chatCompletionCreateBody.prediction
                presencePenalty = chatCompletionCreateBody.presencePenalty
                reasoningEffort = chatCompletionCreateBody.reasoningEffort
                responseFormat = chatCompletionCreateBody.responseFormat
                seed = chatCompletionCreateBody.seed
                serviceTier = chatCompletionCreateBody.serviceTier
                stop = chatCompletionCreateBody.stop
                store = chatCompletionCreateBody.store
                streamOptions = chatCompletionCreateBody.streamOptions
                temperature = chatCompletionCreateBody.temperature
                toolChoice = chatCompletionCreateBody.toolChoice
                tools = chatCompletionCreateBody.tools.map { it.toMutableList() }
                topLogprobs = chatCompletionCreateBody.topLogprobs
                topP = chatCompletionCreateBody.topP
                user = chatCompletionCreateBody.user
                additionalProperties = chatCompletionCreateBody.additionalProperties.toMutableMap()
            }

            /**
             * A list of messages comprising the conversation so far. Depending on the
             * [model](https://platform.openai.com/docs/models) you use, different message types
             * (modalities) are supported, like
             * [text](https://platform.openai.com/docs/guides/text-generation),
             * [images](https://platform.openai.com/docs/guides/vision), and
             * [audio](https://platform.openai.com/docs/guides/audio).
             */
            fun messages(messages: List<ChatCompletionMessageParam>) =
                messages(JsonField.of(messages))

            /**
             * A list of messages comprising the conversation so far. Depending on the
             * [model](https://platform.openai.com/docs/models) you use, different message types
             * (modalities) are supported, like
             * [text](https://platform.openai.com/docs/guides/text-generation),
             * [images](https://platform.openai.com/docs/guides/vision), and
             * [audio](https://platform.openai.com/docs/guides/audio).
             */
            fun messages(messages: JsonField<List<ChatCompletionMessageParam>>) = apply {
                this.messages = messages.map { it.toMutableList() }
            }

            /**
             * A list of messages comprising the conversation so far. Depending on the
             * [model](https://platform.openai.com/docs/models) you use, different message types
             * (modalities) are supported, like
             * [text](https://platform.openai.com/docs/guides/text-generation),
             * [images](https://platform.openai.com/docs/guides/vision), and
             * [audio](https://platform.openai.com/docs/guides/audio).
             */
            fun addMessage(message: ChatCompletionMessageParam) = apply {
                messages =
                    (messages ?: JsonField.of(mutableListOf())).apply {
                        asKnown()
                            .orElseThrow {
                                IllegalStateException(
                                    "Field was set to non-list type: ${javaClass.simpleName}"
                                )
                            }
                            .add(message)
                    }
            }

            /**
             * Developer-provided instructions that the model should follow, regardless of messages
             * sent by the user. With o1 models and newer, `developer` messages replace the previous
             * `system` messages.
             */
            fun addMessage(
                chatCompletionDeveloperMessageParam: ChatCompletionDeveloperMessageParam
            ) =
                addMessage(
                    ChatCompletionMessageParam.ofChatCompletionDeveloperMessageParam(
                        chatCompletionDeveloperMessageParam
                    )
                )

            /**
             * Developer-provided instructions that the model should follow, regardless of messages
             * sent by the user. With o1 models and newer, use `developer` messages for this purpose
             * instead.
             */
            fun addMessage(chatCompletionSystemMessageParam: ChatCompletionSystemMessageParam) =
                addMessage(
                    ChatCompletionMessageParam.ofChatCompletionSystemMessageParam(
                        chatCompletionSystemMessageParam
                    )
                )

            /**
             * Messages sent by an end user, containing prompts or additional context information.
             */
            fun addMessage(chatCompletionUserMessageParam: ChatCompletionUserMessageParam) =
                addMessage(
                    ChatCompletionMessageParam.ofChatCompletionUserMessageParam(
                        chatCompletionUserMessageParam
                    )
                )

            /** Messages sent by the model in response to user messages. */
            fun addMessage(
                chatCompletionAssistantMessageParam: ChatCompletionAssistantMessageParam
            ) =
                addMessage(
                    ChatCompletionMessageParam.ofChatCompletionAssistantMessageParam(
                        chatCompletionAssistantMessageParam
                    )
                )

            /**
             * A list of messages comprising the conversation so far. Depending on the
             * [model](https://platform.openai.com/docs/models) you use, different message types
             * (modalities) are supported, like
             * [text](https://platform.openai.com/docs/guides/text-generation),
             * [images](https://platform.openai.com/docs/guides/vision), and
             * [audio](https://platform.openai.com/docs/guides/audio).
             */
            fun addMessage(chatCompletionToolMessageParam: ChatCompletionToolMessageParam) =
                addMessage(
                    ChatCompletionMessageParam.ofChatCompletionToolMessageParam(
                        chatCompletionToolMessageParam
                    )
                )

            /**
             * A list of messages comprising the conversation so far. Depending on the
             * [model](https://platform.openai.com/docs/models) you use, different message types
             * (modalities) are supported, like
             * [text](https://platform.openai.com/docs/guides/text-generation),
             * [images](https://platform.openai.com/docs/guides/vision), and
             * [audio](https://platform.openai.com/docs/guides/audio).
             */
            fun addMessage(chatCompletionFunctionMessageParam: ChatCompletionFunctionMessageParam) =
                addMessage(
                    ChatCompletionMessageParam.ofChatCompletionFunctionMessageParam(
                        chatCompletionFunctionMessageParam
                    )
                )

            /**
             * 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.
             */
            fun model(model: ChatModel) = model(JsonField.of(model))

            /**
             * 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.
             */
            fun model(model: JsonField<ChatModel>) = apply { this.model = model }

            /**
             * 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.
             */
            fun model(value: String) = apply { model(ChatModel.of(value)) }

            /**
             * Parameters for audio output. Required when audio output is requested with
             * `modalities: ["audio"]`. [Learn more](https://platform.openai.com/docs/guides/audio).
             */
            fun audio(audio: ChatCompletionAudioParam?) = audio(JsonField.ofNullable(audio))

            /**
             * Parameters for audio output. Required when audio output is requested with
             * `modalities: ["audio"]`. [Learn more](https://platform.openai.com/docs/guides/audio).
             */
            fun audio(audio: Optional<ChatCompletionAudioParam>) = audio(audio.orElse(null))

            /**
             * Parameters for audio output. Required when audio output is requested with
             * `modalities: ["audio"]`. [Learn more](https://platform.openai.com/docs/guides/audio).
             */
            fun audio(audio: JsonField<ChatCompletionAudioParam>) = apply { this.audio = audio }

            /**
             * 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.
             */
            fun frequencyPenalty(frequencyPenalty: Double?) =
                frequencyPenalty(JsonField.ofNullable(frequencyPenalty))

            /**
             * 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.
             */
            fun frequencyPenalty(frequencyPenalty: Double) =
                frequencyPenalty(frequencyPenalty as Double?)

            /**
             * 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.
             */
            @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
            fun frequencyPenalty(frequencyPenalty: Optional<Double>) =
                frequencyPenalty(frequencyPenalty.orElse(null) as Double?)

            /**
             * 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.
             */
            fun frequencyPenalty(frequencyPenalty: JsonField<Double>) = apply {
                this.frequencyPenalty = frequencyPenalty
            }

            /**
             * Deprecated in favor of `tool_choice`.
             *
             * Controls which (if any) function is called by the model.
             *
             * `none` means the model will not call a function and instead generates a message.
             *
             * `auto` means the model can pick between generating a message or calling a function.
             *
             * Specifying a particular function via `{"name": "my_function"}` forces the model to
             * call that function.
             *
             * `none` is the default when no functions are present. `auto` is the default if
             * functions are present.
             */
            fun functionCall(functionCall: FunctionCall) = functionCall(JsonField.of(functionCall))

            /**
             * Deprecated in favor of `tool_choice`.
             *
             * Controls which (if any) function is called by the model.
             *
             * `none` means the model will not call a function and instead generates a message.
             *
             * `auto` means the model can pick between generating a message or calling a function.
             *
             * Specifying a particular function via `{"name": "my_function"}` forces the model to
             * call that function.
             *
             * `none` is the default when no functions are present. `auto` is the default if
             * functions are present.
             */
            fun functionCall(functionCall: JsonField<FunctionCall>) = apply {
                this.functionCall = functionCall
            }

            /**
             * `none` means the model will not call a function and instead generates a message.
             * `auto` means the model can pick between generating a message or calling a function.
             */
            fun functionCall(behavior: FunctionCall.Behavior) =
                functionCall(FunctionCall.ofBehavior(behavior))

            /**
             * Specifying a particular function via `{"name": "my_function"}` forces the model to
             * call that function.
             */
            fun functionCall(functionCallOption: ChatCompletionFunctionCallOption) =
                functionCall(FunctionCall.ofFunctionCallOption(functionCallOption))

            /**
             * Deprecated in favor of `tools`.
             *
             * A list of functions the model may generate JSON inputs for.
             */
            fun functions(functions: List<Function>) = functions(JsonField.of(functions))

            /**
             * Deprecated in favor of `tools`.
             *
             * A list of functions the model may generate JSON inputs for.
             */
            fun functions(functions: JsonField<List<Function>>) = apply {
                this.functions = functions.map { it.toMutableList() }
            }

            /**
             * Deprecated in favor of `tools`.
             *
             * A list of functions the model may generate JSON inputs for.
             */
            fun addFunction(function: Function) = apply {
                functions =
                    (functions ?: JsonField.of(mutableListOf())).apply {
                        asKnown()
                            .orElseThrow {
                                IllegalStateException(
                                    "Field was set to non-list type: ${javaClass.simpleName}"
                                )
                            }
                            .add(function)
                    }
            }

            /**
             * 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.
             */
            fun logitBias(logitBias: LogitBias?) = logitBias(JsonField.ofNullable(logitBias))

            /**
             * 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.
             */
            fun logitBias(logitBias: Optional<LogitBias>) = logitBias(logitBias.orElse(null))

            /**
             * 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.
             */
            fun logitBias(logitBias: JsonField<LogitBias>) = apply { this.logitBias = logitBias }

            /**
             * Whether to return log probabilities of the output tokens or not. If true, returns the
             * log probabilities of each output token returned in the `content` of `message`.
             */
            fun logprobs(logprobs: Boolean?) = logprobs(JsonField.ofNullable(logprobs))

            /**
             * Whether to return log probabilities of the output tokens or not. If true, returns the
             * log probabilities of each output token returned in the `content` of `message`.
             */
            fun logprobs(logprobs: Boolean) = logprobs(logprobs as Boolean?)

            /**
             * Whether to return log probabilities of the output tokens or not. If true, returns the
             * log probabilities of each output token returned in the `content` of `message`.
             */
            @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
            fun logprobs(logprobs: Optional<Boolean>) = logprobs(logprobs.orElse(null) as Boolean?)

            /**
             * Whether to return log probabilities of the output tokens or not. If true, returns the
             * log probabilities of each output token returned in the `content` of `message`.
             */
            fun logprobs(logprobs: JsonField<Boolean>) = apply { this.logprobs = logprobs }

            /**
             * An upper bound for the number of tokens that can be generated for a completion,
             * including visible output tokens and
             * [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
             */
            fun maxCompletionTokens(maxCompletionTokens: Long?) =
                maxCompletionTokens(JsonField.ofNullable(maxCompletionTokens))

            /**
             * An upper bound for the number of tokens that can be generated for a completion,
             * including visible output tokens and
             * [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
             */
            fun maxCompletionTokens(maxCompletionTokens: Long) =
                maxCompletionTokens(maxCompletionTokens as Long?)

            /**
             * An upper bound for the number of tokens that can be generated for a completion,
             * including visible output tokens and
             * [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
             */
            @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
            fun maxCompletionTokens(maxCompletionTokens: Optional<Long>) =
                maxCompletionTokens(maxCompletionTokens.orElse(null) as Long?)

            /**
             * An upper bound for the number of tokens that can be generated for a completion,
             * including visible output tokens and
             * [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
             */
            fun maxCompletionTokens(maxCompletionTokens: JsonField<Long>) = apply {
                this.maxCompletionTokens = maxCompletionTokens
            }

            /**
             * The maximum number of [tokens](/tokenizer) that can be generated in the chat
             * completion. This value can be used to control
             * [costs](https://openai.com/api/pricing/) for text generated via API.
             *
             * This value is now deprecated in favor of `max_completion_tokens`, and is not
             * compatible with
             * [o1 series models](https://platform.openai.com/docs/guides/reasoning).
             */
            fun maxTokens(maxTokens: Long?) = maxTokens(JsonField.ofNullable(maxTokens))

            /**
             * The maximum number of [tokens](/tokenizer) that can be generated in the chat
             * completion. This value can be used to control
             * [costs](https://openai.com/api/pricing/) for text generated via API.
             *
             * This value is now deprecated in favor of `max_completion_tokens`, and is not
             * compatible with
             * [o1 series models](https://platform.openai.com/docs/guides/reasoning).
             */
            fun maxTokens(maxTokens: Long) = maxTokens(maxTokens as Long?)

            /**
             * The maximum number of [tokens](/tokenizer) that can be generated in the chat
             * completion. This value can be used to control
             * [costs](https://openai.com/api/pricing/) for text generated via API.
             *
             * This value is now deprecated in favor of `max_completion_tokens`, and is not
             * compatible with
             * [o1 series models](https://platform.openai.com/docs/guides/reasoning).
             */
            @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
            fun maxTokens(maxTokens: Optional<Long>) = maxTokens(maxTokens.orElse(null) as Long?)

            /**
             * The maximum number of [tokens](/tokenizer) that can be generated in the chat
             * completion. This value can be used to control
             * [costs](https://openai.com/api/pricing/) for text generated via API.
             *
             * This value is now deprecated in favor of `max_completion_tokens`, and is not
             * compatible with
             * [o1 series models](https://platform.openai.com/docs/guides/reasoning).
             */
            fun maxTokens(maxTokens: JsonField<Long>) = apply { this.maxTokens = maxTokens }

            /**
             * Developer-defined tags and values used for filtering completions in the
             * [dashboard](https://platform.openai.com/chat-completions).
             */
            fun metadata(metadata: Metadata?) = metadata(JsonField.ofNullable(metadata))

            /**
             * Developer-defined tags and values used for filtering completions in the
             * [dashboard](https://platform.openai.com/chat-completions).
             */
            fun metadata(metadata: Optional<Metadata>) = metadata(metadata.orElse(null))

            /**
             * Developer-defined tags and values used for filtering completions in the
             * [dashboard](https://platform.openai.com/chat-completions).
             */
            fun metadata(metadata: JsonField<Metadata>) = apply { this.metadata = metadata }

            /**
             * Output types that you would like the model to generate for this request. Most models
             * are capable of generating text, which is the default:
             *
             * `["text"]`
             *
             * The `gpt-4o-audio-preview` model can also be used to
             * [generate audio](https://platform.openai.com/docs/guides/audio). To request that this
             * model generate both text and audio responses, you can use:
             *
             * `["text", "audio"]`
             */
            fun modalities(modalities: List<ChatCompletionModality>?) =
                modalities(JsonField.ofNullable(modalities))

            /**
             * Output types that you would like the model to generate for this request. Most models
             * are capable of generating text, which is the default:
             *
             * `["text"]`
             *
             * The `gpt-4o-audio-preview` model can also be used to
             * [generate audio](https://platform.openai.com/docs/guides/audio). To request that this
             * model generate both text and audio responses, you can use:
             *
             * `["text", "audio"]`
             */
            fun modalities(modalities: Optional<List<ChatCompletionModality>>) =
                modalities(modalities.orElse(null))

            /**
             * Output types that you would like the model to generate for this request. Most models
             * are capable of generating text, which is the default:
             *
             * `["text"]`
             *
             * The `gpt-4o-audio-preview` model can also be used to
             * [generate audio](https://platform.openai.com/docs/guides/audio). To request that this
             * model generate both text and audio responses, you can use:
             *
             * `["text", "audio"]`
             */
            fun modalities(modalities: JsonField<List<ChatCompletionModality>>) = apply {
                this.modalities = modalities.map { it.toMutableList() }
            }

            /**
             * Output types that you would like the model to generate for this request. Most models
             * are capable of generating text, which is the default:
             *
             * `["text"]`
             *
             * The `gpt-4o-audio-preview` model can also be used to
             * [generate audio](https://platform.openai.com/docs/guides/audio). To request that this
             * model generate both text and audio responses, you can use:
             *
             * `["text", "audio"]`
             */
            fun addModality(modality: ChatCompletionModality) = apply {
                modalities =
                    (modalities ?: JsonField.of(mutableListOf())).apply {
                        asKnown()
                            .orElseThrow {
                                IllegalStateException(
                                    "Field was set to non-list type: ${javaClass.simpleName}"
                                )
                            }
                            .add(modality)
                    }
            }

            /**
             * How many chat completion choices to generate for each input message. Note that you
             * will be charged based on the number of generated tokens across all of the choices.
             * Keep `n` as `1` to minimize costs.
             */
            fun n(n: Long?) = n(JsonField.ofNullable(n))

            /**
             * How many chat completion choices to generate for each input message. Note that you
             * will be charged based on the number of generated tokens across all of the choices.
             * Keep `n` as `1` to minimize costs.
             */
            fun n(n: Long) = n(n as Long?)

            /**
             * How many chat completion choices to generate for each input message. Note that you
             * will be charged based on the number of generated tokens across all of the choices.
             * Keep `n` as `1` to minimize costs.
             */
            @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
            fun n(n: Optional<Long>) = n(n.orElse(null) as Long?)

            /**
             * How many chat completion choices to generate for each input message. Note that you
             * will be charged based on the number of generated tokens across all of the choices.
             * Keep `n` as `1` to minimize costs.
             */
            fun n(n: JsonField<Long>) = apply { this.n = n }

            /**
             * Whether to enable
             * [parallel function calling](https://platform.openai.com/docs/guides/function-calling#configuring-parallel-function-calling)
             * during tool use.
             */
            fun parallelToolCalls(parallelToolCalls: Boolean) =
                parallelToolCalls(JsonField.of(parallelToolCalls))

            /**
             * Whether to enable
             * [parallel function calling](https://platform.openai.com/docs/guides/function-calling#configuring-parallel-function-calling)
             * during tool use.
             */
            fun parallelToolCalls(parallelToolCalls: JsonField<Boolean>) = apply {
                this.parallelToolCalls = parallelToolCalls
            }

            /**
             * Static predicted output content, such as the content of a text file that is being
             * regenerated.
             */
            fun prediction(prediction: ChatCompletionPredictionContent?) =
                prediction(JsonField.ofNullable(prediction))

            /**
             * Static predicted output content, such as the content of a text file that is being
             * regenerated.
             */
            fun prediction(prediction: Optional<ChatCompletionPredictionContent>) =
                prediction(prediction.orElse(null))

            /**
             * Static predicted output content, such as the content of a text file that is being
             * regenerated.
             */
            fun prediction(prediction: JsonField<ChatCompletionPredictionContent>) = apply {
                this.prediction = prediction
            }

            /**
             * 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.
             */
            fun presencePenalty(presencePenalty: Double?) =
                presencePenalty(JsonField.ofNullable(presencePenalty))

            /**
             * 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.
             */
            fun presencePenalty(presencePenalty: Double) =
                presencePenalty(presencePenalty as Double?)

            /**
             * 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.
             */
            @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
            fun presencePenalty(presencePenalty: Optional<Double>) =
                presencePenalty(presencePenalty.orElse(null) as Double?)

            /**
             * 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.
             */
            fun presencePenalty(presencePenalty: JsonField<Double>) = apply {
                this.presencePenalty = presencePenalty
            }

            /**
             * **o1 models only**
             *
             * Constrains effort on reasoning for
             * [reasoning models](https://platform.openai.com/docs/guides/reasoning). Currently
             * supported values are `low`, `medium`, and `high`. Reducing reasoning effort can
             * result in faster responses and fewer tokens used on reasoning in a response.
             */
            fun reasoningEffort(reasoningEffort: ChatCompletionReasoningEffort) =
                reasoningEffort(JsonField.of(reasoningEffort))

            /**
             * **o1 models only**
             *
             * Constrains effort on reasoning for
             * [reasoning models](https://platform.openai.com/docs/guides/reasoning). Currently
             * supported values are `low`, `medium`, and `high`. Reducing reasoning effort can
             * result in faster responses and fewer tokens used on reasoning in a response.
             */
            fun reasoningEffort(reasoningEffort: JsonField<ChatCompletionReasoningEffort>) = apply {
                this.reasoningEffort = reasoningEffort
            }

            /**
             * An object specifying the format that the model must output.
             *
             * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured
             * Outputs which ensures the model will match your supplied JSON schema. Learn more in
             * the
             * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
             *
             * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message
             * the model generates is valid JSON.
             *
             * **Important:** when using JSON mode, you **must** also instruct the model to produce
             * JSON yourself via a system or user message. Without this, the model may generate an
             * unending stream of whitespace until the generation reaches the token limit, resulting
             * in a long-running and seemingly "stuck" request. Also note that the message content
             * may be partially cut off if `finish_reason="length"`, which indicates the generation
             * exceeded `max_tokens` or the conversation exceeded the max context length.
             */
            fun responseFormat(responseFormat: ResponseFormat) =
                responseFormat(JsonField.of(responseFormat))

            /**
             * An object specifying the format that the model must output.
             *
             * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured
             * Outputs which ensures the model will match your supplied JSON schema. Learn more in
             * the
             * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
             *
             * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message
             * the model generates is valid JSON.
             *
             * **Important:** when using JSON mode, you **must** also instruct the model to produce
             * JSON yourself via a system or user message. Without this, the model may generate an
             * unending stream of whitespace until the generation reaches the token limit, resulting
             * in a long-running and seemingly "stuck" request. Also note that the message content
             * may be partially cut off if `finish_reason="length"`, which indicates the generation
             * exceeded `max_tokens` or the conversation exceeded the max context length.
             */
            fun responseFormat(responseFormat: JsonField<ResponseFormat>) = apply {
                this.responseFormat = responseFormat
            }

            /**
             * An object specifying the format that the model must output.
             *
             * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured
             * Outputs which ensures the model will match your supplied JSON schema. Learn more in
             * the
             * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
             *
             * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message
             * the model generates is valid JSON.
             *
             * **Important:** when using JSON mode, you **must** also instruct the model to produce
             * JSON yourself via a system or user message. Without this, the model may generate an
             * unending stream of whitespace until the generation reaches the token limit, resulting
             * in a long-running and seemingly "stuck" request. Also note that the message content
             * may be partially cut off if `finish_reason="length"`, which indicates the generation
             * exceeded `max_tokens` or the conversation exceeded the max context length.
             */
            fun responseFormat(responseFormatText: ResponseFormatText) =
                responseFormat(ResponseFormat.ofResponseFormatText(responseFormatText))

            /**
             * An object specifying the format that the model must output.
             *
             * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured
             * Outputs which ensures the model will match your supplied JSON schema. Learn more in
             * the
             * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
             *
             * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message
             * the model generates is valid JSON.
             *
             * **Important:** when using JSON mode, you **must** also instruct the model to produce
             * JSON yourself via a system or user message. Without this, the model may generate an
             * unending stream of whitespace until the generation reaches the token limit, resulting
             * in a long-running and seemingly "stuck" request. Also note that the message content
             * may be partially cut off if `finish_reason="length"`, which indicates the generation
             * exceeded `max_tokens` or the conversation exceeded the max context length.
             */
            fun responseFormat(responseFormatJsonObject: ResponseFormatJsonObject) =
                responseFormat(ResponseFormat.ofResponseFormatJsonObject(responseFormatJsonObject))

            /**
             * An object specifying the format that the model must output.
             *
             * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured
             * Outputs which ensures the model will match your supplied JSON schema. Learn more in
             * the
             * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
             *
             * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message
             * the model generates is valid JSON.
             *
             * **Important:** when using JSON mode, you **must** also instruct the model to produce
             * JSON yourself via a system or user message. Without this, the model may generate an
             * unending stream of whitespace until the generation reaches the token limit, resulting
             * in a long-running and seemingly "stuck" request. Also note that the message content
             * may be partially cut off if `finish_reason="length"`, which indicates the generation
             * exceeded `max_tokens` or the conversation exceeded the max context length.
             */
            fun responseFormat(responseFormatJsonSchema: ResponseFormatJsonSchema) =
                responseFormat(ResponseFormat.ofResponseFormatJsonSchema(responseFormatJsonSchema))

            /**
             * This feature is in Beta. If specified, our system will make a best effort to sample
             * deterministically, such that repeated requests with the same `seed` and parameters
             * should return the same result. Determinism is not guaranteed, and you should refer to
             * the `system_fingerprint` response parameter to monitor changes in the backend.
             */
            fun seed(seed: Long?) = seed(JsonField.ofNullable(seed))

            /**
             * This feature is in Beta. If specified, our system will make a best effort to sample
             * deterministically, such that repeated requests with the same `seed` and parameters
             * should return the same result. Determinism is not guaranteed, and you should refer to
             * the `system_fingerprint` response parameter to monitor changes in the backend.
             */
            fun seed(seed: Long) = seed(seed as Long?)

            /**
             * This feature is in Beta. If specified, our system will make a best effort to sample
             * deterministically, such that repeated requests with the same `seed` and parameters
             * should return the same result. Determinism is not guaranteed, and you should refer to
             * the `system_fingerprint` response parameter to monitor changes in the backend.
             */
            @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
            fun seed(seed: Optional<Long>) = seed(seed.orElse(null) as Long?)

            /**
             * This feature is in Beta. If specified, our system will make a best effort to sample
             * deterministically, such that repeated requests with the same `seed` and parameters
             * should return the same result. Determinism is not guaranteed, and you should refer to
             * the `system_fingerprint` response parameter to monitor changes in the backend.
             */
            fun seed(seed: JsonField<Long>) = apply { this.seed = seed }

            /**
             * Specifies the latency tier to use for processing the request. This parameter is
             * relevant for customers subscribed to the scale tier service:
             * - If set to 'auto', and the Project is Scale tier enabled, the system will utilize
             *   scale tier credits until they are exhausted.
             * - If set to 'auto', and the Project is not Scale tier enabled, the request will be
             *   processed using the default service tier with a lower uptime SLA and no latency
             *   guarentee.
             * - If set to 'default', the request will be processed using the default service tier
             *   with a lower uptime SLA and no latency guarentee.
             * - When not set, the default behavior is 'auto'.
             *
             * When this parameter is set, the response body will include the `service_tier`
             * utilized.
             */
            fun serviceTier(serviceTier: ServiceTier?) =
                serviceTier(JsonField.ofNullable(serviceTier))

            /**
             * Specifies the latency tier to use for processing the request. This parameter is
             * relevant for customers subscribed to the scale tier service:
             * - If set to 'auto', and the Project is Scale tier enabled, the system will utilize
             *   scale tier credits until they are exhausted.
             * - If set to 'auto', and the Project is not Scale tier enabled, the request will be
             *   processed using the default service tier with a lower uptime SLA and no latency
             *   guarentee.
             * - If set to 'default', the request will be processed using the default service tier
             *   with a lower uptime SLA and no latency guarentee.
             * - When not set, the default behavior is 'auto'.
             *
             * When this parameter is set, the response body will include the `service_tier`
             * utilized.
             */
            fun serviceTier(serviceTier: Optional<ServiceTier>) =
                serviceTier(serviceTier.orElse(null))

            /**
             * Specifies the latency tier to use for processing the request. This parameter is
             * relevant for customers subscribed to the scale tier service:
             * - If set to 'auto', and the Project is Scale tier enabled, the system will utilize
             *   scale tier credits until they are exhausted.
             * - If set to 'auto', and the Project is not Scale tier enabled, the request will be
             *   processed using the default service tier with a lower uptime SLA and no latency
             *   guarentee.
             * - If set to 'default', the request will be processed using the default service tier
             *   with a lower uptime SLA and no latency guarentee.
             * - When not set, the default behavior is 'auto'.
             *
             * When this parameter is set, the response body will include the `service_tier`
             * utilized.
             */
            fun serviceTier(serviceTier: JsonField<ServiceTier>) = apply {
                this.serviceTier = serviceTier
            }

            /** Up to 4 sequences where the API will stop generating further tokens. */
            fun stop(stop: Stop) = stop(JsonField.of(stop))

            /** Up to 4 sequences where the API will stop generating further tokens. */
            fun stop(stop: JsonField<Stop>) = apply { this.stop = stop }

            /** Up to 4 sequences where the API will stop generating further tokens. */
            fun stop(string: String) = stop(Stop.ofString(string))

            /** Up to 4 sequences where the API will stop generating further tokens. */
            fun stopOfStrings(strings: List<String>) = stop(Stop.ofStrings(strings))

            /**
             * Whether or not to store the output of this chat completion request for use in our
             * [model distillation](https://platform.openai.com/docs/guides/distillation) or
             * [evals](https://platform.openai.com/docs/guides/evals) products.
             */
            fun store(store: Boolean?) = store(JsonField.ofNullable(store))

            /**
             * Whether or not to store the output of this chat completion request for use in our
             * [model distillation](https://platform.openai.com/docs/guides/distillation) or
             * [evals](https://platform.openai.com/docs/guides/evals) products.
             */
            fun store(store: Boolean) = store(store as Boolean?)

            /**
             * Whether or not to store the output of this chat completion request for use in our
             * [model distillation](https://platform.openai.com/docs/guides/distillation) or
             * [evals](https://platform.openai.com/docs/guides/evals) products.
             */
            @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
            fun store(store: Optional<Boolean>) = store(store.orElse(null) as Boolean?)

            /**
             * Whether or not to store the output of this chat completion request for use in our
             * [model distillation](https://platform.openai.com/docs/guides/distillation) or
             * [evals](https://platform.openai.com/docs/guides/evals) products.
             */
            fun store(store: JsonField<Boolean>) = apply { this.store = store }

            /** Options for streaming response. Only set this when you set `stream: true`. */
            fun streamOptions(streamOptions: ChatCompletionStreamOptions?) =
                streamOptions(JsonField.ofNullable(streamOptions))

            /** Options for streaming response. Only set this when you set `stream: true`. */
            fun streamOptions(streamOptions: Optional<ChatCompletionStreamOptions>) =
                streamOptions(streamOptions.orElse(null))

            /** Options for streaming response. Only set this when you set `stream: true`. */
            fun streamOptions(streamOptions: JsonField<ChatCompletionStreamOptions>) = apply {
                this.streamOptions = streamOptions
            }

            /**
             * 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.
             */
            fun temperature(temperature: Double?) = temperature(JsonField.ofNullable(temperature))

            /**
             * 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.
             */
            fun temperature(temperature: Double) = temperature(temperature as Double?)

            /**
             * 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.
             */
            @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
            fun temperature(temperature: Optional<Double>) =
                temperature(temperature.orElse(null) as Double?)

            /**
             * 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.
             */
            fun temperature(temperature: JsonField<Double>) = apply {
                this.temperature = temperature
            }

            /**
             * Controls which (if any) tool is called by the model. `none` means the model will not
             * call any tool and instead generates a message. `auto` means the model can pick
             * between generating a message or calling one or more tools. `required` means the model
             * must call one or more tools. Specifying a particular tool via `{"type": "function",
             * "function": {"name": "my_function"}}` forces the model to call that tool.
             *
             * `none` is the default when no tools are present. `auto` is the default if tools are
             * present.
             */
            fun toolChoice(toolChoice: ChatCompletionToolChoiceOption) =
                toolChoice(JsonField.of(toolChoice))

            /**
             * Controls which (if any) tool is called by the model. `none` means the model will not
             * call any tool and instead generates a message. `auto` means the model can pick
             * between generating a message or calling one or more tools. `required` means the model
             * must call one or more tools. Specifying a particular tool via `{"type": "function",
             * "function": {"name": "my_function"}}` forces the model to call that tool.
             *
             * `none` is the default when no tools are present. `auto` is the default if tools are
             * present.
             */
            fun toolChoice(toolChoice: JsonField<ChatCompletionToolChoiceOption>) = apply {
                this.toolChoice = toolChoice
            }

            /**
             * `none` means the model will not call any tool and instead generates a message. `auto`
             * means the model can pick between generating a message or calling one or more tools.
             * `required` means the model must call one or more tools.
             */
            fun toolChoice(behavior: ChatCompletionToolChoiceOption.Behavior) =
                toolChoice(ChatCompletionToolChoiceOption.ofBehavior(behavior))

            /**
             * Specifies a tool the model should use. Use to force the model to call a specific
             * function.
             */
            fun toolChoice(chatCompletionNamedToolChoice: ChatCompletionNamedToolChoice) =
                toolChoice(
                    ChatCompletionToolChoiceOption.ofChatCompletionNamedToolChoice(
                        chatCompletionNamedToolChoice
                    )
                )

            /**
             * A list of tools the model may call. Currently, only functions are supported as a
             * tool. Use this to provide a list of functions the model may generate JSON inputs for.
             * A max of 128 functions are supported.
             */
            fun tools(tools: List<ChatCompletionTool>) = tools(JsonField.of(tools))

            /**
             * A list of tools the model may call. Currently, only functions are supported as a
             * tool. Use this to provide a list of functions the model may generate JSON inputs for.
             * A max of 128 functions are supported.
             */
            fun tools(tools: JsonField<List<ChatCompletionTool>>) = apply {
                this.tools = tools.map { it.toMutableList() }
            }

            /**
             * A list of tools the model may call. Currently, only functions are supported as a
             * tool. Use this to provide a list of functions the model may generate JSON inputs for.
             * A max of 128 functions are supported.
             */
            fun addTool(tool: ChatCompletionTool) = apply {
                tools =
                    (tools ?: JsonField.of(mutableListOf())).apply {
                        asKnown()
                            .orElseThrow {
                                IllegalStateException(
                                    "Field was set to non-list type: ${javaClass.simpleName}"
                                )
                            }
                            .add(tool)
                    }
            }

            /**
             * An integer between 0 and 20 specifying the number of most likely tokens to return at
             * each token position, each with an associated log probability. `logprobs` must be set
             * to `true` if this parameter is used.
             */
            fun topLogprobs(topLogprobs: Long?) = topLogprobs(JsonField.ofNullable(topLogprobs))

            /**
             * An integer between 0 and 20 specifying the number of most likely tokens to return at
             * each token position, each with an associated log probability. `logprobs` must be set
             * to `true` if this parameter is used.
             */
            fun topLogprobs(topLogprobs: Long) = topLogprobs(topLogprobs as Long?)

            /**
             * An integer between 0 and 20 specifying the number of most likely tokens to return at
             * each token position, each with an associated log probability. `logprobs` must be set
             * to `true` if this parameter is used.
             */
            @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
            fun topLogprobs(topLogprobs: Optional<Long>) =
                topLogprobs(topLogprobs.orElse(null) as Long?)

            /**
             * An integer between 0 and 20 specifying the number of most likely tokens to return at
             * each token position, each with an associated log probability. `logprobs` must be set
             * to `true` if this parameter is used.
             */
            fun topLogprobs(topLogprobs: JsonField<Long>) = apply { this.topLogprobs = topLogprobs }

            /**
             * 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.
             */
            fun topP(topP: Double?) = topP(JsonField.ofNullable(topP))

            /**
             * 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.
             */
            fun topP(topP: Double) = topP(topP as Double?)

            /**
             * 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.
             */
            @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
            fun topP(topP: Optional<Double>) = topP(topP.orElse(null) as Double?)

            /**
             * 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.
             */
            fun topP(topP: JsonField<Double>) = apply { this.topP = topP }

            /**
             * 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).
             */
            fun user(user: String) = user(JsonField.of(user))

            /**
             * 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).
             */
            fun user(user: JsonField<String>) = apply { this.user = user }

            fun additionalProperties(additionalProperties: Map<String, JsonValue>) = apply {
                this.additionalProperties.clear()
                putAllAdditionalProperties(additionalProperties)
            }

            fun putAdditionalProperty(key: String, value: JsonValue) = apply {
                additionalProperties.put(key, value)
            }

            fun putAllAdditionalProperties(additionalProperties: Map<String, JsonValue>) = apply {
                this.additionalProperties.putAll(additionalProperties)
            }

            fun removeAdditionalProperty(key: String) = apply { additionalProperties.remove(key) }

            fun removeAllAdditionalProperties(keys: Set<String>) = apply {
                keys.forEach(::removeAdditionalProperty)
            }

            fun build(): ChatCompletionCreateBody =
                ChatCompletionCreateBody(
                    checkNotNull(messages) { "`messages` is required but was not set" }
                        .map { it.toImmutable() },
                    checkNotNull(model) { "`model` is required but was not set" },
                    audio,
                    frequencyPenalty,
                    functionCall,
                    (functions ?: JsonMissing.of()).map { it.toImmutable() },
                    logitBias,
                    logprobs,
                    maxCompletionTokens,
                    maxTokens,
                    metadata,
                    (modalities ?: JsonMissing.of()).map { it.toImmutable() },
                    n,
                    parallelToolCalls,
                    prediction,
                    presencePenalty,
                    reasoningEffort,
                    responseFormat,
                    seed,
                    serviceTier,
                    stop,
                    store,
                    streamOptions,
                    temperature,
                    toolChoice,
                    (tools ?: JsonMissing.of()).map { it.toImmutable() },
                    topLogprobs,
                    topP,
                    user,
                    additionalProperties.toImmutable(),
                )
        }

        override fun equals(other: Any?): Boolean {
            if (this === other) {
                return true
            }

            return /* spotless:off */ other is ChatCompletionCreateBody && messages == other.messages && model == other.model && audio == other.audio && frequencyPenalty == other.frequencyPenalty && functionCall == other.functionCall && functions == other.functions && logitBias == other.logitBias && logprobs == other.logprobs && maxCompletionTokens == other.maxCompletionTokens && maxTokens == other.maxTokens && metadata == other.metadata && modalities == other.modalities && n == other.n && parallelToolCalls == other.parallelToolCalls && prediction == other.prediction && presencePenalty == other.presencePenalty && reasoningEffort == other.reasoningEffort && responseFormat == other.responseFormat && seed == other.seed && serviceTier == other.serviceTier && stop == other.stop && store == other.store && streamOptions == other.streamOptions && temperature == other.temperature && toolChoice == other.toolChoice && tools == other.tools && topLogprobs == other.topLogprobs && topP == other.topP && user == other.user && additionalProperties == other.additionalProperties /* spotless:on */
        }

        /* spotless:off */
        private val hashCode: Int by lazy { Objects.hash(messages, model, audio, frequencyPenalty, functionCall, functions, logitBias, logprobs, maxCompletionTokens, maxTokens, metadata, modalities, n, parallelToolCalls, prediction, presencePenalty, reasoningEffort, responseFormat, seed, serviceTier, stop, store, streamOptions, temperature, toolChoice, tools, topLogprobs, topP, user, additionalProperties) }
        /* spotless:on */

        override fun hashCode(): Int = hashCode

        override fun toString() =
            "ChatCompletionCreateBody{messages=$messages, model=$model, audio=$audio, frequencyPenalty=$frequencyPenalty, functionCall=$functionCall, functions=$functions, logitBias=$logitBias, logprobs=$logprobs, maxCompletionTokens=$maxCompletionTokens, maxTokens=$maxTokens, metadata=$metadata, modalities=$modalities, n=$n, parallelToolCalls=$parallelToolCalls, prediction=$prediction, presencePenalty=$presencePenalty, reasoningEffort=$reasoningEffort, responseFormat=$responseFormat, seed=$seed, serviceTier=$serviceTier, stop=$stop, store=$store, streamOptions=$streamOptions, temperature=$temperature, toolChoice=$toolChoice, tools=$tools, topLogprobs=$topLogprobs, topP=$topP, user=$user, additionalProperties=$additionalProperties}"
    }

    fun toBuilder() = Builder().from(this)

    companion object {

        @JvmStatic fun builder() = Builder()
    }

    @NoAutoDetect
    class Builder {

        private var body: ChatCompletionCreateBody.Builder = ChatCompletionCreateBody.builder()
        private var additionalHeaders: Headers.Builder = Headers.builder()
        private var additionalQueryParams: QueryParams.Builder = QueryParams.builder()

        @JvmSynthetic
        internal fun from(chatCompletionCreateParams: ChatCompletionCreateParams) = apply {
            body = chatCompletionCreateParams.body.toBuilder()
            additionalHeaders = chatCompletionCreateParams.additionalHeaders.toBuilder()
            additionalQueryParams = chatCompletionCreateParams.additionalQueryParams.toBuilder()
        }

        /**
         * A list of messages comprising the conversation so far. Depending on the
         * [model](https://platform.openai.com/docs/models) you use, different message types
         * (modalities) are supported, like
         * [text](https://platform.openai.com/docs/guides/text-generation),
         * [images](https://platform.openai.com/docs/guides/vision), and
         * [audio](https://platform.openai.com/docs/guides/audio).
         */
        fun messages(messages: List<ChatCompletionMessageParam>) = apply { body.messages(messages) }

        /**
         * A list of messages comprising the conversation so far. Depending on the
         * [model](https://platform.openai.com/docs/models) you use, different message types
         * (modalities) are supported, like
         * [text](https://platform.openai.com/docs/guides/text-generation),
         * [images](https://platform.openai.com/docs/guides/vision), and
         * [audio](https://platform.openai.com/docs/guides/audio).
         */
        fun messages(messages: JsonField<List<ChatCompletionMessageParam>>) = apply {
            body.messages(messages)
        }

        /**
         * A list of messages comprising the conversation so far. Depending on the
         * [model](https://platform.openai.com/docs/models) you use, different message types
         * (modalities) are supported, like
         * [text](https://platform.openai.com/docs/guides/text-generation),
         * [images](https://platform.openai.com/docs/guides/vision), and
         * [audio](https://platform.openai.com/docs/guides/audio).
         */
        fun addMessage(message: ChatCompletionMessageParam) = apply { body.addMessage(message) }

        /**
         * Developer-provided instructions that the model should follow, regardless of messages sent
         * by the user. With o1 models and newer, `developer` messages replace the previous `system`
         * messages.
         */
        fun addMessage(chatCompletionDeveloperMessageParam: ChatCompletionDeveloperMessageParam) =
            apply {
                body.addMessage(chatCompletionDeveloperMessageParam)
            }

        /**
         * Developer-provided instructions that the model should follow, regardless of messages sent
         * by the user. With o1 models and newer, use `developer` messages for this purpose instead.
         */
        fun addMessage(chatCompletionSystemMessageParam: ChatCompletionSystemMessageParam) = apply {
            body.addMessage(chatCompletionSystemMessageParam)
        }

        /** Messages sent by an end user, containing prompts or additional context information. */
        fun addMessage(chatCompletionUserMessageParam: ChatCompletionUserMessageParam) = apply {
            body.addMessage(chatCompletionUserMessageParam)
        }

        /** Messages sent by the model in response to user messages. */
        fun addMessage(chatCompletionAssistantMessageParam: ChatCompletionAssistantMessageParam) =
            apply {
                body.addMessage(chatCompletionAssistantMessageParam)
            }

        /**
         * A list of messages comprising the conversation so far. Depending on the
         * [model](https://platform.openai.com/docs/models) you use, different message types
         * (modalities) are supported, like
         * [text](https://platform.openai.com/docs/guides/text-generation),
         * [images](https://platform.openai.com/docs/guides/vision), and
         * [audio](https://platform.openai.com/docs/guides/audio).
         */
        fun addMessage(chatCompletionToolMessageParam: ChatCompletionToolMessageParam) = apply {
            body.addMessage(chatCompletionToolMessageParam)
        }

        /**
         * A list of messages comprising the conversation so far. Depending on the
         * [model](https://platform.openai.com/docs/models) you use, different message types
         * (modalities) are supported, like
         * [text](https://platform.openai.com/docs/guides/text-generation),
         * [images](https://platform.openai.com/docs/guides/vision), and
         * [audio](https://platform.openai.com/docs/guides/audio).
         */
        fun addMessage(chatCompletionFunctionMessageParam: ChatCompletionFunctionMessageParam) =
            apply {
                body.addMessage(chatCompletionFunctionMessageParam)
            }

        /**
         * 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.
         */
        fun model(model: ChatModel) = apply { body.model(model) }

        /**
         * 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.
         */
        fun model(model: JsonField<ChatModel>) = apply { body.model(model) }

        /**
         * 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.
         */
        fun model(value: String) = apply { body.model(value) }

        /**
         * Parameters for audio output. Required when audio output is requested with `modalities:
         * ["audio"]`. [Learn more](https://platform.openai.com/docs/guides/audio).
         */
        fun audio(audio: ChatCompletionAudioParam?) = apply { body.audio(audio) }

        /**
         * Parameters for audio output. Required when audio output is requested with `modalities:
         * ["audio"]`. [Learn more](https://platform.openai.com/docs/guides/audio).
         */
        fun audio(audio: Optional<ChatCompletionAudioParam>) = audio(audio.orElse(null))

        /**
         * Parameters for audio output. Required when audio output is requested with `modalities:
         * ["audio"]`. [Learn more](https://platform.openai.com/docs/guides/audio).
         */
        fun audio(audio: JsonField<ChatCompletionAudioParam>) = apply { body.audio(audio) }

        /**
         * 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.
         */
        fun frequencyPenalty(frequencyPenalty: Double?) = apply {
            body.frequencyPenalty(frequencyPenalty)
        }

        /**
         * 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.
         */
        fun frequencyPenalty(frequencyPenalty: Double) =
            frequencyPenalty(frequencyPenalty as Double?)

        /**
         * 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.
         */
        @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
        fun frequencyPenalty(frequencyPenalty: Optional<Double>) =
            frequencyPenalty(frequencyPenalty.orElse(null) as Double?)

        /**
         * 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.
         */
        fun frequencyPenalty(frequencyPenalty: JsonField<Double>) = apply {
            body.frequencyPenalty(frequencyPenalty)
        }

        /**
         * Deprecated in favor of `tool_choice`.
         *
         * Controls which (if any) function is called by the model.
         *
         * `none` means the model will not call a function and instead generates a message.
         *
         * `auto` means the model can pick between generating a message or calling a function.
         *
         * Specifying a particular function via `{"name": "my_function"}` forces the model to call
         * that function.
         *
         * `none` is the default when no functions are present. `auto` is the default if functions
         * are present.
         */
        fun functionCall(functionCall: FunctionCall) = apply { body.functionCall(functionCall) }

        /**
         * Deprecated in favor of `tool_choice`.
         *
         * Controls which (if any) function is called by the model.
         *
         * `none` means the model will not call a function and instead generates a message.
         *
         * `auto` means the model can pick between generating a message or calling a function.
         *
         * Specifying a particular function via `{"name": "my_function"}` forces the model to call
         * that function.
         *
         * `none` is the default when no functions are present. `auto` is the default if functions
         * are present.
         */
        fun functionCall(functionCall: JsonField<FunctionCall>) = apply {
            body.functionCall(functionCall)
        }

        /**
         * `none` means the model will not call a function and instead generates a message. `auto`
         * means the model can pick between generating a message or calling a function.
         */
        fun functionCall(behavior: FunctionCall.Behavior) = apply { body.functionCall(behavior) }

        /**
         * Specifying a particular function via `{"name": "my_function"}` forces the model to call
         * that function.
         */
        fun functionCall(functionCallOption: ChatCompletionFunctionCallOption) = apply {
            body.functionCall(functionCallOption)
        }

        /**
         * Deprecated in favor of `tools`.
         *
         * A list of functions the model may generate JSON inputs for.
         */
        fun functions(functions: List<Function>) = apply { body.functions(functions) }

        /**
         * Deprecated in favor of `tools`.
         *
         * A list of functions the model may generate JSON inputs for.
         */
        fun functions(functions: JsonField<List<Function>>) = apply { body.functions(functions) }

        /**
         * Deprecated in favor of `tools`.
         *
         * A list of functions the model may generate JSON inputs for.
         */
        fun addFunction(function: Function) = apply { body.addFunction(function) }

        /**
         * 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.
         */
        fun logitBias(logitBias: LogitBias?) = apply { body.logitBias(logitBias) }

        /**
         * 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.
         */
        fun logitBias(logitBias: Optional<LogitBias>) = logitBias(logitBias.orElse(null))

        /**
         * 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.
         */
        fun logitBias(logitBias: JsonField<LogitBias>) = apply { body.logitBias(logitBias) }

        /**
         * Whether to return log probabilities of the output tokens or not. If true, returns the log
         * probabilities of each output token returned in the `content` of `message`.
         */
        fun logprobs(logprobs: Boolean?) = apply { body.logprobs(logprobs) }

        /**
         * Whether to return log probabilities of the output tokens or not. If true, returns the log
         * probabilities of each output token returned in the `content` of `message`.
         */
        fun logprobs(logprobs: Boolean) = logprobs(logprobs as Boolean?)

        /**
         * Whether to return log probabilities of the output tokens or not. If true, returns the log
         * probabilities of each output token returned in the `content` of `message`.
         */
        @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
        fun logprobs(logprobs: Optional<Boolean>) = logprobs(logprobs.orElse(null) as Boolean?)

        /**
         * Whether to return log probabilities of the output tokens or not. If true, returns the log
         * probabilities of each output token returned in the `content` of `message`.
         */
        fun logprobs(logprobs: JsonField<Boolean>) = apply { body.logprobs(logprobs) }

        /**
         * An upper bound for the number of tokens that can be generated for a completion, including
         * visible output tokens and
         * [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
         */
        fun maxCompletionTokens(maxCompletionTokens: Long?) = apply {
            body.maxCompletionTokens(maxCompletionTokens)
        }

        /**
         * An upper bound for the number of tokens that can be generated for a completion, including
         * visible output tokens and
         * [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
         */
        fun maxCompletionTokens(maxCompletionTokens: Long) =
            maxCompletionTokens(maxCompletionTokens as Long?)

        /**
         * An upper bound for the number of tokens that can be generated for a completion, including
         * visible output tokens and
         * [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
         */
        @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
        fun maxCompletionTokens(maxCompletionTokens: Optional<Long>) =
            maxCompletionTokens(maxCompletionTokens.orElse(null) as Long?)

        /**
         * An upper bound for the number of tokens that can be generated for a completion, including
         * visible output tokens and
         * [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
         */
        fun maxCompletionTokens(maxCompletionTokens: JsonField<Long>) = apply {
            body.maxCompletionTokens(maxCompletionTokens)
        }

        /**
         * The maximum number of [tokens](/tokenizer) that can be generated in the chat completion.
         * This value can be used to control [costs](https://openai.com/api/pricing/) for text
         * generated via API.
         *
         * This value is now deprecated in favor of `max_completion_tokens`, and is not compatible
         * with [o1 series models](https://platform.openai.com/docs/guides/reasoning).
         */
        fun maxTokens(maxTokens: Long?) = apply { body.maxTokens(maxTokens) }

        /**
         * The maximum number of [tokens](/tokenizer) that can be generated in the chat completion.
         * This value can be used to control [costs](https://openai.com/api/pricing/) for text
         * generated via API.
         *
         * This value is now deprecated in favor of `max_completion_tokens`, and is not compatible
         * with [o1 series models](https://platform.openai.com/docs/guides/reasoning).
         */
        fun maxTokens(maxTokens: Long) = maxTokens(maxTokens as Long?)

        /**
         * The maximum number of [tokens](/tokenizer) that can be generated in the chat completion.
         * This value can be used to control [costs](https://openai.com/api/pricing/) for text
         * generated via API.
         *
         * This value is now deprecated in favor of `max_completion_tokens`, and is not compatible
         * with [o1 series models](https://platform.openai.com/docs/guides/reasoning).
         */
        @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
        fun maxTokens(maxTokens: Optional<Long>) = maxTokens(maxTokens.orElse(null) as Long?)

        /**
         * The maximum number of [tokens](/tokenizer) that can be generated in the chat completion.
         * This value can be used to control [costs](https://openai.com/api/pricing/) for text
         * generated via API.
         *
         * This value is now deprecated in favor of `max_completion_tokens`, and is not compatible
         * with [o1 series models](https://platform.openai.com/docs/guides/reasoning).
         */
        fun maxTokens(maxTokens: JsonField<Long>) = apply { body.maxTokens(maxTokens) }

        /**
         * Developer-defined tags and values used for filtering completions in the
         * [dashboard](https://platform.openai.com/chat-completions).
         */
        fun metadata(metadata: Metadata?) = apply { body.metadata(metadata) }

        /**
         * Developer-defined tags and values used for filtering completions in the
         * [dashboard](https://platform.openai.com/chat-completions).
         */
        fun metadata(metadata: Optional<Metadata>) = metadata(metadata.orElse(null))

        /**
         * Developer-defined tags and values used for filtering completions in the
         * [dashboard](https://platform.openai.com/chat-completions).
         */
        fun metadata(metadata: JsonField<Metadata>) = apply { body.metadata(metadata) }

        /**
         * Output types that you would like the model to generate for this request. Most models are
         * capable of generating text, which is the default:
         *
         * `["text"]`
         *
         * The `gpt-4o-audio-preview` model can also be used to
         * [generate audio](https://platform.openai.com/docs/guides/audio). To request that this
         * model generate both text and audio responses, you can use:
         *
         * `["text", "audio"]`
         */
        fun modalities(modalities: List<ChatCompletionModality>?) = apply {
            body.modalities(modalities)
        }

        /**
         * Output types that you would like the model to generate for this request. Most models are
         * capable of generating text, which is the default:
         *
         * `["text"]`
         *
         * The `gpt-4o-audio-preview` model can also be used to
         * [generate audio](https://platform.openai.com/docs/guides/audio). To request that this
         * model generate both text and audio responses, you can use:
         *
         * `["text", "audio"]`
         */
        fun modalities(modalities: Optional<List<ChatCompletionModality>>) =
            modalities(modalities.orElse(null))

        /**
         * Output types that you would like the model to generate for this request. Most models are
         * capable of generating text, which is the default:
         *
         * `["text"]`
         *
         * The `gpt-4o-audio-preview` model can also be used to
         * [generate audio](https://platform.openai.com/docs/guides/audio). To request that this
         * model generate both text and audio responses, you can use:
         *
         * `["text", "audio"]`
         */
        fun modalities(modalities: JsonField<List<ChatCompletionModality>>) = apply {
            body.modalities(modalities)
        }

        /**
         * Output types that you would like the model to generate for this request. Most models are
         * capable of generating text, which is the default:
         *
         * `["text"]`
         *
         * The `gpt-4o-audio-preview` model can also be used to
         * [generate audio](https://platform.openai.com/docs/guides/audio). To request that this
         * model generate both text and audio responses, you can use:
         *
         * `["text", "audio"]`
         */
        fun addModality(modality: ChatCompletionModality) = apply { body.addModality(modality) }

        /**
         * How many chat completion choices to generate for each input message. Note that you will
         * be charged based on the number of generated tokens across all of the choices. Keep `n` as
         * `1` to minimize costs.
         */
        fun n(n: Long?) = apply { body.n(n) }

        /**
         * How many chat completion choices to generate for each input message. Note that you will
         * be charged based on the number of generated tokens across all of the choices. Keep `n` as
         * `1` to minimize costs.
         */
        fun n(n: Long) = n(n as Long?)

        /**
         * How many chat completion choices to generate for each input message. Note that you will
         * be charged based on the number of generated tokens across all of the choices. Keep `n` as
         * `1` to minimize costs.
         */
        @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
        fun n(n: Optional<Long>) = n(n.orElse(null) as Long?)

        /**
         * How many chat completion choices to generate for each input message. Note that you will
         * be charged based on the number of generated tokens across all of the choices. Keep `n` as
         * `1` to minimize costs.
         */
        fun n(n: JsonField<Long>) = apply { body.n(n) }

        /**
         * Whether to enable
         * [parallel function calling](https://platform.openai.com/docs/guides/function-calling#configuring-parallel-function-calling)
         * during tool use.
         */
        fun parallelToolCalls(parallelToolCalls: Boolean) = apply {
            body.parallelToolCalls(parallelToolCalls)
        }

        /**
         * Whether to enable
         * [parallel function calling](https://platform.openai.com/docs/guides/function-calling#configuring-parallel-function-calling)
         * during tool use.
         */
        fun parallelToolCalls(parallelToolCalls: JsonField<Boolean>) = apply {
            body.parallelToolCalls(parallelToolCalls)
        }

        /**
         * Static predicted output content, such as the content of a text file that is being
         * regenerated.
         */
        fun prediction(prediction: ChatCompletionPredictionContent?) = apply {
            body.prediction(prediction)
        }

        /**
         * Static predicted output content, such as the content of a text file that is being
         * regenerated.
         */
        fun prediction(prediction: Optional<ChatCompletionPredictionContent>) =
            prediction(prediction.orElse(null))

        /**
         * Static predicted output content, such as the content of a text file that is being
         * regenerated.
         */
        fun prediction(prediction: JsonField<ChatCompletionPredictionContent>) = apply {
            body.prediction(prediction)
        }

        /**
         * 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.
         */
        fun presencePenalty(presencePenalty: Double?) = apply {
            body.presencePenalty(presencePenalty)
        }

        /**
         * 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.
         */
        fun presencePenalty(presencePenalty: Double) = presencePenalty(presencePenalty as Double?)

        /**
         * 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.
         */
        @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
        fun presencePenalty(presencePenalty: Optional<Double>) =
            presencePenalty(presencePenalty.orElse(null) as Double?)

        /**
         * 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.
         */
        fun presencePenalty(presencePenalty: JsonField<Double>) = apply {
            body.presencePenalty(presencePenalty)
        }

        /**
         * **o1 models only**
         *
         * Constrains effort on reasoning for
         * [reasoning models](https://platform.openai.com/docs/guides/reasoning). Currently
         * supported values are `low`, `medium`, and `high`. Reducing reasoning effort can result in
         * faster responses and fewer tokens used on reasoning in a response.
         */
        fun reasoningEffort(reasoningEffort: ChatCompletionReasoningEffort) = apply {
            body.reasoningEffort(reasoningEffort)
        }

        /**
         * **o1 models only**
         *
         * Constrains effort on reasoning for
         * [reasoning models](https://platform.openai.com/docs/guides/reasoning). Currently
         * supported values are `low`, `medium`, and `high`. Reducing reasoning effort can result in
         * faster responses and fewer tokens used on reasoning in a response.
         */
        fun reasoningEffort(reasoningEffort: JsonField<ChatCompletionReasoningEffort>) = apply {
            body.reasoningEffort(reasoningEffort)
        }

        /**
         * An object specifying the format that the model must output.
         *
         * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured Outputs
         * which ensures the model will match your supplied JSON schema. Learn more in the
         * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
         *
         * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message the
         * model generates is valid JSON.
         *
         * **Important:** when using JSON mode, you **must** also instruct the model to produce JSON
         * yourself via a system or user message. Without this, the model may generate an unending
         * stream of whitespace until the generation reaches the token limit, resulting in a
         * long-running and seemingly "stuck" request. Also note that the message content may be
         * partially cut off if `finish_reason="length"`, which indicates the generation exceeded
         * `max_tokens` or the conversation exceeded the max context length.
         */
        fun responseFormat(responseFormat: ResponseFormat) = apply {
            body.responseFormat(responseFormat)
        }

        /**
         * An object specifying the format that the model must output.
         *
         * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured Outputs
         * which ensures the model will match your supplied JSON schema. Learn more in the
         * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
         *
         * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message the
         * model generates is valid JSON.
         *
         * **Important:** when using JSON mode, you **must** also instruct the model to produce JSON
         * yourself via a system or user message. Without this, the model may generate an unending
         * stream of whitespace until the generation reaches the token limit, resulting in a
         * long-running and seemingly "stuck" request. Also note that the message content may be
         * partially cut off if `finish_reason="length"`, which indicates the generation exceeded
         * `max_tokens` or the conversation exceeded the max context length.
         */
        fun responseFormat(responseFormat: JsonField<ResponseFormat>) = apply {
            body.responseFormat(responseFormat)
        }

        /**
         * An object specifying the format that the model must output.
         *
         * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured Outputs
         * which ensures the model will match your supplied JSON schema. Learn more in the
         * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
         *
         * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message the
         * model generates is valid JSON.
         *
         * **Important:** when using JSON mode, you **must** also instruct the model to produce JSON
         * yourself via a system or user message. Without this, the model may generate an unending
         * stream of whitespace until the generation reaches the token limit, resulting in a
         * long-running and seemingly "stuck" request. Also note that the message content may be
         * partially cut off if `finish_reason="length"`, which indicates the generation exceeded
         * `max_tokens` or the conversation exceeded the max context length.
         */
        fun responseFormat(responseFormatText: ResponseFormatText) = apply {
            body.responseFormat(responseFormatText)
        }

        /**
         * An object specifying the format that the model must output.
         *
         * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured Outputs
         * which ensures the model will match your supplied JSON schema. Learn more in the
         * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
         *
         * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message the
         * model generates is valid JSON.
         *
         * **Important:** when using JSON mode, you **must** also instruct the model to produce JSON
         * yourself via a system or user message. Without this, the model may generate an unending
         * stream of whitespace until the generation reaches the token limit, resulting in a
         * long-running and seemingly "stuck" request. Also note that the message content may be
         * partially cut off if `finish_reason="length"`, which indicates the generation exceeded
         * `max_tokens` or the conversation exceeded the max context length.
         */
        fun responseFormat(responseFormatJsonObject: ResponseFormatJsonObject) = apply {
            body.responseFormat(responseFormatJsonObject)
        }

        /**
         * An object specifying the format that the model must output.
         *
         * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured Outputs
         * which ensures the model will match your supplied JSON schema. Learn more in the
         * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
         *
         * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message the
         * model generates is valid JSON.
         *
         * **Important:** when using JSON mode, you **must** also instruct the model to produce JSON
         * yourself via a system or user message. Without this, the model may generate an unending
         * stream of whitespace until the generation reaches the token limit, resulting in a
         * long-running and seemingly "stuck" request. Also note that the message content may be
         * partially cut off if `finish_reason="length"`, which indicates the generation exceeded
         * `max_tokens` or the conversation exceeded the max context length.
         */
        fun responseFormat(responseFormatJsonSchema: ResponseFormatJsonSchema) = apply {
            body.responseFormat(responseFormatJsonSchema)
        }

        /**
         * This feature is in Beta. If specified, our system will make a best effort to sample
         * deterministically, such that repeated requests with the same `seed` and parameters should
         * return the same result. Determinism is not guaranteed, and you should refer to the
         * `system_fingerprint` response parameter to monitor changes in the backend.
         */
        fun seed(seed: Long?) = apply { body.seed(seed) }

        /**
         * This feature is in Beta. If specified, our system will make a best effort to sample
         * deterministically, such that repeated requests with the same `seed` and parameters should
         * return the same result. Determinism is not guaranteed, and you should refer to the
         * `system_fingerprint` response parameter to monitor changes in the backend.
         */
        fun seed(seed: Long) = seed(seed as Long?)

        /**
         * This feature is in Beta. If specified, our system will make a best effort to sample
         * deterministically, such that repeated requests with the same `seed` and parameters should
         * return the same result. Determinism is not guaranteed, and you should refer to the
         * `system_fingerprint` response parameter to monitor changes in the backend.
         */
        @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
        fun seed(seed: Optional<Long>) = seed(seed.orElse(null) as Long?)

        /**
         * This feature is in Beta. If specified, our system will make a best effort to sample
         * deterministically, such that repeated requests with the same `seed` and parameters should
         * return the same result. Determinism is not guaranteed, and you should refer to the
         * `system_fingerprint` response parameter to monitor changes in the backend.
         */
        fun seed(seed: JsonField<Long>) = apply { body.seed(seed) }

        /**
         * Specifies the latency tier to use for processing the request. This parameter is relevant
         * for customers subscribed to the scale tier service:
         * - If set to 'auto', and the Project is Scale tier enabled, the system will utilize scale
         *   tier credits until they are exhausted.
         * - If set to 'auto', and the Project is not Scale tier enabled, the request will be
         *   processed using the default service tier with a lower uptime SLA and no latency
         *   guarentee.
         * - If set to 'default', the request will be processed using the default service tier with
         *   a lower uptime SLA and no latency guarentee.
         * - When not set, the default behavior is 'auto'.
         *
         * When this parameter is set, the response body will include the `service_tier` utilized.
         */
        fun serviceTier(serviceTier: ServiceTier?) = apply { body.serviceTier(serviceTier) }

        /**
         * Specifies the latency tier to use for processing the request. This parameter is relevant
         * for customers subscribed to the scale tier service:
         * - If set to 'auto', and the Project is Scale tier enabled, the system will utilize scale
         *   tier credits until they are exhausted.
         * - If set to 'auto', and the Project is not Scale tier enabled, the request will be
         *   processed using the default service tier with a lower uptime SLA and no latency
         *   guarentee.
         * - If set to 'default', the request will be processed using the default service tier with
         *   a lower uptime SLA and no latency guarentee.
         * - When not set, the default behavior is 'auto'.
         *
         * When this parameter is set, the response body will include the `service_tier` utilized.
         */
        fun serviceTier(serviceTier: Optional<ServiceTier>) = serviceTier(serviceTier.orElse(null))

        /**
         * Specifies the latency tier to use for processing the request. This parameter is relevant
         * for customers subscribed to the scale tier service:
         * - If set to 'auto', and the Project is Scale tier enabled, the system will utilize scale
         *   tier credits until they are exhausted.
         * - If set to 'auto', and the Project is not Scale tier enabled, the request will be
         *   processed using the default service tier with a lower uptime SLA and no latency
         *   guarentee.
         * - If set to 'default', the request will be processed using the default service tier with
         *   a lower uptime SLA and no latency guarentee.
         * - When not set, the default behavior is 'auto'.
         *
         * When this parameter is set, the response body will include the `service_tier` utilized.
         */
        fun serviceTier(serviceTier: JsonField<ServiceTier>) = apply {
            body.serviceTier(serviceTier)
        }

        /** Up to 4 sequences where the API will stop generating further tokens. */
        fun stop(stop: Stop) = apply { body.stop(stop) }

        /** Up to 4 sequences where the API will stop generating further tokens. */
        fun stop(stop: JsonField<Stop>) = apply { body.stop(stop) }

        /** Up to 4 sequences where the API will stop generating further tokens. */
        fun stop(string: String) = apply { body.stop(string) }

        /** Up to 4 sequences where the API will stop generating further tokens. */
        fun stopOfStrings(strings: List<String>) = apply { body.stopOfStrings(strings) }

        /**
         * Whether or not to store the output of this chat completion request for use in our
         * [model distillation](https://platform.openai.com/docs/guides/distillation) or
         * [evals](https://platform.openai.com/docs/guides/evals) products.
         */
        fun store(store: Boolean?) = apply { body.store(store) }

        /**
         * Whether or not to store the output of this chat completion request for use in our
         * [model distillation](https://platform.openai.com/docs/guides/distillation) or
         * [evals](https://platform.openai.com/docs/guides/evals) products.
         */
        fun store(store: Boolean) = store(store as Boolean?)

        /**
         * Whether or not to store the output of this chat completion request for use in our
         * [model distillation](https://platform.openai.com/docs/guides/distillation) or
         * [evals](https://platform.openai.com/docs/guides/evals) products.
         */
        @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
        fun store(store: Optional<Boolean>) = store(store.orElse(null) as Boolean?)

        /**
         * Whether or not to store the output of this chat completion request for use in our
         * [model distillation](https://platform.openai.com/docs/guides/distillation) or
         * [evals](https://platform.openai.com/docs/guides/evals) products.
         */
        fun store(store: JsonField<Boolean>) = apply { body.store(store) }

        /** Options for streaming response. Only set this when you set `stream: true`. */
        fun streamOptions(streamOptions: ChatCompletionStreamOptions?) = apply {
            body.streamOptions(streamOptions)
        }

        /** Options for streaming response. Only set this when you set `stream: true`. */
        fun streamOptions(streamOptions: Optional<ChatCompletionStreamOptions>) =
            streamOptions(streamOptions.orElse(null))

        /** Options for streaming response. Only set this when you set `stream: true`. */
        fun streamOptions(streamOptions: JsonField<ChatCompletionStreamOptions>) = apply {
            body.streamOptions(streamOptions)
        }

        /**
         * 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.
         */
        fun temperature(temperature: Double?) = apply { body.temperature(temperature) }

        /**
         * 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.
         */
        fun temperature(temperature: Double) = temperature(temperature as Double?)

        /**
         * 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.
         */
        @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
        fun temperature(temperature: Optional<Double>) =
            temperature(temperature.orElse(null) as Double?)

        /**
         * 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.
         */
        fun temperature(temperature: JsonField<Double>) = apply { body.temperature(temperature) }

        /**
         * Controls which (if any) tool is called by the model. `none` means the model will not call
         * any tool and instead generates a message. `auto` means the model can pick between
         * generating a message or calling one or more tools. `required` means the model must call
         * one or more tools. Specifying a particular tool via `{"type": "function", "function":
         * {"name": "my_function"}}` forces the model to call that tool.
         *
         * `none` is the default when no tools are present. `auto` is the default if tools are
         * present.
         */
        fun toolChoice(toolChoice: ChatCompletionToolChoiceOption) = apply {
            body.toolChoice(toolChoice)
        }

        /**
         * Controls which (if any) tool is called by the model. `none` means the model will not call
         * any tool and instead generates a message. `auto` means the model can pick between
         * generating a message or calling one or more tools. `required` means the model must call
         * one or more tools. Specifying a particular tool via `{"type": "function", "function":
         * {"name": "my_function"}}` forces the model to call that tool.
         *
         * `none` is the default when no tools are present. `auto` is the default if tools are
         * present.
         */
        fun toolChoice(toolChoice: JsonField<ChatCompletionToolChoiceOption>) = apply {
            body.toolChoice(toolChoice)
        }

        /**
         * `none` means the model will not call any tool and instead generates a message. `auto`
         * means the model can pick between generating a message or calling one or more tools.
         * `required` means the model must call one or more tools.
         */
        fun toolChoice(behavior: ChatCompletionToolChoiceOption.Behavior) = apply {
            body.toolChoice(behavior)
        }

        /**
         * Specifies a tool the model should use. Use to force the model to call a specific
         * function.
         */
        fun toolChoice(chatCompletionNamedToolChoice: ChatCompletionNamedToolChoice) = apply {
            body.toolChoice(chatCompletionNamedToolChoice)
        }

        /**
         * A list of tools the model may call. Currently, only functions are supported as a tool.
         * Use this to provide a list of functions the model may generate JSON inputs for. A max of
         * 128 functions are supported.
         */
        fun tools(tools: List<ChatCompletionTool>) = apply { body.tools(tools) }

        /**
         * A list of tools the model may call. Currently, only functions are supported as a tool.
         * Use this to provide a list of functions the model may generate JSON inputs for. A max of
         * 128 functions are supported.
         */
        fun tools(tools: JsonField<List<ChatCompletionTool>>) = apply { body.tools(tools) }

        /**
         * A list of tools the model may call. Currently, only functions are supported as a tool.
         * Use this to provide a list of functions the model may generate JSON inputs for. A max of
         * 128 functions are supported.
         */
        fun addTool(tool: ChatCompletionTool) = apply { body.addTool(tool) }

        /**
         * An integer between 0 and 20 specifying the number of most likely tokens to return at each
         * token position, each with an associated log probability. `logprobs` must be set to `true`
         * if this parameter is used.
         */
        fun topLogprobs(topLogprobs: Long?) = apply { body.topLogprobs(topLogprobs) }

        /**
         * An integer between 0 and 20 specifying the number of most likely tokens to return at each
         * token position, each with an associated log probability. `logprobs` must be set to `true`
         * if this parameter is used.
         */
        fun topLogprobs(topLogprobs: Long) = topLogprobs(topLogprobs as Long?)

        /**
         * An integer between 0 and 20 specifying the number of most likely tokens to return at each
         * token position, each with an associated log probability. `logprobs` must be set to `true`
         * if this parameter is used.
         */
        @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
        fun topLogprobs(topLogprobs: Optional<Long>) =
            topLogprobs(topLogprobs.orElse(null) as Long?)

        /**
         * An integer between 0 and 20 specifying the number of most likely tokens to return at each
         * token position, each with an associated log probability. `logprobs` must be set to `true`
         * if this parameter is used.
         */
        fun topLogprobs(topLogprobs: JsonField<Long>) = apply { body.topLogprobs(topLogprobs) }

        /**
         * 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.
         */
        fun topP(topP: Double?) = apply { body.topP(topP) }

        /**
         * 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.
         */
        fun topP(topP: Double) = topP(topP as Double?)

        /**
         * 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.
         */
        @Suppress("USELESS_CAST") // See https://youtrack.jetbrains.com/issue/KT-74228
        fun topP(topP: Optional<Double>) = topP(topP.orElse(null) as Double?)

        /**
         * 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.
         */
        fun topP(topP: JsonField<Double>) = apply { body.topP(topP) }

        /**
         * 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).
         */
        fun user(user: String) = apply { body.user(user) }

        /**
         * 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).
         */
        fun user(user: JsonField<String>) = apply { body.user(user) }

        fun additionalBodyProperties(additionalBodyProperties: Map<String, JsonValue>) = apply {
            body.additionalProperties(additionalBodyProperties)
        }

        fun putAdditionalBodyProperty(key: String, value: JsonValue) = apply {
            body.putAdditionalProperty(key, value)
        }

        fun putAllAdditionalBodyProperties(additionalBodyProperties: Map<String, JsonValue>) =
            apply {
                body.putAllAdditionalProperties(additionalBodyProperties)
            }

        fun removeAdditionalBodyProperty(key: String) = apply { body.removeAdditionalProperty(key) }

        fun removeAllAdditionalBodyProperties(keys: Set<String>) = apply {
            body.removeAllAdditionalProperties(keys)
        }

        fun additionalHeaders(additionalHeaders: Headers) = apply {
            this.additionalHeaders.clear()
            putAllAdditionalHeaders(additionalHeaders)
        }

        fun additionalHeaders(additionalHeaders: Map<String, Iterable<String>>) = apply {
            this.additionalHeaders.clear()
            putAllAdditionalHeaders(additionalHeaders)
        }

        fun putAdditionalHeader(name: String, value: String) = apply {
            additionalHeaders.put(name, value)
        }

        fun putAdditionalHeaders(name: String, values: Iterable<String>) = apply {
            additionalHeaders.put(name, values)
        }

        fun putAllAdditionalHeaders(additionalHeaders: Headers) = apply {
            this.additionalHeaders.putAll(additionalHeaders)
        }

        fun putAllAdditionalHeaders(additionalHeaders: Map<String, Iterable<String>>) = apply {
            this.additionalHeaders.putAll(additionalHeaders)
        }

        fun replaceAdditionalHeaders(name: String, value: String) = apply {
            additionalHeaders.replace(name, value)
        }

        fun replaceAdditionalHeaders(name: String, values: Iterable<String>) = apply {
            additionalHeaders.replace(name, values)
        }

        fun replaceAllAdditionalHeaders(additionalHeaders: Headers) = apply {
            this.additionalHeaders.replaceAll(additionalHeaders)
        }

        fun replaceAllAdditionalHeaders(additionalHeaders: Map<String, Iterable<String>>) = apply {
            this.additionalHeaders.replaceAll(additionalHeaders)
        }

        fun removeAdditionalHeaders(name: String) = apply { additionalHeaders.remove(name) }

        fun removeAllAdditionalHeaders(names: Set<String>) = apply {
            additionalHeaders.removeAll(names)
        }

        fun additionalQueryParams(additionalQueryParams: QueryParams) = apply {
            this.additionalQueryParams.clear()
            putAllAdditionalQueryParams(additionalQueryParams)
        }

        fun additionalQueryParams(additionalQueryParams: Map<String, Iterable<String>>) = apply {
            this.additionalQueryParams.clear()
            putAllAdditionalQueryParams(additionalQueryParams)
        }

        fun putAdditionalQueryParam(key: String, value: String) = apply {
            additionalQueryParams.put(key, value)
        }

        fun putAdditionalQueryParams(key: String, values: Iterable<String>) = apply {
            additionalQueryParams.put(key, values)
        }

        fun putAllAdditionalQueryParams(additionalQueryParams: QueryParams) = apply {
            this.additionalQueryParams.putAll(additionalQueryParams)
        }

        fun putAllAdditionalQueryParams(additionalQueryParams: Map<String, Iterable<String>>) =
            apply {
                this.additionalQueryParams.putAll(additionalQueryParams)
            }

        fun replaceAdditionalQueryParams(key: String, value: String) = apply {
            additionalQueryParams.replace(key, value)
        }

        fun replaceAdditionalQueryParams(key: String, values: Iterable<String>) = apply {
            additionalQueryParams.replace(key, values)
        }

        fun replaceAllAdditionalQueryParams(additionalQueryParams: QueryParams) = apply {
            this.additionalQueryParams.replaceAll(additionalQueryParams)
        }

        fun replaceAllAdditionalQueryParams(additionalQueryParams: Map<String, Iterable<String>>) =
            apply {
                this.additionalQueryParams.replaceAll(additionalQueryParams)
            }

        fun removeAdditionalQueryParams(key: String) = apply { additionalQueryParams.remove(key) }

        fun removeAllAdditionalQueryParams(keys: Set<String>) = apply {
            additionalQueryParams.removeAll(keys)
        }

        fun build(): ChatCompletionCreateParams =
            ChatCompletionCreateParams(
                body.build(),
                additionalHeaders.build(),
                additionalQueryParams.build(),
            )
    }

    /**
     * Deprecated in favor of `tool_choice`.
     *
     * Controls which (if any) function is called by the model.
     *
     * `none` means the model will not call a function and instead generates a message.
     *
     * `auto` means the model can pick between generating a message or calling a function.
     *
     * Specifying a particular function via `{"name": "my_function"}` forces the model to call that
     * function.
     *
     * `none` is the default when no functions are present. `auto` is the default if functions are
     * present.
     */
    @JsonDeserialize(using = FunctionCall.Deserializer::class)
    @JsonSerialize(using = FunctionCall.Serializer::class)
    class FunctionCall
    private constructor(
        private val behavior: Behavior? = null,
        private val functionCallOption: ChatCompletionFunctionCallOption? = null,
        private val _json: JsonValue? = null,
    ) {

        /**
         * `none` means the model will not call a function and instead generates a message. `auto`
         * means the model can pick between generating a message or calling a function.
         */
        fun behavior(): Optional<Behavior> = Optional.ofNullable(behavior)

        /**
         * Specifying a particular function via `{"name": "my_function"}` forces the model to call
         * that function.
         */
        fun functionCallOption(): Optional<ChatCompletionFunctionCallOption> =
            Optional.ofNullable(functionCallOption)

        fun isBehavior(): Boolean = behavior != null

        fun isFunctionCallOption(): Boolean = functionCallOption != null

        /**
         * `none` means the model will not call a function and instead generates a message. `auto`
         * means the model can pick between generating a message or calling a function.
         */
        fun asBehavior(): Behavior = behavior.getOrThrow("behavior")

        /**
         * Specifying a particular function via `{"name": "my_function"}` forces the model to call
         * that function.
         */
        fun asFunctionCallOption(): ChatCompletionFunctionCallOption =
            functionCallOption.getOrThrow("functionCallOption")

        fun _json(): Optional<JsonValue> = Optional.ofNullable(_json)

        fun <T> accept(visitor: Visitor<T>): T {
            return when {
                behavior != null -> visitor.visitBehavior(behavior)
                functionCallOption != null -> visitor.visitFunctionCallOption(functionCallOption)
                else -> visitor.unknown(_json)
            }
        }

        private var validated: Boolean = false

        fun validate(): FunctionCall = apply {
            if (validated) {
                return@apply
            }

            accept(
                object : Visitor<Unit> {
                    override fun visitBehavior(behavior: Behavior) {}

                    override fun visitFunctionCallOption(
                        functionCallOption: ChatCompletionFunctionCallOption
                    ) {
                        functionCallOption.validate()
                    }
                }
            )
            validated = true
        }

        override fun equals(other: Any?): Boolean {
            if (this === other) {
                return true
            }

            return /* spotless:off */ other is FunctionCall && behavior == other.behavior && functionCallOption == other.functionCallOption /* spotless:on */
        }

        override fun hashCode(): Int = /* spotless:off */ Objects.hash(behavior, functionCallOption) /* spotless:on */

        override fun toString(): String =
            when {
                behavior != null -> "FunctionCall{behavior=$behavior}"
                functionCallOption != null -> "FunctionCall{functionCallOption=$functionCallOption}"
                _json != null -> "FunctionCall{_unknown=$_json}"
                else -> throw IllegalStateException("Invalid FunctionCall")
            }

        companion object {

            /**
             * `none` means the model will not call a function and instead generates a message.
             * `auto` means the model can pick between generating a message or calling a function.
             */
            @JvmStatic fun ofBehavior(behavior: Behavior) = FunctionCall(behavior = behavior)

            /**
             * Specifying a particular function via `{"name": "my_function"}` forces the model to
             * call that function.
             */
            @JvmStatic
            fun ofFunctionCallOption(functionCallOption: ChatCompletionFunctionCallOption) =
                FunctionCall(functionCallOption = functionCallOption)
        }

        interface Visitor<out T> {

            fun visitBehavior(behavior: Behavior): T

            fun visitFunctionCallOption(functionCallOption: ChatCompletionFunctionCallOption): T

            fun unknown(json: JsonValue?): T {
                throw OpenAIInvalidDataException("Unknown FunctionCall: $json")
            }
        }

        class Deserializer : BaseDeserializer<FunctionCall>(FunctionCall::class) {

            override fun ObjectCodec.deserialize(node: JsonNode): FunctionCall {
                val json = JsonValue.fromJsonNode(node)

                tryDeserialize(node, jacksonTypeRef<Behavior>())?.let {
                    return FunctionCall(behavior = it, _json = json)
                }
                tryDeserialize(node, jacksonTypeRef<ChatCompletionFunctionCallOption>()) {
                        it.validate()
                    }
                    ?.let {
                        return FunctionCall(functionCallOption = it, _json = json)
                    }

                return FunctionCall(_json = json)
            }
        }

        class Serializer : BaseSerializer<FunctionCall>(FunctionCall::class) {

            override fun serialize(
                value: FunctionCall,
                generator: JsonGenerator,
                provider: SerializerProvider
            ) {
                when {
                    value.behavior != null -> generator.writeObject(value.behavior)
                    value.functionCallOption != null ->
                        generator.writeObject(value.functionCallOption)
                    value._json != null -> generator.writeObject(value._json)
                    else -> throw IllegalStateException("Invalid FunctionCall")
                }
            }
        }

        class Behavior
        @JsonCreator
        private constructor(
            private val value: JsonField<String>,
        ) : Enum {

            @com.fasterxml.jackson.annotation.JsonValue fun _value(): JsonField<String> = value

            companion object {

                @JvmField val NONE = of("none")

                @JvmField val AUTO = of("auto")

                @JvmStatic fun of(value: String) = Behavior(JsonField.of(value))
            }

            enum class Known {
                NONE,
                AUTO,
            }

            enum class Value {
                NONE,
                AUTO,
                _UNKNOWN,
            }

            fun value(): Value =
                when (this) {
                    NONE -> Value.NONE
                    AUTO -> Value.AUTO
                    else -> Value._UNKNOWN
                }

            fun known(): Known =
                when (this) {
                    NONE -> Known.NONE
                    AUTO -> Known.AUTO
                    else -> throw OpenAIInvalidDataException("Unknown Behavior: $value")
                }

            fun asString(): String = _value().asStringOrThrow()

            override fun equals(other: Any?): Boolean {
                if (this === other) {
                    return true
                }

                return /* spotless:off */ other is Behavior && value == other.value /* spotless:on */
            }

            override fun hashCode() = value.hashCode()

            override fun toString() = value.toString()
        }
    }

    @NoAutoDetect
    class Function
    @JsonCreator
    private constructor(
        @JsonProperty("name")
        @ExcludeMissing
        private val name: JsonField<String> = JsonMissing.of(),
        @JsonProperty("description")
        @ExcludeMissing
        private val description: JsonField<String> = JsonMissing.of(),
        @JsonProperty("parameters")
        @ExcludeMissing
        private val parameters: JsonField<FunctionParameters> = JsonMissing.of(),
        @JsonAnySetter
        private val additionalProperties: Map<String, JsonValue> = immutableEmptyMap(),
    ) {

        /**
         * The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and
         * dashes, with a maximum length of 64.
         */
        fun name(): String = name.getRequired("name")

        /**
         * A description of what the function does, used by the model to choose when and how to call
         * the function.
         */
        fun description(): Optional<String> =
            Optional.ofNullable(description.getNullable("description"))

        /**
         * The parameters the functions accepts, described as a JSON Schema object. See the
         * [guide](https://platform.openai.com/docs/guides/function-calling) for examples, and the
         * [JSON Schema reference](https://json-schema.org/understanding-json-schema/) for
         * documentation about the format.
         *
         * Omitting `parameters` defines a function with an empty parameter list.
         */
        fun parameters(): Optional<FunctionParameters> =
            Optional.ofNullable(parameters.getNullable("parameters"))

        /**
         * The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and
         * dashes, with a maximum length of 64.
         */
        @JsonProperty("name") @ExcludeMissing fun _name(): JsonField<String> = name

        /**
         * A description of what the function does, used by the model to choose when and how to call
         * the function.
         */
        @JsonProperty("description")
        @ExcludeMissing
        fun _description(): JsonField<String> = description

        /**
         * The parameters the functions accepts, described as a JSON Schema object. See the
         * [guide](https://platform.openai.com/docs/guides/function-calling) for examples, and the
         * [JSON Schema reference](https://json-schema.org/understanding-json-schema/) for
         * documentation about the format.
         *
         * Omitting `parameters` defines a function with an empty parameter list.
         */
        @JsonProperty("parameters")
        @ExcludeMissing
        fun _parameters(): JsonField<FunctionParameters> = parameters

        @JsonAnyGetter
        @ExcludeMissing
        fun _additionalProperties(): Map<String, JsonValue> = additionalProperties

        private var validated: Boolean = false

        fun validate(): Function = apply {
            if (validated) {
                return@apply
            }

            name()
            description()
            parameters().ifPresent { it.validate() }
            validated = true
        }

        fun toBuilder() = Builder().from(this)

        companion object {

            @JvmStatic fun builder() = Builder()
        }

        class Builder {

            private var name: JsonField<String>? = null
            private var description: JsonField<String> = JsonMissing.of()
            private var parameters: JsonField<FunctionParameters> = JsonMissing.of()
            private var additionalProperties: MutableMap<String, JsonValue> = mutableMapOf()

            @JvmSynthetic
            internal fun from(function: Function) = apply {
                name = function.name
                description = function.description
                parameters = function.parameters
                additionalProperties = function.additionalProperties.toMutableMap()
            }

            /**
             * The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores
             * and dashes, with a maximum length of 64.
             */
            fun name(name: String) = name(JsonField.of(name))

            /**
             * The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores
             * and dashes, with a maximum length of 64.
             */
            fun name(name: JsonField<String>) = apply { this.name = name }

            /**
             * A description of what the function does, used by the model to choose when and how to
             * call the function.
             */
            fun description(description: String) = description(JsonField.of(description))

            /**
             * A description of what the function does, used by the model to choose when and how to
             * call the function.
             */
            fun description(description: JsonField<String>) = apply {
                this.description = description
            }

            /**
             * The parameters the functions accepts, described as a JSON Schema object. See the
             * [guide](https://platform.openai.com/docs/guides/function-calling) for examples, and
             * the [JSON Schema reference](https://json-schema.org/understanding-json-schema/) for
             * documentation about the format.
             *
             * Omitting `parameters` defines a function with an empty parameter list.
             */
            fun parameters(parameters: FunctionParameters) = parameters(JsonField.of(parameters))

            /**
             * The parameters the functions accepts, described as a JSON Schema object. See the
             * [guide](https://platform.openai.com/docs/guides/function-calling) for examples, and
             * the [JSON Schema reference](https://json-schema.org/understanding-json-schema/) for
             * documentation about the format.
             *
             * Omitting `parameters` defines a function with an empty parameter list.
             */
            fun parameters(parameters: JsonField<FunctionParameters>) = apply {
                this.parameters = parameters
            }

            fun additionalProperties(additionalProperties: Map<String, JsonValue>) = apply {
                this.additionalProperties.clear()
                putAllAdditionalProperties(additionalProperties)
            }

            fun putAdditionalProperty(key: String, value: JsonValue) = apply {
                additionalProperties.put(key, value)
            }

            fun putAllAdditionalProperties(additionalProperties: Map<String, JsonValue>) = apply {
                this.additionalProperties.putAll(additionalProperties)
            }

            fun removeAdditionalProperty(key: String) = apply { additionalProperties.remove(key) }

            fun removeAllAdditionalProperties(keys: Set<String>) = apply {
                keys.forEach(::removeAdditionalProperty)
            }

            fun build(): Function =
                Function(
                    checkNotNull(name) { "`name` is required but was not set" },
                    description,
                    parameters,
                    additionalProperties.toImmutable(),
                )
        }

        override fun equals(other: Any?): Boolean {
            if (this === other) {
                return true
            }

            return /* spotless:off */ other is Function && name == other.name && description == other.description && parameters == other.parameters && additionalProperties == other.additionalProperties /* spotless:on */
        }

        /* spotless:off */
        private val hashCode: Int by lazy { Objects.hash(name, description, parameters, additionalProperties) }
        /* spotless:on */

        override fun hashCode(): Int = hashCode

        override fun toString() =
            "Function{name=$name, description=$description, parameters=$parameters, additionalProperties=$additionalProperties}"
    }

    /**
     * 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.
     */
    @NoAutoDetect
    class LogitBias
    @JsonCreator
    private constructor(
        @JsonAnySetter
        private val additionalProperties: Map<String, JsonValue> = immutableEmptyMap(),
    ) {

        @JsonAnyGetter
        @ExcludeMissing
        fun _additionalProperties(): Map<String, JsonValue> = additionalProperties

        private var validated: Boolean = false

        fun validate(): LogitBias = apply {
            if (validated) {
                return@apply
            }

            validated = true
        }

        fun toBuilder() = Builder().from(this)

        companion object {

            @JvmStatic fun builder() = Builder()
        }

        class Builder {

            private var additionalProperties: MutableMap<String, JsonValue> = mutableMapOf()

            @JvmSynthetic
            internal fun from(logitBias: LogitBias) = apply {
                additionalProperties = logitBias.additionalProperties.toMutableMap()
            }

            fun additionalProperties(additionalProperties: Map<String, JsonValue>) = apply {
                this.additionalProperties.clear()
                putAllAdditionalProperties(additionalProperties)
            }

            fun putAdditionalProperty(key: String, value: JsonValue) = apply {
                additionalProperties.put(key, value)
            }

            fun putAllAdditionalProperties(additionalProperties: Map<String, JsonValue>) = apply {
                this.additionalProperties.putAll(additionalProperties)
            }

            fun removeAdditionalProperty(key: String) = apply { additionalProperties.remove(key) }

            fun removeAllAdditionalProperties(keys: Set<String>) = apply {
                keys.forEach(::removeAdditionalProperty)
            }

            fun build(): LogitBias = LogitBias(additionalProperties.toImmutable())
        }

        override fun equals(other: Any?): Boolean {
            if (this === other) {
                return true
            }

            return /* spotless:off */ other is LogitBias && additionalProperties == other.additionalProperties /* spotless:on */
        }

        /* spotless:off */
        private val hashCode: Int by lazy { Objects.hash(additionalProperties) }
        /* spotless:on */

        override fun hashCode(): Int = hashCode

        override fun toString() = "LogitBias{additionalProperties=$additionalProperties}"
    }

    /**
     * Developer-defined tags and values used for filtering completions in the
     * [dashboard](https://platform.openai.com/chat-completions).
     */
    @NoAutoDetect
    class Metadata
    @JsonCreator
    private constructor(
        @JsonAnySetter
        private val additionalProperties: Map<String, JsonValue> = immutableEmptyMap(),
    ) {

        @JsonAnyGetter
        @ExcludeMissing
        fun _additionalProperties(): Map<String, JsonValue> = additionalProperties

        private var validated: Boolean = false

        fun validate(): Metadata = apply {
            if (validated) {
                return@apply
            }

            validated = true
        }

        fun toBuilder() = Builder().from(this)

        companion object {

            @JvmStatic fun builder() = Builder()
        }

        class Builder {

            private var additionalProperties: MutableMap<String, JsonValue> = mutableMapOf()

            @JvmSynthetic
            internal fun from(metadata: Metadata) = apply {
                additionalProperties = metadata.additionalProperties.toMutableMap()
            }

            fun additionalProperties(additionalProperties: Map<String, JsonValue>) = apply {
                this.additionalProperties.clear()
                putAllAdditionalProperties(additionalProperties)
            }

            fun putAdditionalProperty(key: String, value: JsonValue) = apply {
                additionalProperties.put(key, value)
            }

            fun putAllAdditionalProperties(additionalProperties: Map<String, JsonValue>) = apply {
                this.additionalProperties.putAll(additionalProperties)
            }

            fun removeAdditionalProperty(key: String) = apply { additionalProperties.remove(key) }

            fun removeAllAdditionalProperties(keys: Set<String>) = apply {
                keys.forEach(::removeAdditionalProperty)
            }

            fun build(): Metadata = Metadata(additionalProperties.toImmutable())
        }

        override fun equals(other: Any?): Boolean {
            if (this === other) {
                return true
            }

            return /* spotless:off */ other is Metadata && additionalProperties == other.additionalProperties /* spotless:on */
        }

        /* spotless:off */
        private val hashCode: Int by lazy { Objects.hash(additionalProperties) }
        /* spotless:on */

        override fun hashCode(): Int = hashCode

        override fun toString() = "Metadata{additionalProperties=$additionalProperties}"
    }

    /**
     * An object specifying the format that the model must output.
     *
     * Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured Outputs which
     * ensures the model will match your supplied JSON schema. Learn more in the
     * [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
     *
     * Setting to `{ "type": "json_object" }` enables JSON mode, which ensures the message the model
     * generates is valid JSON.
     *
     * **Important:** when using JSON mode, you **must** also instruct the model to produce JSON
     * yourself via a system or user message. Without this, the model may generate an unending
     * stream of whitespace until the generation reaches the token limit, resulting in a
     * long-running and seemingly "stuck" request. Also note that the message content may be
     * partially cut off if `finish_reason="length"`, which indicates the generation exceeded
     * `max_tokens` or the conversation exceeded the max context length.
     */
    @JsonDeserialize(using = ResponseFormat.Deserializer::class)
    @JsonSerialize(using = ResponseFormat.Serializer::class)
    class ResponseFormat
    private constructor(
        private val responseFormatText: ResponseFormatText? = null,
        private val responseFormatJsonObject: ResponseFormatJsonObject? = null,
        private val responseFormatJsonSchema: ResponseFormatJsonSchema? = null,
        private val _json: JsonValue? = null,
    ) {

        fun responseFormatText(): Optional<ResponseFormatText> =
            Optional.ofNullable(responseFormatText)

        fun responseFormatJsonObject(): Optional<ResponseFormatJsonObject> =
            Optional.ofNullable(responseFormatJsonObject)

        fun responseFormatJsonSchema(): Optional<ResponseFormatJsonSchema> =
            Optional.ofNullable(responseFormatJsonSchema)

        fun isResponseFormatText(): Boolean = responseFormatText != null

        fun isResponseFormatJsonObject(): Boolean = responseFormatJsonObject != null

        fun isResponseFormatJsonSchema(): Boolean = responseFormatJsonSchema != null

        fun asResponseFormatText(): ResponseFormatText =
            responseFormatText.getOrThrow("responseFormatText")

        fun asResponseFormatJsonObject(): ResponseFormatJsonObject =
            responseFormatJsonObject.getOrThrow("responseFormatJsonObject")

        fun asResponseFormatJsonSchema(): ResponseFormatJsonSchema =
            responseFormatJsonSchema.getOrThrow("responseFormatJsonSchema")

        fun _json(): Optional<JsonValue> = Optional.ofNullable(_json)

        fun <T> accept(visitor: Visitor<T>): T {
            return when {
                responseFormatText != null -> visitor.visitResponseFormatText(responseFormatText)
                responseFormatJsonObject != null ->
                    visitor.visitResponseFormatJsonObject(responseFormatJsonObject)
                responseFormatJsonSchema != null ->
                    visitor.visitResponseFormatJsonSchema(responseFormatJsonSchema)
                else -> visitor.unknown(_json)
            }
        }

        private var validated: Boolean = false

        fun validate(): ResponseFormat = apply {
            if (validated) {
                return@apply
            }

            accept(
                object : Visitor<Unit> {
                    override fun visitResponseFormatText(responseFormatText: ResponseFormatText) {
                        responseFormatText.validate()
                    }

                    override fun visitResponseFormatJsonObject(
                        responseFormatJsonObject: ResponseFormatJsonObject
                    ) {
                        responseFormatJsonObject.validate()
                    }

                    override fun visitResponseFormatJsonSchema(
                        responseFormatJsonSchema: ResponseFormatJsonSchema
                    ) {
                        responseFormatJsonSchema.validate()
                    }
                }
            )
            validated = true
        }

        override fun equals(other: Any?): Boolean {
            if (this === other) {
                return true
            }

            return /* spotless:off */ other is ResponseFormat && responseFormatText == other.responseFormatText && responseFormatJsonObject == other.responseFormatJsonObject && responseFormatJsonSchema == other.responseFormatJsonSchema /* spotless:on */
        }

        override fun hashCode(): Int = /* spotless:off */ Objects.hash(responseFormatText, responseFormatJsonObject, responseFormatJsonSchema) /* spotless:on */

        override fun toString(): String =
            when {
                responseFormatText != null ->
                    "ResponseFormat{responseFormatText=$responseFormatText}"
                responseFormatJsonObject != null ->
                    "ResponseFormat{responseFormatJsonObject=$responseFormatJsonObject}"
                responseFormatJsonSchema != null ->
                    "ResponseFormat{responseFormatJsonSchema=$responseFormatJsonSchema}"
                _json != null -> "ResponseFormat{_unknown=$_json}"
                else -> throw IllegalStateException("Invalid ResponseFormat")
            }

        companion object {

            @JvmStatic
            fun ofResponseFormatText(responseFormatText: ResponseFormatText) =
                ResponseFormat(responseFormatText = responseFormatText)

            @JvmStatic
            fun ofResponseFormatJsonObject(responseFormatJsonObject: ResponseFormatJsonObject) =
                ResponseFormat(responseFormatJsonObject = responseFormatJsonObject)

            @JvmStatic
            fun ofResponseFormatJsonSchema(responseFormatJsonSchema: ResponseFormatJsonSchema) =
                ResponseFormat(responseFormatJsonSchema = responseFormatJsonSchema)
        }

        interface Visitor<out T> {

            fun visitResponseFormatText(responseFormatText: ResponseFormatText): T

            fun visitResponseFormatJsonObject(responseFormatJsonObject: ResponseFormatJsonObject): T

            fun visitResponseFormatJsonSchema(responseFormatJsonSchema: ResponseFormatJsonSchema): T

            fun unknown(json: JsonValue?): T {
                throw OpenAIInvalidDataException("Unknown ResponseFormat: $json")
            }
        }

        class Deserializer : BaseDeserializer<ResponseFormat>(ResponseFormat::class) {

            override fun ObjectCodec.deserialize(node: JsonNode): ResponseFormat {
                val json = JsonValue.fromJsonNode(node)

                tryDeserialize(node, jacksonTypeRef<ResponseFormatText>()) { it.validate() }
                    ?.let {
                        return ResponseFormat(responseFormatText = it, _json = json)
                    }
                tryDeserialize(node, jacksonTypeRef<ResponseFormatJsonObject>()) { it.validate() }
                    ?.let {
                        return ResponseFormat(responseFormatJsonObject = it, _json = json)
                    }
                tryDeserialize(node, jacksonTypeRef<ResponseFormatJsonSchema>()) { it.validate() }
                    ?.let {
                        return ResponseFormat(responseFormatJsonSchema = it, _json = json)
                    }

                return ResponseFormat(_json = json)
            }
        }

        class Serializer : BaseSerializer<ResponseFormat>(ResponseFormat::class) {

            override fun serialize(
                value: ResponseFormat,
                generator: JsonGenerator,
                provider: SerializerProvider
            ) {
                when {
                    value.responseFormatText != null ->
                        generator.writeObject(value.responseFormatText)
                    value.responseFormatJsonObject != null ->
                        generator.writeObject(value.responseFormatJsonObject)
                    value.responseFormatJsonSchema != null ->
                        generator.writeObject(value.responseFormatJsonSchema)
                    value._json != null -> generator.writeObject(value._json)
                    else -> throw IllegalStateException("Invalid ResponseFormat")
                }
            }
        }
    }

    class ServiceTier
    @JsonCreator
    private constructor(
        private val value: JsonField<String>,
    ) : Enum {

        @com.fasterxml.jackson.annotation.JsonValue fun _value(): JsonField<String> = value

        companion object {

            @JvmField val AUTO = of("auto")

            @JvmField val DEFAULT = of("default")

            @JvmStatic fun of(value: String) = ServiceTier(JsonField.of(value))
        }

        enum class Known {
            AUTO,
            DEFAULT,
        }

        enum class Value {
            AUTO,
            DEFAULT,
            _UNKNOWN,
        }

        fun value(): Value =
            when (this) {
                AUTO -> Value.AUTO
                DEFAULT -> Value.DEFAULT
                else -> Value._UNKNOWN
            }

        fun known(): Known =
            when (this) {
                AUTO -> Known.AUTO
                DEFAULT -> Known.DEFAULT
                else -> throw OpenAIInvalidDataException("Unknown ServiceTier: $value")
            }

        fun asString(): String = _value().asStringOrThrow()

        override fun equals(other: Any?): Boolean {
            if (this === other) {
                return true
            }

            return /* spotless:off */ other is ServiceTier && value == other.value /* spotless:on */
        }

        override fun hashCode() = value.hashCode()

        override fun toString() = value.toString()
    }

    /** Up to 4 sequences where the API will stop generating further tokens. */
    @JsonDeserialize(using = Stop.Deserializer::class)
    @JsonSerialize(using = Stop.Serializer::class)
    class Stop
    private constructor(
        private val string: String? = null,
        private val strings: List<String>? = null,
        private val _json: JsonValue? = null,
    ) {

        fun string(): Optional<String> = Optional.ofNullable(string)

        fun strings(): Optional<List<String>> = Optional.ofNullable(strings)

        fun isString(): Boolean = string != null

        fun isStrings(): Boolean = strings != null

        fun asString(): String = string.getOrThrow("string")

        fun asStrings(): List<String> = strings.getOrThrow("strings")

        fun _json(): Optional<JsonValue> = Optional.ofNullable(_json)

        fun <T> accept(visitor: Visitor<T>): T {
            return when {
                string != null -> visitor.visitString(string)
                strings != null -> visitor.visitStrings(strings)
                else -> visitor.unknown(_json)
            }
        }

        private var validated: Boolean = false

        fun validate(): Stop = apply {
            if (validated) {
                return@apply
            }

            accept(
                object : Visitor<Unit> {
                    override fun visitString(string: String) {}

                    override fun visitStrings(strings: List<String>) {}
                }
            )
            validated = true
        }

        override fun equals(other: Any?): Boolean {
            if (this === other) {
                return true
            }

            return /* spotless:off */ other is Stop && string == other.string && strings == other.strings /* spotless:on */
        }

        override fun hashCode(): Int = /* spotless:off */ Objects.hash(string, strings) /* spotless:on */

        override fun toString(): String =
            when {
                string != null -> "Stop{string=$string}"
                strings != null -> "Stop{strings=$strings}"
                _json != null -> "Stop{_unknown=$_json}"
                else -> throw IllegalStateException("Invalid Stop")
            }

        companion object {

            @JvmStatic fun ofString(string: String) = Stop(string = string)

            @JvmStatic fun ofStrings(strings: List<String>) = Stop(strings = strings)
        }

        interface Visitor<out T> {

            fun visitString(string: String): T

            fun visitStrings(strings: List<String>): T

            fun unknown(json: JsonValue?): T {
                throw OpenAIInvalidDataException("Unknown Stop: $json")
            }
        }

        class Deserializer : BaseDeserializer<Stop>(Stop::class) {

            override fun ObjectCodec.deserialize(node: JsonNode): Stop {
                val json = JsonValue.fromJsonNode(node)

                tryDeserialize(node, jacksonTypeRef<String>())?.let {
                    return Stop(string = it, _json = json)
                }
                tryDeserialize(node, jacksonTypeRef<List<String>>())?.let {
                    return Stop(strings = it, _json = json)
                }

                return Stop(_json = json)
            }
        }

        class Serializer : BaseSerializer<Stop>(Stop::class) {

            override fun serialize(
                value: Stop,
                generator: JsonGenerator,
                provider: SerializerProvider
            ) {
                when {
                    value.string != null -> generator.writeObject(value.string)
                    value.strings != null -> generator.writeObject(value.strings)
                    value._json != null -> generator.writeObject(value._json)
                    else -> throw IllegalStateException("Invalid Stop")
                }
            }
        }
    }

    override fun equals(other: Any?): Boolean {
        if (this === other) {
            return true
        }

        return /* spotless:off */ other is ChatCompletionCreateParams && body == other.body && additionalHeaders == other.additionalHeaders && additionalQueryParams == other.additionalQueryParams /* spotless:on */
    }

    override fun hashCode(): Int = /* spotless:off */ Objects.hash(body, additionalHeaders, additionalQueryParams) /* spotless:on */

    override fun toString() =
        "ChatCompletionCreateParams{body=$body, additionalHeaders=$additionalHeaders, additionalQueryParams=$additionalQueryParams}"
}
