my-pi/packages/ai/src/providers/openai-codex/request-transformer.ts

export interface ReasoningConfig {
	effort: "none" | "minimal" | "low" | "medium" | "high" | "xhigh";
	summary: "auto" | "concise" | "detailed" | "off" | "on";
}

export interface CodexRequestOptions {
	reasoningEffort?: ReasoningConfig["effort"];
	reasoningSummary?: ReasoningConfig["summary"] | null;
	textVerbosity?: "low" | "medium" | "high";
	include?: string[];
}

export interface InputItem {
	id?: string | null;
	type?: string | null;
	role?: string;
	content?: unknown;
	call_id?: string | null;
	name?: string;
	output?: unknown;
	arguments?: string;
}

export interface RequestBody {
	model: string;
	store?: boolean;
	stream?: boolean;
	instructions?: string;
	input?: InputItem[];
	tools?: unknown;
	temperature?: number;
	reasoning?: Partial<ReasoningConfig>;
	text?: {
		verbosity?: "low" | "medium" | "high";
	};
	include?: string[];
	prompt_cache_key?: string;
	prompt_cache_retention?: "in_memory" | "24h";
	max_output_tokens?: number;
	max_completion_tokens?: number;
	[key: string]: unknown;
}

function clampReasoningEffort(model: string, effort: ReasoningConfig["effort"]): ReasoningConfig["effort"] {
	// Codex backend expects exact model IDs. Do not normalize model names here.
	const modelId = model.includes("/") ? model.split("/").pop()! : model;

	// gpt-5.1 does not support xhigh.
	if (modelId === "gpt-5.1" && effort === "xhigh") {
		return "high";
	}

	// gpt-5.1-codex-mini only supports medium/high.
	if (modelId === "gpt-5.1-codex-mini") {
		return effort === "high" || effort === "xhigh" ? "high" : "medium";
	}

	return effort;
}

function getReasoningConfig(model: string, options: CodexRequestOptions): ReasoningConfig {
	return {
		effort: clampReasoningEffort(model, options.reasoningEffort as ReasoningConfig["effort"]),
		summary: options.reasoningSummary ?? "auto",
	};
}

function filterInput(input: InputItem[] | undefined): InputItem[] | undefined {
	if (!Array.isArray(input)) return input;

	return input
		.filter((item) => item.type !== "item_reference")
		.map((item) => {
			if (item.id != null) {
				const { id: _id, ...rest } = item;
				return rest as InputItem;
			}
			return item;
		});
}

export async function transformRequestBody(
	body: RequestBody,
	options: CodexRequestOptions = {},
	prompt?: { instructions: string; developerMessages: string[] },
): Promise<RequestBody> {
	body.store = false;
	body.stream = true;

	if (body.input && Array.isArray(body.input)) {
		body.input = filterInput(body.input);

		if (body.input) {
			const functionCallIds = new Set(
				body.input
					.filter((item) => item.type === "function_call" && typeof item.call_id === "string")
					.map((item) => item.call_id as string),
			);

			body.input = body.input.map((item) => {
				if (item.type === "function_call_output" && typeof item.call_id === "string") {
					const callId = item.call_id as string;
					if (!functionCallIds.has(callId)) {
						const itemRecord = item as unknown as Record<string, unknown>;
						const toolName = typeof itemRecord.name === "string" ? itemRecord.name : "tool";
						let text = "";
						try {
							const output = itemRecord.output;
							text = typeof output === "string" ? output : JSON.stringify(output);
						} catch {
							text = String(itemRecord.output ?? "");
						}
						if (text.length > 16000) {
							text = `${text.slice(0, 16000)}\n...[truncated]`;
						}
						return {
							type: "message",
							role: "assistant",
							content: `[Previous ${toolName} result; call_id=${callId}]: ${text}`,
						} as InputItem;
					}
				}
				return item;
			});
		}
	}

	if (prompt?.developerMessages && prompt.developerMessages.length > 0 && Array.isArray(body.input)) {
		const developerMessages = prompt.developerMessages.map(
			(text) =>
				({
					type: "message",
					role: "developer",
					content: [{ type: "input_text", text }],
				}) as InputItem,
		);
		body.input = [...developerMessages, ...body.input];
	}

	if (options.reasoningEffort !== undefined) {
		const reasoningConfig = getReasoningConfig(body.model, options);
		body.reasoning = {
			...body.reasoning,
			...reasoningConfig,
		};
	} else {
		delete body.reasoning;
	}

	body.text = {
		...body.text,
		verbosity: options.textVerbosity || "medium",
	};

	const include = Array.isArray(options.include) ? [...options.include] : [];
	include.push("reasoning.encrypted_content");
	body.include = Array.from(new Set(include));

	delete body.max_output_tokens;
	delete body.max_completion_tokens;

	return body;
}