manifest, thin proxy router, and the sidecar backend
ported from the standalone officer-atelier service: providers (adapter-shaped, openai the only adapter today), the generation queue, collections, and the database layer — kept on its own Postgres database rather than folded into officer_db, since both collections and provider_settings had rows on the machine this was extracted from. what changed rather than just moved: - images are served from an authenticated GET /files/:filename instead of an unauthenticated PUBLIC_URL-prefixed static route - the OpenAI-key bootstrap fallback reads the secret store (getSecret) before .env, per-adapter - provider_settings (the one-row-per-adapter shape providers replaced) did not come along — it has no callers left anywhere in the server code, only a migration script that also did not move ping/echo (smoke-test routes with no caller) did not move either; the sidecar's own /_health covers liveness. not yet wired into the platform: sidecar/index.ts exists but nothing mounts it, and web/ has not been written, so this is not yet a discoverable plugin.
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import type { ImageAdapter, ImageEditRequest, ImageRequest, ImageUsage, ProviderResult, UploadedImage } from './types';
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const GENERATIONS_ENDPOINT = 'https://api.openai.com/v1/images/generations';
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const EDITS_ENDPOINT = 'https://api.openai.com/v1/images/edits';
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/**
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* USD per million tokens, from OpenAI's published pricing.
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*
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* A table rather than a computed rate, and one that is allowed to be incomplete: a model missing here
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* yields a null cost, which is honest. Guessing a price for a model we do not know produces a number
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* that looks authoritative and sums into a total that is wrong. Every row also stores its raw `usage`,
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* so any cost here can be recomputed later if these figures move.
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*/
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const PRICING: Record<string, { textInput: number; imageInput: number; imageOutput: number }> = {
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'gpt-image-1': { textInput: 5, imageInput: 10, imageOutput: 40 },
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'gpt-image-1-mini': { textInput: 2, imageInput: 2.5, imageOutput: 8 },
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'gpt-image-1.5': { textInput: 5, imageInput: 8, imageOutput: 32 },
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};
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/** Only the parameters OpenAI accepts, and only those the caller actually set. */
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function body(request: ImageRequest, n: number): Record<string, unknown> {
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const out: Record<string, unknown> = { prompt: request.prompt, model: request.model, n };
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for (const key of [
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'quality',
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'size',
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'response_format',
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'output_format',
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'output_compression',
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'background',
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'moderation',
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'style',
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'user',
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] as const) {
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if (request[key] !== undefined) out[key] = request[key];
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}
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return out;
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}
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export const openaiAdapter: ImageAdapter = {
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name: 'openai',
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label: 'OpenAI',
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models: ['gpt-image-1.5', 'gpt-image-1', 'gpt-image-1-mini', 'dall-e-3', 'dall-e-2'],
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defaultModel: 'gpt-image-1',
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async generate(request, apiKey, signal): Promise<ProviderResult> {
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// One image per call, even when the caller asked for several. Each is its own row with its own
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// cost and duration, and one failure out of four should not discard the three that worked.
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const response = await fetch(GENERATIONS_ENDPOINT, {
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method: 'POST',
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headers: { 'content-type': 'application/json', authorization: `Bearer ${apiKey}` },
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body: JSON.stringify(body(request, 1)),
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signal,
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});
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return decodeImagesResponse(response, request.output_format, signal);
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},
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async edit(request, images, mask, apiKey, signal): Promise<ProviderResult> {
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// multipart/form-data, because that is what the endpoint takes. Built rather than translated:
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// this is a bridge, and every field goes across under the name OpenAI gave it.
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const form = new FormData();
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form.append('prompt', request.prompt);
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form.append('n', '1');
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// Repeated `image[]` — the wire form of an array of files, and what the API expects for the GPT
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// image models. One image still goes as `image[]`; the endpoint accepts either.
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for (const image of images) {
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form.append('image[]', new Blob([image.bytes as BlobPart], { type: image.type }), image.filename);
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}
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if (mask) form.append('mask', new Blob([mask.bytes as BlobPart], { type: mask.type }), mask.filename);
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for (const key of [
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'model',
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'quality',
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'size',
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'response_format',
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'output_format',
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'output_compression',
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'background',
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'input_fidelity',
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'user',
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] as const) {
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const value = request[key];
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if (value !== undefined) form.append(key, String(value));
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}
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// No content-type header: fetch sets it, with the multipart boundary that only it knows.
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const response = await fetch(EDITS_ENDPOINT, {
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method: 'POST',
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headers: { authorization: `Bearer ${apiKey}` },
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body: form,
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signal,
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});
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return decodeImagesResponse(response, request.output_format, signal);
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},
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costUsd(model, usage) {
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const price = PRICING[model];
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if (!price || !usage) return null;
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const textTokens = usage.input_tokens_details?.text_tokens ?? usage.input_tokens ?? 0;
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const imageTokens = usage.input_tokens_details?.image_tokens ?? 0;
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const outputTokens = usage.output_tokens ?? 0;
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const total =
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(textTokens * price.textInput + imageTokens * price.imageInput + outputTokens * price.imageOutput) / 1_000_000;
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return total.toFixed(6);
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},
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};
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/**
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* Turn OpenAI's ImagesResponse into bytes, whichever endpoint produced it.
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*
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* Shared by generate and edit because the response shape is identical — only the request differs.
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*/
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async function decodeImagesResponse(
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response: Response,
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requestedFormat: string | undefined,
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signal?: AbortSignal,
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): Promise<ProviderResult> {
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if (!response.ok) {
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const text = await response.text();
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// Surface OpenAI's own message where there is one — "content policy violation" is worth keeping.
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let detail = text.slice(0, 500);
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try {
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const parsed = JSON.parse(text) as { error?: { message?: string } };
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if (parsed.error?.message) detail = parsed.error.message;
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} catch {}
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throw new Error(`OpenAI ${response.status}: ${detail}`);
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}
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const payload = (await response.json()) as {
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data?: { b64_json?: string; url?: string; revised_prompt?: string }[];
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usage?: ImageUsage;
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background?: string;
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output_format?: string;
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size?: string;
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quality?: string;
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};
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const format = payload.output_format ?? requestedFormat ?? 'png';
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const images = await Promise.all(
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(payload.data ?? []).map(async (item) => {
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if (item.b64_json) {
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return { bytes: Buffer.from(item.b64_json, 'base64'), format, revisedPrompt: item.revised_prompt };
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}
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// dall-e-2 with the default response_format hands back a URL that expires in an hour. Fetch it
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// now: the whole point of Atelier is that the image outlives the request.
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if (item.url) {
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const bytes = new Uint8Array(await (await fetch(item.url, { signal })).arrayBuffer());
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return { bytes, format, revisedPrompt: item.revised_prompt };
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}
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throw new Error('OpenAI returned neither b64_json nor url');
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}),
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);
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return {
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images,
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usage: payload.usage,
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echoed: {
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background: payload.background,
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output_format: payload.output_format,
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size: payload.size,
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quality: payload.quality,
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},
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};
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}
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