pediatric-ai-scribe-v3/test/assistant-image-attachments.test.js

const { test } = require('node:test');
const assert = require('node:assert/strict');
const fs = require('node:fs');
const path = require('node:path');
const vm = require('node:vm');
const express = require('express');
const { JSDOM } = require('jsdom');
const { pathToFileURL } = require('node:url');
const policy = require('../src/utils/clinicalConversation');
const answer = require('../src/utils/clinicalAnswer');
const root = path.join(__dirname, '..');
const read = file => fs.readFileSync(path.join(root, file), 'utf8');
const quiet = { log() {}, warn() {}, error() {}, info() {} };
const b64 = bytes => Buffer.from(bytes).toString('base64');
const canonical = (length, fill = 7) => Buffer.alloc(length, fill).toString('base64');
const png = { dataBase64: canonical(16), mimeType: 'image/png' };
const jpeg = { dataBase64: canonical(32), mimeType: 'image/jpeg' };
const webp = { dataBase64: canonical(48), mimeType: 'image/webp' };

function server(options = {}) {
  const calls = { ai: [], search: [], writes: [], health: [] };
  let saved;
  const db = {
    async getSetting(key) {
      if (options.dbError) throw Error('private diagnostic');
      if (key.endsWith('conversation_chars')) return options.legacyLimit ?? 'broken';
      return options.model || null;
    },
    async get(sql, params) {
      if (sql.includes('COUNT')) return { cnt: 0 };
      return saved && params[1] === 7 ? { id: 1, title: saved[1], payload: saved[2] } : null;
    },
    async run(sql, params) { calls.writes.push(sql); saved = params; return { lastInsertRowid: 1 }; }
  };
  const ai = async (messages, settings) => {
    calls.ai.push({ messages, settings });
    return { content: 'Complete supported answer. [1]', finishReason: 'stop', model: 'synthetic' };
  };
  const source = { number: 1, title: 'Synthetic source', excerpt: 'Synthetic reference.', page: 9 };
  const mocks = {
    express, axios: { async post(url, payload) { if (String(url).includes('/translate')) return { data: { translatedText: 'Synthetic translation.' } }; calls.images.push(payload); return { data: { data: [{ b64_json: 'c3ludGhldGlj' }] } }; } }, crypto: require('node:crypto'), '../db/database': db,
    '../middleware/auth': { authMiddleware() {} },
    './auth': { __sendEmail: async () => false }, '../utils/ai': options.ai || { callAI: ai, callAIStream: ai },
    '../utils/errors': { gatewayUrl: path => 'http://synthetic.invalid' + path }, '../utils/litellm': { getLiteLLMHeaders: () => ({}) }, '../utils/logger': { audit() {}, error() {}, warn() {} },
    '../utils/crypto': { encryptString: value => 'encrypted:' + value, decryptString: value => value.replace(/^encrypted:/, '') },
    '../utils/redis': { async getJson() { return null; }, async setJson() {} }, '../utils/clinicalPromptPool': { createClinicalPromptPool: () => ({}) },
    '../utils/generatedImages': require('../src/utils/generatedImages'),
    '../utils/generatedImageLinks': require('../src/utils/generatedImageLinks'),
    '../utils/visionTool': require('../src/utils/visionTool'),
    '../utils/modelVision': { supportsVision: async () => null },
    '../utils/imageTool': require('../src/utils/imageTool'),
    '../utils/clinicalPrompts': require('../src/utils/clinicalPrompts'),
    '../utils/clinicalMcpClient': {
      async semanticSearch(query) { calls.search.push(query); return {}; }, async getMcpHealth() { calls.health.push('health'); return {}; }
    },
    '../utils/clinicalRetrieval': {
      cleanSourceExcerpt: require('../src/utils/clinicalRetrieval').cleanSourceExcerpt,
      normalizeMcpSearchResponse: () => options.noSources ? [] : [source],
      normalizeMcpMultimodalResponse: () => [], dedupeSources: value => value,
      isVisualSourceQuery: () => false, classifyAndRerankMultimodalResults: async () => []
    },
    '../utils/clinicalAnswer': answer, '../utils/clinicalConversation': policy,
    '../utils/clinicalTranslation': require('../src/utils/clinicalTranslation'),
    '../utils/patientTakehome': require('../src/utils/patientTakehome')
  };
  const module = { exports: {} };
  vm.runInNewContext(read('src/routes/clinicalAssistant.js'), {
    module, exports: module.exports, console: quiet, Buffer, Map,
    process: { env: { CLINICAL_ASSISTANT_MCP_WARMUP: 'false', CLINICAL_ASSISTANT_CONVERSATION_CHARS: options.limit, LITELLM_API_BASE: 'http://synthetic.invalid' } }, setTimeout() {},
    require(name) { assert.ok(Object.hasOwn(mocks, name), 'Unexpected import: ' + name); return mocks[name]; }
  });
  async function request(method, endpoint, body, userId = 7) {
    const handler = module.exports.stack.find(layer => layer.route && layer.route.path === endpoint && layer.route.methods[method]).route.stack.find(layer => layer.method === method).handle;
    const res = {
      statusCode: 200, headers: {}, events: '', body: null,
      status(code) { this.statusCode = code; return this; }, json(body) { this.body = body; return this; },
      setHeader(k, v) { this.headers[k] = v; }, write(text) { this.events += text; }, end() {}, flushHeaders() {}
    };
    await handler({ body, params: { id: 1 }, user: { id: userId } }, res);
    return res;
  }
  return { calls, request };
}

test('attachment policy validates MIME, canonical base64, per-image/count/total limits without provider contact', async () => {
  const oversize = { dataBase64: canonical(5 * 1024 * 1024 + 1), mimeType: 'image/png' };
  const big = { dataBase64: canonical(4 * 1024 * 1024), mimeType: 'image/jpeg' };
  const invalid = [
    { images: [{ ...png, mimeType: 'image/svg+xml' }] },
    { images: [{ ...png, mimeType: 'image/gif' }] },
    { images: [{ ...png, mimeType: 'application/octet-stream' }] },
    { images: [{ ...png, dataBase64: 'not base64!!!' }] },
    { images: [{ ...png, dataBase64: 'SGVsbG8' }] },
    { images: [{ ...png, dataBase64: 'AB==' }] }, // non-canonical trailing bits
    { images: [{ ...png, dataBase64: '' }] },
    { images: [{ ...png, dataBase64: 42 }] },
    { images: [{ mimeType: 'image/png' }] },
    { images: [{ dataBase64: png.dataBase64 }] },
    { images: [null] }, { images: ['png'] }, { images: [{}] },
    { images: 'not-a-list' },
    { images: [png, jpeg, webp, png, jpeg] },
    { images: [oversize] },
    { images: [big, big, big] }
  ];
  for (const body of invalid) {
    for (const endpoint of ['/clinical-assistant/chat', '/clinical-assistant/chat/stream']) {
      const app = server();
      const result = await app.request('post', endpoint, { message: 'What is the recommended dose of acetaminophen for a febrile three year old?', history: [], ...body });
      assert.equal(result.statusCode, 400, JSON.stringify(body.images) + ' on ' + endpoint);
      assert.equal(result.body.code, 'INVALID_ATTACHMENTS');
      assert.equal(app.calls.ai.length + app.calls.search.length, 0, 'no provider or retrieval call on ' + endpoint);
    }
  }
});

test('valid images are normalized, ride the outgoing question only, and are excluded from the UTF-16 text budget', async () => {
  const app = server({ limit: '1000' });
  const images = [{ ...png, extra: 'ignored' }, jpeg, webp, { dataBase64: canonical(64), mimeType: 'image/webp' }];
  const question = 'What about monitoring?';
  const result = await app.request('post', '/clinical-assistant/chat', { message: question, history: [{ role: 'user', content: 'x'.repeat(500) }], images });
  assert.equal(result.statusCode, 200);
  assert.equal(app.calls.search.length, 1);
  assert.equal(app.calls.ai.length, 2, 'rewrite plus generation');
  const generation = app.calls.ai.at(-1);
  assert.deepEqual(generation.settings.images, [png, jpeg, webp, { dataBase64: canonical(64), mimeType: 'image/webp' }], 'normalized, extra fields dropped');
  assert.equal(typeof generation.messages[1].content, 'string', 'the route keeps prompt text; ai.js builds multimodal parts');
  assert.ok(generation.messages[1].content.includes(question));
  assert.equal(app.calls.ai[0].settings.images, undefined, 'the retrieval rewrite is text-only');

  const atLimit = server({ limit: '1000' });
  const budgetBody = { message: 'x', history: [{ role: 'user', content: 'x'.repeat(999) }], images: [png, jpeg, webp, png] };
  assert.equal((await atLimit.request('post', '/clinical-assistant/chat', budgetBody)).statusCode, 200, 'images are excluded from the UTF-16 budget');
  assert.equal((await atLimit.request('post', '/clinical-assistant/chat', { ...budgetBody, message: 'xx' })).statusCode, 413, 'the text boundary is unchanged by images');
  const over = await atLimit.request('post', '/clinical-assistant/chat', { ...budgetBody, message: 'xx' });
  assert.equal(over.body.budget.unit, 'characters');
});

test('attachments are validated before any provider is contacted, greetings included', async () => {
  const bad = server();
  const rejected = await bad.request('post', '/clinical-assistant/chat/stream', { message: 'thanks', history: [], images: [{ dataBase64: canonical(8), mimeType: 'image/bmp' }] });
  assert.equal(rejected.statusCode, 400);
  assert.equal(bad.calls.ai.length + bad.calls.search.length, 0, 'a bad attachment costs nothing');
  // Greetings are no longer short-circuited by a keyword list in the route: the
  // model reads the message in whatever language it was written and decides
  // (see DEFAULT_BEHAVIOR). Attachment validation still runs first regardless.
  const good = server();
  const answered = await good.request('post', '/clinical-assistant/chat', { message: 'hi', history: [], images: [png] });
  assert.equal(answered.statusCode, 200);
  assert.ok(good.calls.ai.length > 0, 'the model decides what a greeting deserves');
});

test('handoff route is gone: images were never part of it and the layer no longer exists', async () => {
  const app = server();
  await assert.rejects(app.request('post', '/clinical-assistant/handoff', { history: [{ role: 'user', content: 'Known facts.' }], images: [{ dataBase64: canonical(8), mimeType: 'image/png' }] }), /route/);
  assert.equal(app.calls.ai.length + app.calls.search.length, 0);
});

function openaiAdapter() {
  const requests = [];
  class OpenAI {
    constructor() {
      this.chat = { completions: { create: async r => {
        requests.push(r);
        if (r.stream) return [{ choices: [{ delta: { content: 'streamed answer' }, finish_reason: null }] }, { choices: [{ delta: {}, finish_reason: 'stop' }] }];
        return { choices: [{ message: { content: 'Synthetic multimodal answer.' }, finish_reason: 'stop' }], usage: null };
      } } };
    }
  }
  const mocks = {
    openai: { OpenAI },
    './models': { ...require('../src/utils/models'), async getAllowedModelIds() { return new Set(['synthetic-vision']); } },
    './generationOptions': require('../src/utils/generationOptions'),
    './logger': { apiCall() {}, error() {} },
    '../db/database': { async getSetting() { return null; } }
  };
  const module = { exports: {} };
  vm.runInNewContext(read('src/utils/ai.js'), {
    module, exports: module.exports, console: quiet, TextDecoder,
    process: { env: { LITELLM_API_BASE: 'http://synthetic.invalid', LITELLM_API_KEY: 'sk-synthetic' } },
    require(name) { assert.ok(Object.hasOwn(mocks, name), 'Unexpected adapter import: ' + name); return mocks[name]; }
  });
  return { requests, ai: module.exports };
}

test('LiteLLM generation builds multimodal parts only on the latest user message and keeps tools/temperature/streaming', async () => {
  const tools = [{ type: 'function', function: { name: 'generate_image', parameters: { type: 'object' } } }];
  const { requests, ai } = openaiAdapter();
  const messages = [
    { role: 'system', content: 'System behavior.' },
    { role: 'user', content: 'Earlier turn.' },
    { role: 'assistant', content: 'Earlier answer.' },
    { role: 'user', content: 'Latest question.' }
  ];
  const result = await ai.callAI(messages, { model: 'synthetic-vision', images: [png, jpeg], tools, temperature: 0.15, maxTokens: 2600 });
  assert.equal(result.content, 'Synthetic multimodal answer.');
  const request = requests[0];
  assert.equal(request.model, 'synthetic-vision');
  assert.equal(request.temperature, 0.15);
  assert.equal(request.max_tokens, 2600);
  assert.deepEqual(request.tools, tools);
  assert.equal(request.tool_choice, 'auto');
  assert.equal(request.parallel_tool_calls, false);
  assert.deepEqual(request.messages[0], { role: 'system', content: 'System behavior.' });
  assert.deepEqual(request.messages[1], { role: 'user', content: 'Earlier turn.' });
  assert.deepEqual(request.messages[2], { role: 'assistant', content: 'Earlier answer.' });
  assert.equal(request.messages[3].role, 'user');
  assert.equal(request.messages[3].content[0].type, 'text');
  assert.equal(request.messages[3].content[0].text, 'Latest question.');
  assert.deepEqual(JSON.parse(JSON.stringify(request.messages[3].content[1])), { type: 'image_url', image_url: { url: 'data:image/png;base64,' + png.dataBase64 } });
  assert.deepEqual(JSON.parse(JSON.stringify(request.messages[3].content[2])), { type: 'image_url', image_url: { url: 'data:image/jpeg;base64,' + jpeg.dataBase64 } });

  const streamed = await ai.callAIStream(messages, { model: 'synthetic-vision', images: [png], tools, temperature: 0.15, maxTokens: 2600 }, () => {});
  assert.equal(streamed.content, 'streamed answer');
  const streamRequest = requests[1];
  assert.equal(streamRequest.stream, true);
  assert.deepEqual(streamRequest.tools, tools);
  assert.equal(streamRequest.messages[3].content.length, 2);
  assert.deepEqual(JSON.parse(JSON.stringify(streamRequest.messages[3].content[1].image_url)), { url: 'data:image/png;base64,' + png.dataBase64 });

  const noImages = await ai.callAI(messages, { model: 'synthetic-vision' });
  assert.equal(noImages.content, 'Synthetic multimodal answer.');
  assert.deepEqual(requests[2].messages[3], { role: 'user', content: 'Latest question.' });
});

test('legacy direct provider adapters refuse image attachments with 400 before contacting the provider', async () => {
  const sdkCalls = [];
  class InvokeModelCommand { constructor(input) { this.input = input; } }
  class ConverseCommand { constructor(input) { this.input = input; } }
  const mocks = {
    openai: { OpenAI: class { constructor() { throw Error('Unexpected OpenAI client'); } } },
    './models': { ...require('../src/utils/models'), async getAllowedModelIds() { return new Set(['anthropic.claude-synthetic', 'amazon/nova-lite', 'google/gemini-2.5-flash']); } },
    './generationOptions': require('../src/utils/generationOptions'),
    './logger': { apiCall() {}, error() {} },
    '../db/database': { async getSetting() { return null; } },
    '@aws-sdk/client-bedrock-runtime': {
      InvokeModelCommand, ConverseCommand,
      BedrockRuntimeClient: class { async send(command) { sdkCalls.push(command); return { body: Buffer.from('{}') }; } }
    }
  };
  const module = { exports: {} };
  vm.runInNewContext(read('src/utils/ai.js'), {
    module, exports: module.exports, console: quiet, TextDecoder,
    process: { env: { AI_PROVIDER: 'bedrock', AWS_BEDROCK_REGION: 'synthetic' } },
    require(name) { assert.ok(Object.hasOwn(mocks, name), 'Unexpected adapter import: ' + name); return mocks[name]; }
  });
  const messages = [{ role: 'user', content: 'Question with image.' }];
  await assert.rejects(module.exports.callAI(messages, { model: 'anthropic.claude-synthetic', images: [png] }), error => {
    assert.equal(error.statusCode, 400);
    assert.equal(error.code, 'IMAGES_UNSUPPORTED_PROVIDER');
    assert.match(error.message, /OpenAI-compatible provider/);
    return true;
  });
  await assert.rejects(module.exports.callAIStream(messages, { model: 'anthropic.claude-synthetic', images: [png] }, () => {}), error => error.statusCode === 400);
  assert.equal(sdkCalls.length, 0, 'refused before any provider contact');

  // AI_PROVIDER=vertex used to select a direct Google SDK adapter. That adapter
  // and its dependency are gone — Google models are reached through LiteLLM now —
  // so asking for it must fall back rather than half-configure something.
  const vertexModule = { exports: {} };
  vm.runInNewContext(read('src/utils/ai.js'), {
    module: vertexModule, exports: vertexModule.exports, console: quiet, TextDecoder,
    process: { env: { AI_PROVIDER: 'vertex', GOOGLE_VERTEX_PROJECT: 'synthetic' } },
    require(name) { assert.ok(Object.hasOwn(mocks, name), 'Unexpected adapter import: ' + name); return mocks[name]; }
  });
  assert.equal(vertexModule.exports.activeProvider, 'openrouter', 'vertex is no longer selectable');
  assert.equal(sdkCalls.length, 0);
});

function browserUI(options = {}) {
  const dom = new JSDOM('<div id="assistant-tab">' + read('public/components/assistant.html') + '</div>', {
    url: 'https://example.test/', runScripts: 'outside-only'
  });
  const calls = { stream: [], save: [] };
  const toasts = [];
  const escapeHtml = text => String(text).replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;');
  const context = {
    window: dom.window, document: dom.window.document, navigator: dom.window.navigator,
    console: quiet, AbortController, TextDecoder, TextEncoder, URL, Blob, crypto: require('node:crypto').webcrypto,
    FileReader: dom.window.FileReader, File: dom.window.File,
    setTimeout() {}, showToast(text, kind) { toasts.push([String(text), kind || '']); }, escapeHtml, escapeAttr: escapeHtml,
    renderAssistantMarkdown: text => escapeHtml(text), renderSourcesList: () => '', EMPTY_PROMPT_SETS: [[]],
    createAssistantExporter: () => ({ invalidate() {}, exportAnswerPdf() {} }),
    createAssistantImageStore: () => ({ renderGeneratedImage: src => '<img src="' + src + '">', clear() {} }),
    isImageRequest: () => false,
    fetchAssistantStatus: async () => ({ success: true, conversationChars: options.limit || 120000 }),
    fetchAssistantExamples: async () => ({}), fetchSavedAssistantChats: async () => ({ success: true, chats: [] }),
    openAssistantStream: async payload => {
      calls.stream.push(payload);
      if (options.stream) return options.stream(payload);
      return new Response('event: done\ndata: ' + JSON.stringify({ success: true, answer: 'Complete response.', sources: [] }) + '\n\n');
    },
    fetchAssistantChat: async payload => ({ success: true, answer: 'Fallback answer.' }),
    saveAssistantChat: async payload => { calls.save.push(payload); return { success: true }; }
  };
  vm.createContext(context);
  vm.runInContext(read('public/js/clinicalAssistant.js').replace(/^import[\s\S]*?from ['"][^'"]+['"];\s*/gm, ''), context);
  context.bindEvents();
  context.conversationChars = options.limit || 120000;
  return { context, document: dom.window.document, calls, toasts, dom };
}

async function realApiHelpers() {
  const module = await import(pathToFileURL(path.join(root, 'public/js/assistant/api.js')).href);
  return module;
}

function attachFile(ui, name, type, bytes) {
  const input = ui.document.getElementById('assistant-attach-input');
  const file = new ui.dom.window.File([bytes], name, { type });
  Object.defineProperty(input, 'files', { value: [file], configurable: true });
  input.dispatchEvent(new ui.dom.window.Event('change'));
}

async function tick() { for (let i = 0; i < 25; i++) await new Promise(resolve => setImmediate(resolve)); }

test('UI attaches only PNG/JPEG/WebP with thumbnails, removal and client-side limits', async () => {
  const api = await realApiHelpers();
  const ui = browserUI();
  ui.context.assistantAttachmentLimits = api.assistantAttachmentLimits;
  ui.context.assistantAttachmentPayload = api.assistantAttachmentPayload;
  const bytes = new Uint8Array([137, 80, 78, 71, 13, 10, 26, 10, 1, 2, 3]);
  attachFile(ui, 'photo.png', 'image/png', bytes);
  attachFile(ui, 'scan.jpeg', 'image/jpeg', bytes);
  await tick();
  const wrap = ui.document.getElementById('assistant-attachments');
  assert.equal(wrap.hidden, false);
  assert.equal(wrap.querySelectorAll('.assistant-attachment').length, 2);
  assert.ok(wrap.querySelector('img').getAttribute('src').startsWith('data:image/png;base64,'));

  attachFile(ui, 'notes.txt', 'text/plain', new Uint8Array([1, 2, 3]));
  await tick();
  assert.equal(wrap.querySelectorAll('.assistant-attachment').length, 2);
  assert.ok(ui.toasts.some(([text]) => /PNG, JPEG and WebP/.test(text)));

  attachFile(ui, 'third.webp', 'image/webp', bytes);
  attachFile(ui, 'fourth.png', 'image/png', bytes);
  await tick();
  assert.equal(wrap.querySelectorAll('.assistant-attachment').length, 4);
  attachFile(ui, 'fifth.png', 'image/png', bytes);
  await tick();
  assert.equal(wrap.querySelectorAll('.assistant-attachment').length, 4);
  assert.ok(ui.toasts.some(([text]) => /maximum of 4/.test(text)));

  attachFile(ui, 'huge.png', 'image/png', new Uint8Array(5 * 1024 * 1024 + 1));
  await tick();
  assert.equal(wrap.querySelectorAll('.assistant-attachment').length, 4);
  assert.ok(ui.toasts.some(([text]) => /5 MiB/.test(text)));

  wrap.querySelector('[data-assistant-remove-attachment="0"]').click();
  await tick();
  assert.equal(wrap.querySelectorAll('.assistant-attachment').length, 3);
  assert.equal(ui.context.attachments.length, 3);
  ui.dom.window.close();
});

test('UI sends images only with the question, clears them on success, persists them in saved chats, and keeps the budget label text-only', async () => {
  const api = await realApiHelpers();
  const ui = browserUI({ limit: 1000 });
  ui.context.assistantAttachmentLimits = api.assistantAttachmentLimits;
  ui.context.assistantAttachmentPayload = api.assistantAttachmentPayload;
  const bytes = new Uint8Array([1, 2, 3, 4]);
  attachFile(ui, 'photo.png', 'image/png', bytes);
  await tick();
  const expected = [{ dataBase64: Buffer.from(bytes).toString('base64'), mimeType: 'image/png' }];

  ui.context.restoreSavedChat({ messages: [{ role: 'user', content: 'x'.repeat(999) }], lastAnswer: '' });
  const input = ui.document.getElementById('assistant-input');
  input.value = 'x';
  input.dispatchEvent(new ui.dom.window.Event('input'));
  assert.equal(ui.document.getElementById('assistant-context-budget'), null, 'the constant character counter is gone; only the approaching-limit warning remains');

  // Pending attachments stay input-only until the question is sent.
  ui.context.performAutosave();
  assert.equal(ui.calls.save[0].images, undefined);
  assert.equal(JSON.stringify(ui.calls.save[0]).includes('dataBase64'), false);

  await ui.context.onAsk();
  await tick();
  assert.deepEqual(JSON.parse(JSON.stringify(ui.calls.stream[0].images)), expected);
  assert.equal(ui.context.attachments.length, 0, 'cleared on successful send');
  assert.equal(ui.document.getElementById('assistant-attachments').hidden, true);
  assert.equal(ui.context.messages.length, 3, 'question and answer appended');

  // The sent question now persists its attachments with the saved chat.
  ui.context.performAutosave();
  assert.ok(JSON.stringify(ui.calls.save.at(-1)).includes('dataBase64'));
  const savedUser = ui.calls.save.at(-1).messages.filter(m => m.role === 'user').at(-1);
  assert.deepEqual(JSON.parse(JSON.stringify(savedUser.attachments)), [{ dataBase64: expected[0].dataBase64, mimeType: 'image/png', name: 'photo.png' }]);

  // New chat clears any later attachments.
  attachFile(ui, 'again.png', 'image/png', bytes);
  await tick();
  assert.equal(ui.context.attachments.length, 1);
  await ui.context.clearConversation();
  assert.equal(ui.context.attachments.length, 0);
  assert.equal(ui.document.getElementById('assistant-attachments').hidden, true);

  attachFile(ui, 'kept.png', 'image/png', bytes);
  await tick();
  assert.equal(ui.context.attachments.length, 1);
  const failing = browserUI({ stream: () => new Response(JSON.stringify({ error: 'Image rejected' }), { status: 400 }) });
  failing.context.assistantAttachmentLimits = api.assistantAttachmentLimits;
  failing.context.assistantAttachmentPayload = api.assistantAttachmentPayload;
  attachFile(failing, 'kept.png', 'image/png', bytes);
  await tick();
  failing.document.getElementById('assistant-input').value = 'Question with attachment';
  await failing.context.onAsk();
  await tick();
  assert.equal(failing.context.attachments.length, 1, 'rejected send keeps the attachments for correction');
  assert.equal(failing.document.getElementById('assistant-attachments').hidden, false);
  ui.dom.window.close();
  failing.dom.window.close();
});