fix(ai): verify native reference image adapters and clarify model states
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@ -1,6 +1,6 @@
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import { act, fireEvent, render, screen, waitFor, within } from '@testing-library/react'
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import AiModelManagementPanel from './AiModelManagementPanel'
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import type { AiModelAdminState, AiModelVerificationSummary } from '@/lib/ai-model-types'
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import type { AiModelAdminState, AiModelCatalogEntry, AiModelVerificationSummary } from '@/lib/ai-model-types'
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function response(body: unknown, status = 200) { return { ok: status < 400, status, json: async () => body } as Response }
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const initial = (): AiModelAdminState => ({ providers: [], revision: 0, defaults: { analysis: null, copy: null, image: null }, encryptionConfigured: true, legacyAvailable: true })
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@ -25,14 +25,17 @@ function jointConfiguration() {
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return next
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}
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test.each(['failed', 'unknown'] as const)('生图 %s 的具体原因直接可见，未知结果不能重试', async status => {
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test.each(['failed', 'unknown'] as const)('生图 %s 显示明确状态，具体原因可展开，未知结果不能重试', async status => {
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const next = configured(), model = next.providers[0].models[1]
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model.enabled = false; model.stages = []
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model.verification.image = { status, id: 'verification-image', checkedAt: null, requestId: 'request-image', message: '上游返回异常响应（502），请核查请求结果；勿重复提交', canReconcile: status === 'unknown' }
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global.fetch = jest.fn(async () => response(next)) as typeof fetch
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render(<AiModelManagementPanel />)
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const row = await screen.findByLabelText('nano-banana-2 · 现有上游', { selector: 'div' })
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expect(within(row).getAllByText(model.verification.image.message!, { selector: 'p' }).find(element => !element.closest('[hidden]'))).toBeVisible()
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expect(within(row).getByRole('status')).toHaveTextContent(status === 'failed' ? '测试失败' : '待核查')
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expect(within(row).getByText(model.verification.image.message!, { selector: 'p' })).not.toBeVisible()
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fireEvent.click(within(row).getByRole('button', { name: 'nano-banana-2 · 现有上游 图片生成测试详情' }))
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expect(within(row).getByText(model.verification.image.message!, { selector: 'p' })).toBeVisible()
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expect(within(row).getByRole('checkbox', { name: 'nano-banana-2 · 现有上游 用于图片生成' })).toBeDisabled()
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if (status === 'unknown') expect(within(row).getByRole('button', { name: '核查 nano-banana-2 · 现有上游 的图片生成结果' })).toBeEnabled()
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expect((global.fetch as jest.Mock).mock.calls.every(([, init]) => !init?.method)).toBe(true)
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@ -278,8 +281,8 @@ test('按能力筛选未开放模型，纯对话不能分配生图或图片分
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expect(screen.queryByText('new-vision-model')).not.toBeInTheDocument()
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const image = screen.getByRole('checkbox', { name: 'text-chat-only · 现有上游 用于图片生成' })
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expect(image).toBeDisabled()
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expect(image).toHaveAccessibleDescription('此模型不支持图片生成')
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expect(image.closest('label')).toHaveAttribute('title', '此模型不支持图片生成')
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expect(image).toHaveAccessibleDescription('当前未接入图片生成用途')
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expect(image.closest('label')).toHaveAttribute('title', '当前未接入图片生成用途')
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expect(screen.getByRole('checkbox', { name: 'text-chat-only · 现有上游 用于资料分析' })).toBeDisabled()
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expect(screen.getByRole('checkbox', { name: 'text-chat-only · 现有上游 用于卖点文案' })).toBeEnabled()
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expect(image).toBeDisabled()
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@ -307,21 +310,90 @@ test('能力待识别模型没有人工兼容开关，不能解锁用途或实
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expect(screen.queryByText('gemini-2.5-flash')).not.toBeInTheDocument()
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})
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test('生图接口未接入时就近显示服务端具体原因', async () => {
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test('生图接口未接入时显示待适配，详情解释原因而不冒充测试结果', async () => {
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const next = configured()
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next.providers[0].models[2] = { ...next.providers[0].models[2], id: 'image-other-protocol', capabilities: { kind: 'image', allowedStages: [], source: 'upstream', reason: '具备图片输出能力，当前参考图协议待适配' } }
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next.providers[0].models[2] = { ...next.providers[0].models[2], id: 'image-other-protocol', capabilities: { kind: 'image', allowedStages: [], source: 'upstream', reason: '参考图待适配：目录已声明图片输入，当前传入方式尚未接入' } }
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global.fetch = jest.fn(async () => response(next)) as typeof fetch
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render(<AiModelManagementPanel />)
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const image = await screen.findByRole('checkbox', { name: 'image-other-protocol · 现有上游 用于图片生成' })
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expect(image).toBeDisabled()
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expect(image.closest('label')).toHaveAttribute('title', '具备图片输出能力，当前参考图协议待适配')
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expect(image).toHaveAccessibleDescription('具备图片输出能力，当前参考图协议待适配')
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expect(screen.getAllByRole('status').find(element => element.textContent === '具备图片输出能力，当前参考图协议待适配')).toBeVisible()
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expect(image.closest('label')).toHaveAttribute('title', '参考图接入待适配，完成后可测试')
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expect(image).toHaveAccessibleDescription('参考图接入待适配，完成后可测试')
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const row = screen.getByLabelText('image-other-protocol · 现有上游', { selector: 'div' })
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expect(within(row).getByRole('status')).toHaveTextContent('待适配')
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expect(within(row).getByText('参考图待适配：目录已声明图片输入，当前传入方式尚未接入')).not.toBeVisible()
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fireEvent.click(within(row).getByRole('button', { name: 'image-other-protocol · 现有上游 参考图接入详情' }))
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expect(within(row).getByText('参考图待适配：目录已声明图片输入，当前传入方式尚未接入')).toBeVisible()
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expect(within(row).queryByRole('button', { name: /^测试 / })).not.toBeInTheDocument()
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fireEvent.change(screen.getByLabelText('按能力筛选模型'), { target: { value: 'image' } })
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expect(screen.getByText('image-other-protocol')).toBeInTheDocument()
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expect(screen.queryByText('gemini-2.5-flash')).not.toBeInTheDocument()
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})
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test('状态筛选区分待适配、未测试、真实失败、待核查和人工核查历史，筛选不触发调用', async () => {
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const next = configured(), template = next.providers[0].models[1]
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const entries = [
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{ key: 'adapting', status: 'untested' }, { key: 'untested', status: 'untested' },
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{ key: 'failed', status: 'failed' }, { key: 'unknown', status: 'unknown' },
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{ key: 'reconciled', status: 'failed' }, { key: 'stale', status: 'stale' },
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] as const
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next.providers[0].models = entries.map(({ key, status }): AiModelCatalogEntry => ({
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...template, key, id: `image-${key}`, enabled: false, stages: [],
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capabilities: { ...template.capabilities, allowedStages: key === 'adapting' ? [] : ['image'], reason: key === 'adapting' ? '参考图待适配：当前目录尚未确认可用的参考图传入方式' : template.capabilities.reason },
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verification: { ...verification(), image: { status, id: status === 'untested' ? null : `verification-${key}`, checkedAt: null, requestId: null, message: key === 'reconciled' ? '人工核查确认历史请求已结束' : null, reconciled: key === 'reconciled' } },
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}))
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next.defaults = { analysis: null, copy: null, image: null }
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global.fetch = jest.fn(async () => response(next)) as typeof fetch
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render(<AiModelManagementPanel />)
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const filter = await screen.findByLabelText('按状态筛选模型')
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for (const { key } of entries) {
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fireEvent.change(filter, { target: { value: key } })
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expect(screen.getByText(`image-${key}`)).toBeInTheDocument()
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for (const other of entries.filter(entry => entry.key !== key)) expect(screen.queryByText(`image-${other.key}`)).not.toBeInTheDocument()
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}
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fireEvent.change(filter, { target: { value: 'reconciled' } })
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const row = screen.getByLabelText('image-reconciled · 现有上游', { selector: 'div' })
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expect(within(row).getByRole('status')).toHaveTextContent('已核查，可重测')
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expect(within(row).queryByText('测试失败')).not.toBeInTheDocument()
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expect(within(row).getByRole('checkbox', { name: 'image-reconciled · 现有上游 用于图片生成' })).toBeDisabled()
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fireEvent.click(within(row).getByRole('button', { name: 'image-reconciled · 现有上游 图片生成测试详情' }))
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expect(within(row).getByText('历史请求经人工核查结束，尚需重新测试。')).toBeVisible()
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expect(within(row).getByText('人工核查确认历史请求已结束')).toBeVisible()
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expect(global.fetch).toHaveBeenCalledTimes(1)
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})
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test('已适配但上游不可用的模型显示真实原因，不进入待适配或未测试筛选', async () => {
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const next = configured(), model = next.providers[0].models[1]
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model.enabled = false; model.stages = []
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model.verification = verification()
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model.capabilities = { kind: 'image', allowedStages: [], source: 'upstream', reason: '上游当前不可用，暂不能测试或开放' }
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global.fetch = jest.fn(async () => response(next)) as typeof fetch
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render(<AiModelManagementPanel />)
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const row = await screen.findByLabelText('nano-banana-2 · 现有上游', { selector: 'div' })
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expect(within(row).getByRole('status')).toHaveTextContent('上游当前不可用，暂不能测试或开放')
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expect(within(row).queryByText('待适配')).not.toBeInTheDocument()
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expect(within(row).queryByRole('button', { name: /^测试 / })).not.toBeInTheDocument()
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expect(within(row).getByRole('checkbox', { name: '开放 nano-banana-2 · 现有上游 给用户' })).toBeDisabled()
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expect(within(row).getByRole('checkbox', { name: 'nano-banana-2 · 现有上游 用于图片生成' })).toBeDisabled()
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for (const value of ['adapting', 'untested']) {
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fireEvent.change(screen.getByLabelText('按状态筛选模型'), { target: { value } })
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expect(screen.queryByText('nano-banana-2')).not.toBeInTheDocument()
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}
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expect(global.fetch).toHaveBeenCalledTimes(1)
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})
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test('上游停用只阻止操作，不掩盖模型的真实失败状态', async () => {
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const next = configured(), provider = next.providers[0], model = provider.models[1]
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provider.enabled = false; model.enabled = false; model.stages = []
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model.verification.image.status = 'failed'
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global.fetch = jest.fn(async () => response(next)) as typeof fetch
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render(<AiModelManagementPanel />)
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const row = await screen.findByLabelText('nano-banana-2 · 现有上游', { selector: 'div' })
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expect(within(row).getByRole('status')).toHaveTextContent('测试失败')
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expect(within(row).getByText('请先启用上游连接')).toBeVisible()
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expect(within(row).getByRole('button', { name: '测试 nano-banana-2 · 现有上游 的图片生成' })).toBeDisabled()
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})
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test('取消最后用途同步关闭开放并清空默认值，保存不会携带失效默认模型', async () => {
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const next = configured()
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global.fetch = jest.fn(async () => response(next)) as typeof fetch
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@ -451,7 +523,9 @@ test.each(['disabled', 'credential'] as const)('测试无法执行时就近显
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const button = await screen.findByRole('button', { name: '测试 new-vision-model · 现有上游 的资料分析' })
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expect(button).toBeDisabled()
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expect(button).toHaveAccessibleDescription(reason === 'disabled' ? '请先启用上游连接' : '请先配置上游密钥')
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expect(within(screen.getByLabelText('new-vision-model · 现有上游', { selector: 'div' })).getByRole('status')).toHaveTextContent(reason === 'disabled' ? '请先启用上游连接' : '请先配置上游密钥')
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const row = screen.getByLabelText('new-vision-model · 现有上游', { selector: 'div' })
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expect(within(row).getByRole('status')).toHaveTextContent('未测试')
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expect(within(row).getByText(reason === 'disabled' ? '请先启用上游连接' : '请先配置上游密钥')).toBeVisible()
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})
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test('有开放设置草稿时查询原异步任务不重新测试且保留草稿', async () => {
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@ -500,7 +574,7 @@ test('测试处理中禁止重复请求；未知结果暂停整个模型并可
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expect(screen.getByRole('button', { name: '同步 现有上游 的模型目录' })).toBeDisabled()
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expect((global.fetch as jest.Mock).mock.calls.filter(([url]) => url.endsWith('/verify'))).toHaveLength(1)
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finish(response(unknown))
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await screen.findByText('结果未知，暂停核查')
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await screen.findByText('待核查', { selector: 'p' })
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expect(testButton).toBeDisabled()
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fireEvent.change(screen.getByLabelText('new-vision-model · 现有上游 测试用途'), { target: { value: 'copy' } })
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expect(screen.getByRole('button', { name: '测试 new-vision-model · 现有上游 的资料分析' })).toBeDisabled()
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@ -552,7 +626,7 @@ test('已有上游任务仅查询结果，无费用弹窗，不发送新验证
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completed.providers[0].models[2].verification.analysis = { status: 'passed', id: 'existing-verification', checkedAt: '2026-10-13T01:00:00Z', requestId: 'existing-task', message: '测试通过' }
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global.fetch = jest.fn(async (url: string) => response(url.endsWith('/refresh') ? completed : next)) as typeof fetch
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render(<AiModelManagementPanel />)
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expect(await screen.findByText('等待上游结果，可查询')).toBeInTheDocument()
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expect(await screen.findByText('待核查', { selector: 'p' })).toBeInTheDocument()
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expect(screen.queryByRole('button', { name: '测试 new-vision-model · 现有上游 的资料分析' })).not.toBeInTheDocument()
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fireEvent.click(screen.getByRole('button', { name: '查询 new-vision-model · 现有上游 的资料分析结果' }))
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await waitFor(() => expect(screen.getByRole('checkbox', { name: 'new-vision-model · 现有上游 用于资料分析' })).toBeEnabled())
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@ -4,7 +4,7 @@ import { useEffect, useMemo, useRef, useState } from 'react'
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import { Info, Plus, RefreshCw, Save, Search } from 'lucide-react'
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import { useConfirmActionDialog } from '@/components/ConfirmActionDialog'
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import { createBrowserUuid } from '@/lib/browser-uuid'
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import { AI_MODEL_TEST_PURPOSES, aiModelTestStages, type AiModelAdminState, type AiModelCatalogEntry, type AiModelProvider, type AiModelStage, type AiModelTestPurpose } from '@/lib/ai-model-types'
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import { AI_MODEL_TEST_PURPOSES, aiModelTestStages, type AiModelAdminState, type AiModelCatalogEntry, type AiModelProvider, type AiModelStage, type AiModelTestPurpose, type AiModelVerificationSummary } from '@/lib/ai-model-types'
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const stages: Array<{ id: AiModelStage; label: string; shortLabel: string }> = [
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{ id: 'analysis', label: '资料分析', shortLabel: '分析' }, { id: 'copy', label: '卖点文案', shortLabel: '文案' }, { id: 'image', label: '图片生成', shortLabel: '生图' },
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@ -20,7 +20,16 @@ const buttonClass = 'inline-flex min-h-11 shrink-0 items-center justify-center g
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const primaryClass = `${buttonClass} !border-[#0050cb] !bg-[#0050cb] !text-white hover:!bg-[#003da3]`
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const checkboxClass = 'h-4 w-4 shrink-0 accent-[#0050cb] focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-[#1677ff] focus-visible:ring-offset-2 disabled:cursor-not-allowed'
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const capabilityLabels = { text: '对话', vision: '视觉理解', image: '生图', tts: '语音合成', asr: '语音识别', music: '音乐', video: '视频', embedding: '向量', 'image-tool': '图像工具', 'three-d': '3D', other: '其他', unknown: '待识别' } as const
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const verificationLabels = { untested: '待测试', running: '测试中', passed: '已通过', failed: '未通过', unknown: '暂停核查', stale: '需重新测试' } as const
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const verificationLabels = { untested: '未测试', running: '测试中', passed: '已通过', failed: '测试失败', unknown: '待核查', stale: '需重新测试' } as const
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function verificationLabel(result: AiModelVerificationSummary) {
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return result.reconciled && result.status === 'failed' ? '已核查，可重测' : verificationLabels[result.status]
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}
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function verificationColor(result: AiModelVerificationSummary) {
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return result.status === 'passed' ? 'text-[#087052]' : result.status === 'unknown' ? 'text-[#854a0e]' : result.status === 'failed' && !result.reconciled ? 'text-[#b42318]' : 'text-[#66758b]'
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}
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function needsReferenceAdaptation(model: AiModelCatalogEntry) {
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return model.capabilities.kind === 'image' && !model.capabilities.allowedStages.includes('image') && model.capabilities.reason.startsWith('参考图待适配：')
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}
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interface ProviderForm { id: string | null; revision: number; name: string; baseUrl: string; apiKey: string }
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interface RecoveryForm { providerId: string; verificationId: string; modelId: string; outcome: 'not_executed' | 'failed' | 'completed'; reference: string; note: string; confirmed: boolean }
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@ -215,8 +224,10 @@ export default function AiModelManagementPanel({ onStateChange }: { onStateChang
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if (statusFilter === 'enabled' && !model.enabled) return false
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if (statusFilter === 'disabled' && model.enabled) return false
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if (statusFilter === 'missing' && model.present) return false
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if (statusFilter === 'untested' && !model.capabilities.allowedStages.some((stage) => ['untested', 'stale'].includes(model.verification[stage].status))) return false
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if (statusFilter === 'attention' && !Object.values(model.verification).some((result) => ['failed', 'unknown', 'running'].includes(result.status))) return false
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if (statusFilter === 'adapting' && !needsReferenceAdaptation(model)) return false
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if (['untested', 'stale', 'passed'].includes(statusFilter) && !model.capabilities.allowedStages.some((stage) => model.verification[stage].status === statusFilter)) return false
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if (['failed', 'unknown', 'running'].includes(statusFilter) && !Object.values(model.verification).some((result) => result.status === statusFilter && !(statusFilter === 'failed' && result.reconciled))) return false
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if (statusFilter === 'reconciled' && !Object.values(model.verification).some((result) => result.reconciled && result.status === 'failed')) return false
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return true
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})
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const providerBlocked = !!busy || dirty || !!form || !!recovery
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@ -279,12 +290,14 @@ export default function AiModelManagementPanel({ onStateChange }: { onStateChang
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})}
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</fieldset>
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{!!rows.length && <>
|
||||
<div className="grid min-w-0 grid-cols-2 gap-2 sm:flex sm:flex-wrap"><label className="relative col-span-2 min-w-0 sm:flex-1 sm:basis-48"><span className="sr-only">搜索模型或上游</span><Search className="pointer-events-none absolute left-3 top-3.5 h-4 w-4 text-[#66758b]" aria-hidden="true" /><input className={`${fieldClass} !pl-9`} value={search} onChange={(event) => { setSearch(event.target.value); setPage(1) }} placeholder="搜索模型或上游" /></label><select aria-label="按能力筛选模型" className={`${fieldClass} sm:!w-auto sm:flex-none`} value={capabilityFilter} onChange={(event) => { setCapabilityFilter(event.target.value); setPage(1) }}><option value="all">全部能力</option>{Object.entries(capabilityLabels).map(([kind, label]) => <option key={kind} value={kind}>{label}</option>)}</select><select aria-label="按状态筛选模型" className={`${fieldClass} sm:!w-auto sm:flex-none`} value={statusFilter} onChange={(event) => { setStatusFilter(event.target.value); setPage(1) }}><option value="all">全部状态</option><option value="enabled">已开放</option><option value="disabled">未开放</option><option value="untested">待测试</option><option value="attention">待处理</option><option value="missing">已离开目录</option></select></div>
|
||||
<div className="grid min-w-0 grid-cols-2 gap-2 sm:flex sm:flex-wrap"><label className="relative col-span-2 min-w-0 sm:flex-1 sm:basis-48"><span className="sr-only">搜索模型或上游</span><Search className="pointer-events-none absolute left-3 top-3.5 h-4 w-4 text-[#66758b]" aria-hidden="true" /><input className={`${fieldClass} !pl-9`} value={search} onChange={(event) => { setSearch(event.target.value); setPage(1) }} placeholder="搜索模型或上游" /></label><select aria-label="按能力筛选模型" className={`${fieldClass} sm:!w-auto sm:flex-none`} value={capabilityFilter} onChange={(event) => { setCapabilityFilter(event.target.value); setPage(1) }}><option value="all">全部能力</option>{Object.entries(capabilityLabels).map(([kind, label]) => <option key={kind} value={kind}>{label}</option>)}</select><select aria-label="按状态筛选模型" className={`${fieldClass} sm:!w-auto sm:flex-none`} value={statusFilter} onChange={(event) => { setStatusFilter(event.target.value); setPage(1) }}><option value="all">全部状态</option><option value="enabled">已开放</option><option value="disabled">未开放</option><option value="adapting">待适配</option><option value="untested">未测试</option><option value="running">测试中</option><option value="passed">已通过</option><option value="failed">测试失败</option><option value="unknown">待核查</option><option value="reconciled">已核查，可重测</option><option value="stale">需重新测试</option><option value="missing">已离开目录</option></select></div>
|
||||
<p className="text-xs text-[#66758b]">多模态模型一次测试分析与文案，通过的用途可勾选；开放后保存配置。</p>
|
||||
<div className="hidden grid-cols-[minmax(0,1fr)_88px_200px_248px] gap-3 border-b border-[#e2e8f0] bg-[#f8fafc] px-3 py-2 text-xs font-semibold text-[#4f6077] xl:grid" aria-hidden="true"><span>模型 / 上游</span><span>开放给用户</span><span>用户可选用途</span><span>实际测试</span></div>
|
||||
<div className="divide-y divide-[#e2e8f0] border-b border-[#e2e8f0]" aria-label="模型目录">
|
||||
{!visibleRows.length && <p role="status" className="px-3 py-5 text-sm text-[#66758b]">没有匹配模型</p>}
|
||||
{pageRows.map(({ provider, model }) => {
|
||||
const referenceAdaptation = needsReferenceAdaptation(model)
|
||||
const capabilityReason = referenceAdaptation ? '参考图接入待适配，完成后可测试' : model.capabilities.reason
|
||||
const testableStages = testPurposes(model)
|
||||
const unresolvedStage = stages.find(stage => ['unknown', 'running'].includes(model.verification[stage.id].status))
|
||||
const unresolvedPurpose = unresolvedStage && (model.verification[unresolvedStage.id].purpose || unresolvedStage.id)
|
||||
@ -296,7 +309,7 @@ export default function AiModelManagementPanel({ onStateChange }: { onStateChang
|
||||
const protocol = resultStage ? model.capabilities.protocols?.[resultStage] : null
|
||||
const detailsId = `test-details-${model.key}-${selectedStage?.id}`
|
||||
const canInspect = !!protocol || testedStages.some(stage => { const value = model.verification[stage]; return !!(value.message || value.checkedAt || value.requestId) })
|
||||
const retest = testedStages.some(stage => { const value = model.verification[stage]; return value.status === 'passed' || value.status === 'stale' || value.reconciled })
|
||||
const retest = testedStages.some(stage => { const value = model.verification[stage]; return value.status === 'passed' || value.status === 'failed' || value.status === 'stale' || value.reconciled })
|
||||
const unsettled = Object.values(model.verification).some((entry) => entry.status === 'running' || entry.status === 'unknown')
|
||||
const testing = busy.startsWith(`verify:${model.key}:`)
|
||||
const attempt = uncertainAttempts.current.get(model.key)
|
||||
@ -307,16 +320,18 @@ export default function AiModelManagementPanel({ onStateChange }: { onStateChang
|
||||
: !provider.hasApiKey ? '请先配置上游密钥'
|
||||
: !model.present ? '模型已离开上游目录，请同步目录'
|
||||
: unsettled ? '已有测试进行中或结果未知，请先查询状态'
|
||||
: !selectedStage ? model.capabilities.reason : ''
|
||||
: !selectedStage ? capabilityReason : ''
|
||||
const testStatusId = `test-status-${model.key}`
|
||||
const blockedReasonId = `test-blocked-${model.key}`
|
||||
const showBlockedReason = !!blockedReason && !testing && !attempt && !unsettled
|
||||
return <div key={model.key} aria-label={`${model.id} · ${provider.name}`} className="grid min-w-0 gap-x-3 gap-y-1 px-3 py-3 sm:grid-cols-[minmax(0,1fr)_auto] xl:grid-cols-[minmax(0,1fr)_88px_200px_248px] xl:items-center">
|
||||
<div className="min-w-0"><p className="break-all text-sm font-medium">{model.id}</p><p className="mt-1 break-words text-xs text-[#66758b]"><span>{capabilityLabels[model.capabilities.kind]}</span> · {provider.name}{!provider.enabled ? ' · 上游已停用' : ''}{!model.present ? ' · 已离开上游目录' : ''}</p><span id={`capability-${model.key}`} className="sr-only">{model.capabilities.reason}</span></div>
|
||||
<div className="min-w-0"><p className="break-all text-sm font-medium">{model.id}</p><p className="mt-1 break-words text-xs text-[#66758b]"><span>{capabilityLabels[model.capabilities.kind]}</span> · {provider.name}{!provider.enabled ? ' · 上游已停用' : ''}{!model.present ? ' · 已离开上游目录' : ''}</p><span id={`capability-${model.key}`} className="sr-only">{capabilityReason}</span></div>
|
||||
<label className="inline-flex min-h-11 items-center gap-2 text-sm"><input type="checkbox" className={checkboxClass} checked={model.enabled} disabled={!!busy || !!form || !model.enabled && (!model.present || !provider.enabled || !provider.hasApiKey || !model.stages.length || !model.stages.every((stage) => model.capabilities.allowedStages.includes(stage) && model.verification[stage].status === 'passed'))} aria-label={`开放 ${model.id} · ${provider.name} 给用户`} onChange={(event) => updateModel(provider.id, model.key, { enabled: event.target.checked })} />开放</label>
|
||||
<fieldset disabled={!!busy || !!form} className="flex min-w-0 flex-wrap gap-x-3"><legend className="sr-only">{model.id} 的用户可选用途</legend>{stages.map((stage) => {
|
||||
const adapted = model.capabilities.allowedStages.includes(stage.id)
|
||||
const supported = adapted && model.verification[stage.id].status === 'passed'
|
||||
const checked = model.stages.includes(stage.id)
|
||||
const reason = supported ? `${stage.label}测试已通过` : adapted ? `${stage.label}：${verificationLabels[model.verification[stage.id].status]}，通过后可开放` : model.capabilities.allowedStages.length ? `此模型不支持${stage.label}` : model.capabilities.reason
|
||||
const reason = supported ? `${stage.label}测试已通过` : adapted ? `${stage.label}：${verificationLabel(model.verification[stage.id])}，通过后可开放` : referenceAdaptation && stage.id === 'image' ? capabilityReason : model.capabilities.allowedStages.length || referenceAdaptation ? `当前未接入${stage.label}用途` : capabilityReason
|
||||
return <label key={stage.id} title={reason} className={`inline-flex min-h-11 items-center gap-1.5 text-xs ${!supported ? 'text-[#66758b]' : ''}`}><input type="checkbox" className={checkboxClass} aria-label={`${model.id} · ${provider.name} 用于${stage.label}`} aria-describedby={`purpose-${model.key}-${stage.id}`} checked={checked} disabled={!supported && !checked} onChange={(event) => { if (!event.target.checked || supported) updateModel(provider.id, model.key, { stages: event.target.checked ? [...model.stages, stage.id] : model.stages.filter((value) => value !== stage.id) }) }} />{stage.shortLabel}<span id={`purpose-${model.key}-${stage.id}`} className="sr-only">{reason}</span></label>
|
||||
})}</fieldset>
|
||||
<div className="min-w-0 space-y-1.5">
|
||||
@ -328,20 +343,22 @@ export default function AiModelManagementPanel({ onStateChange }: { onStateChang
|
||||
{attempt ? <button type="button" className={buttonClass} disabled={statusBlocked} aria-label={`核查 ${model.id} · ${provider.name} 的测试提交`} onClick={() => void run(`submission:${model.key}`, () => request(`/providers/${encodeURIComponent(provider.id)}/models/${encodeURIComponent(model.key)}/submission`, { method: 'POST', body: JSON.stringify({ stage: attempt.stage, idempotencyKey: attempt.requestId }) }), '', { preserveDraft: true })}>核查提交</button> : result.canResume && result.id ? <button type="button" className={buttonClass} disabled={statusBlocked || !provider.enabled || !provider.hasApiKey} aria-label={`查询 ${model.id} · ${provider.name} 的${selectedStage.label}结果`} onClick={() => {
|
||||
setTestStages((current) => ({ ...current, [model.key]: selectedStage.id }))
|
||||
void run(`result:${model.key}`, () => request(`/providers/${encodeURIComponent(provider.id)}/verifications/${encodeURIComponent(result.id!)}/refresh`, { method: 'POST' }), '', { preserveDraft: true })
|
||||
}}>{busy === `result:${model.key}` ? '查询中…' : '查询结果'}</button> : result.canReconcile && result.id ? <button type="button" className={buttonClass} disabled={statusBlocked} aria-label={`核查 ${model.id} · ${provider.name} 的${selectedStage.label}结果`} onClick={() => { setError(''); setNotice(''); setRecovery({ providerId: provider.id, verificationId: result.id!, modelId: model.id, outcome: 'not_executed', reference: '', note: '', confirmed: false }) }}>核查结果</button> : <button type="button" className={buttonClass} disabled={!!blockedReason} aria-describedby={testStatusId} aria-label={`测试 ${model.id} · ${provider.name} 的${selectedStage.label}`} onClick={() => void testModel(provider, model, selectedStage.id)}>{testing || result.status === 'running' ? '测试中…' : retest ? '重测' : '测试'}</button>}
|
||||
}}>{busy === `result:${model.key}` ? '查询中…' : '查询结果'}</button> : result.canReconcile && result.id ? <button type="button" className={buttonClass} disabled={statusBlocked} aria-label={`核查 ${model.id} · ${provider.name} 的${selectedStage.label}结果`} onClick={() => { setError(''); setNotice(''); setRecovery({ providerId: provider.id, verificationId: result.id!, modelId: model.id, outcome: 'not_executed', reference: '', note: '', confirmed: false }) }}>核查结果</button> : <button type="button" className={buttonClass} disabled={!!blockedReason} aria-describedby={showBlockedReason ? blockedReasonId : testStatusId} aria-label={`测试 ${model.id} · ${provider.name} 的${selectedStage.label}`} onClick={() => void testModel(provider, model, selectedStage.id)}>{testing || result.status === 'running' ? '测试中…' : retest ? '重测' : '测试'}</button>}
|
||||
{canInspect && <button type="button" className={`${buttonClass} !w-11 !px-0`} aria-label={`${model.id} · ${provider.name} ${selectedStage.label}测试详情`} aria-expanded={!!expandedDetails[detailsId]} aria-controls={detailsId} title="测试详情" onClick={() => setExpandedDetails((current) => ({ ...current, [detailsId]: !current[detailsId] }))}><Info className="h-4 w-4" aria-hidden="true" /></button>}
|
||||
</div>
|
||||
<p id={testStatusId} role="status" className={`flex flex-wrap gap-x-3 gap-y-1 text-xs ${blockedReason ? 'text-[#66758b]' : result.status === 'passed' ? 'text-[#087052]' : result.status === 'failed' || result.status === 'unknown' ? 'text-[#b42318]' : 'text-[#66758b]'}`}>{testing ? '正在调用模型，请稍候' : attempt ? '提交结果未确认，请核查' : selectedStage.id === 'analysis-copy' ? testedStages.map(stage => {
|
||||
<p id={testStatusId} role="status" className={`flex flex-wrap gap-x-3 gap-y-1 text-xs ${verificationColor(result)}`}>{testing ? '正在调用模型，请稍候' : attempt ? '提交结果未确认，请核查' : selectedStage.id === 'analysis-copy' ? testedStages.map(stage => {
|
||||
const value = model.verification[stage]
|
||||
return <span key={stage} className={value.status === 'passed' ? 'text-[#087052]' : ['failed', 'unknown'].includes(value.status) ? 'text-[#b42318]' : 'text-[#66758b]'}>{stages.find(entry => entry.id === stage)!.shortLabel}：{value.reconciled ? '已核查，可重测' : verificationLabels[value.status]}</span>
|
||||
}) : result.status === 'unknown' ? result.canResume ? '等待上游结果，可查询' : '结果未知，暂停核查' : result.status === 'running' ? '测试进行中，自动更新状态' : result.reconciled ? '已核查，可重测' : blockedReason || verificationLabels[result.status]}</p>
|
||||
{selectedStage.id === 'analysis-copy' && blockedReason && !testing && !attempt && !unsettled && <p className="text-xs text-[#66758b]">{blockedReason}</p>}
|
||||
return <span key={stage} className={verificationColor(value)}>{stages.find(entry => entry.id === stage)!.shortLabel}：{verificationLabel(value)}</span>
|
||||
}) : result.status === 'running' ? '测试进行中，自动更新状态' : verificationLabel(result)}</p>
|
||||
{showBlockedReason && <p id={blockedReasonId} className="text-xs text-[#66758b]">{blockedReason}</p>}
|
||||
{canInspect && <div id={detailsId} hidden={!expandedDetails[detailsId]} className="space-y-1 break-words pb-2 text-xs text-[#66758b]">
|
||||
{protocol && <><p className="break-all">协议：{protocol.id}</p><p className="break-all">接口：{protocol.path}</p>{(protocol.scopeId || protocol.routeId || protocol.operationId) && <p className="break-all">路由：{[protocol.scopeId, protocol.routeId, protocol.operationId].filter(Boolean).join(' / ')}</p>}</>}
|
||||
{testedStages.map(stage => { const value = model.verification[stage]; return <div key={stage} className="space-y-1">{selectedStage.id === 'analysis-copy' && <p className="font-medium text-[#344054]">{stages.find(entry => entry.id === stage)!.label}</p>}{value.message && <p>{value.message}</p>}{value.checkedAt && <p>{new Date(value.checkedAt).toLocaleString('zh-CN')}</p>}{value.requestId && <p className="break-all">请求编号：{value.requestId}</p>}</div> })}
|
||||
{protocol && <>{protocol.maxImages && <p>参考图：最多 {protocol.maxImages} 张</p>}<p className="break-all">协议：{protocol.id}</p><p className="break-all">接口：{protocol.path}</p>{(protocol.scopeId || protocol.routeId || protocol.operationId) && <p className="break-all">路由：{[protocol.scopeId, protocol.routeId, protocol.operationId].filter(Boolean).join(' / ')}</p>}</>}
|
||||
{testedStages.map(stage => { const value = model.verification[stage]; return <div key={stage} className="space-y-1">{selectedStage.id === 'analysis-copy' && <p className="font-medium text-[#344054]">{stages.find(entry => entry.id === stage)!.label}</p>}{value.reconciled && <p>历史请求经人工核查结束，尚需重新测试。</p>}{value.message && <p>{value.message}</p>}{value.checkedAt && <p>{new Date(value.checkedAt).toLocaleString('zh-CN')}</p>}{value.requestId && <p className="break-all">请求编号：{value.requestId}</p>}</div> })}
|
||||
{result.taskId && <p className="break-all">上游任务：{result.taskId}</p>}
|
||||
</div>}
|
||||
{!testing && !attempt && !expandedDetails[detailsId] && ['failed', 'stale', 'unknown'].includes(result.status) && result.message && <p className="break-words text-xs text-[#b42318]">{result.message}</p>}
|
||||
</> : referenceAdaptation ? <>
|
||||
<div className="flex items-center justify-between gap-2"><p role="status" className="text-xs text-[#66758b]">待适配</p><button type="button" className={`${buttonClass} !w-11 !px-0`} aria-label={`${model.id} · ${provider.name} 参考图接入详情`} aria-expanded={!!expandedDetails[detailsId]} aria-controls={detailsId} title="参考图接入详情" onClick={() => setExpandedDetails((current) => ({ ...current, [detailsId]: !current[detailsId] }))}><Info className="h-4 w-4" aria-hidden="true" /></button></div>
|
||||
<div id={detailsId} hidden={!expandedDetails[detailsId]} className="space-y-1 break-words pb-2 text-xs text-[#66758b]"><p>{capabilityReason}</p><p>{model.capabilities.reason}</p></div>
|
||||
</> : <p role="status" className="break-words text-xs text-[#66758b]">{blockedReason}</p>}
|
||||
</div>
|
||||
</div>})}
|
||||
|
||||
@ -27,7 +27,7 @@ test('reload restores lost intake, server-confirmed same submission clears it wi
|
||||
const known = snapshot(); known.providers[0].models[0].verification.analysis = { status: 'passed', id: 'verification', checkedAt: null, requestId: 'upstream', message: null, submissionKey: key }
|
||||
global.fetch = jest.fn(async () => response(known)) as typeof fetch
|
||||
render(<AiModelManagementPanel />)
|
||||
await screen.findByText('已通过')
|
||||
await screen.findByText('已通过', { selector: 'p' })
|
||||
expect(screen.queryByRole('button', { name: intakeName })).not.toBeInTheDocument()
|
||||
expect(screen.getByRole('checkbox', { name: 'vision-model · 平台模型服务 用于资料分析' })).toBeEnabled()
|
||||
expect((global.fetch as jest.Mock).mock.calls.every(([, init]) => !init?.method)).toBe(true)
|
||||
|
||||
@ -22,16 +22,69 @@ test('image understanding and image output require separate interfaces', () => {
|
||||
expect(getAiModelCapabilities(generic, 'editor', metadata(['text', 'image'], ['image'], ['/images/edits']))).toMatchObject({ kind: 'image', allowedStages: ['image'] })
|
||||
})
|
||||
|
||||
test('image models explain missing reference input and unavailable upstream separately', () => {
|
||||
expect(getAiModelCapabilities(generic, 'text-image', metadata(['text'], ['image'], ['/images/generations']))).toMatchObject({ allowedStages: [], reason: '仅支持文生图，不能接收产品参考图' })
|
||||
expect(getAiModelCapabilities(generic, 'reference-image', metadata(['text', 'image'], ['image'], ['/images/generations']))).toMatchObject({ allowedStages: [], reason: '上游尚未提供已适配的参考图接口' })
|
||||
test('image models explain pending adaptation without treating missing metadata as proof of no reference support', () => {
|
||||
expect(getAiModelCapabilities(generic, 'text-image', metadata(['text'], ['image'], ['/images/generations']))).toMatchObject({ allowedStages: [], reason: '参考图待适配：当前目录尚未确认可用的参考图传入方式' })
|
||||
expect(getAiModelCapabilities(generic, 'reference-image', metadata(['text', 'image'], ['image'], ['/images/generations']))).toMatchObject({ allowedStages: [], reason: '参考图待适配：目录已声明图片输入，当前传入方式尚未接入' })
|
||||
const undeclaredReference = metadata(['text'], ['image'], ['/images/generations'])
|
||||
undeclaredReference.inputDeclared = false
|
||||
undeclaredReference.protocols = [{ id: 'openai-image-generate', path: '/v1/images/generations', headers: {}, supportsImageInput: false }]
|
||||
expect(getAiModelCapabilities(generic, 'undeclared-reference', undeclaredReference)).toMatchObject({ allowedStages: [], reason: '上游未提供可用的参考图参数，暂不能接收产品参考图' })
|
||||
expect(getAiModelCapabilities(generic, 'undeclared-reference', undeclaredReference)).toMatchObject({ allowedStages: [], reason: '参考图待适配：当前目录尚未确认可用的参考图传入方式' })
|
||||
const unavailable = { ...metadata(['text', 'image'], ['image'], ['/images/edits']), unavailable: true }
|
||||
expect(getAiModelCapabilities(generic, 'unavailable', unavailable)).toMatchObject({ allowedStages: [], reason: '上游当前不可用，暂不能测试或开放' })
|
||||
})
|
||||
|
||||
test('selected reference route declarations override the default offer summary without claiming a real test passed', () => {
|
||||
const referenceRoute = { id: 'reference-route', input_modes: ['text', 'image_url'], output_modes: ['image'], invocation: {
|
||||
...invocation('/v1/images/edits', { 'X-Relay-Route-Option': 'reference-route' }),
|
||||
input_schema: { properties: { 'image[]': { type: 'array' } } },
|
||||
} }
|
||||
const entry = model({ type: 'image', modality: 'image', capabilities: ['text_to_image', 'image_to_image'], input_modes: ['text'], output_modes: ['text'], service_offers: [
|
||||
{ is_default: true, service_scope: { id: 'scope-default' }, input_modes: ['text'], output_modes: ['text'], route_options: [
|
||||
{ id: 'default-route', invocation: { ...invocation('/v1/images/generations', { 'X-Relay-Route-Option': 'default-route' }), content_type: 'application/json' } },
|
||||
referenceRoute,
|
||||
] },
|
||||
] })
|
||||
const evidence = publicMetadata(entry, 'tool')
|
||||
expect(evidence).toMatchObject({ inputModalities: ['image', 'text'], outputModalities: ['image'], inputDeclared: true, outputDeclared: true })
|
||||
expect(getAiModelCapabilities(base, entry.id, evidence)).toMatchObject({ kind: 'image', allowedStages: ['image'], reason: '接口已适配；通过对应用途的实际测试后可开放', protocols: { image: { routeId: 'reference-route' } } })
|
||||
})
|
||||
|
||||
test('a reference adapter remains testable when the directory summary only lists text input', () => {
|
||||
const entry = model({ type: 'image', modality: 'image', capabilities: ['image_to_image'], input_modes: ['text'], invocation: {
|
||||
...invocation('/v1/images/edits'), input_schema: { properties: { image: { type: 'string', format: 'binary' } } },
|
||||
} })
|
||||
const evidence = publicMetadata(entry, 'tool')
|
||||
expect(evidence).toMatchObject({ inputModalities: ['image', 'text'], outputModalities: ['image'] })
|
||||
expect(getAiModelCapabilities(base, entry.id, evidence)).toMatchObject({ kind: 'image', allowedStages: ['image'] })
|
||||
})
|
||||
|
||||
test('generic URL input does not become image input until a concrete reference adapter is available', () => {
|
||||
const entry = model({ type: 'image', modality: 'image', capabilities: ['text_to_image', 'image_to_image'], input_modes: ['text', 'url'], invocation: {
|
||||
...invocation('/v1/images/generations'), content_type: 'application/json',
|
||||
} })
|
||||
const evidence = publicMetadata(entry, 'tool')
|
||||
expect(evidence.inputModalities).toEqual(['text', 'url'])
|
||||
expect(getAiModelCapabilities(base, entry.id, evidence)).toMatchObject({ allowedStages: [], reason: '参考图待适配：当前目录尚未确认可用的参考图传入方式' })
|
||||
})
|
||||
|
||||
test('an explicit non-image output declaration keeps an image route closed with a precise adaptation reason', () => {
|
||||
const evidence = { ...metadata(['text', 'image'], ['text'], ['/images/edits']), kindHint: 'image' as const }
|
||||
expect(getAiModelCapabilities(base, 'any-image-model', evidence)).toMatchObject({ allowedStages: [], reason: '参考图待适配：当前路线的输出声明与生图要求不一致' })
|
||||
})
|
||||
test('a standard reference probe is testable without claiming a declared or already verified reference contract', () => {
|
||||
const evidence = { ...metadata(['text'], ['image'], ['/images/generations']), protocols: [{
|
||||
id: 'openai-image-generate', path: '/v1/images/generations', headers: { 'X-Relay-Route-Option': 'candidate-route' }, supportsImageInput: true,
|
||||
inputField: 'image', referenceTransport: 'json-inline', referenceContract: 'probe-required',
|
||||
requestParameters: { n: 1, response_format: 'b64_json' }, maxImages: 1, scopeId: 'candidate-scope', routeId: 'candidate-route',
|
||||
}] }
|
||||
expect(getAiModelCapabilities(base, 'standard-reference-candidate', evidence)).toMatchObject({
|
||||
kind: 'image', allowedStages: ['image'], reason: '参考图采用标准图片参数；通过实际测试后可开放',
|
||||
})
|
||||
expect(getAiModelCapabilities(base, 'unavailable-candidate', { ...evidence, unavailable: true })).toMatchObject({
|
||||
allowedStages: [], reason: '上游当前不可用，暂不能测试或开放',
|
||||
})
|
||||
expect(aiModelCapabilityFingerprint(evidence)).not.toBe(aiModelCapabilityFingerprint({ ...evidence, protocols: evidence.protocols.map(({ referenceContract: _candidate, ...protocol }) => protocol) }))
|
||||
})
|
||||
test('audio transcription is not a copy-writing model just because it returns text over chat', () => {
|
||||
expect(getAiModelCapabilities(generic, 'asr', metadata(['audio'], ['text'], ['/chat/completions']))).toMatchObject({ kind: 'asr', allowedStages: [] })
|
||||
})
|
||||
|
||||
@ -71,6 +71,230 @@ test('working quality survives storage and reaches the selected image route with
|
||||
expect(validateStoredAiModelProtocols([{ ...chat, imageQuality: 'medium' }])).toEqual([])
|
||||
})
|
||||
|
||||
const fileRouteModel = (schema: any = {}, example: any = {}) => ({
|
||||
type: 'image', modality: 'image', capabilities: ['text_to_image', 'openai.images.generations'],
|
||||
default_service_scope_id: 'scope-one', default_route_option_id: 'text-default',
|
||||
service_offers: [{ service_scope: { id: 'scope-one' }, public_operation_id: 'openai.images.generations.create', route_options: [
|
||||
{ id: 'text-default', input_modes: ['text'], invocation: invocation('/v1/images/generations', 'text-default') },
|
||||
{ id: 'file-route', input_modes: ['file', 'image_url', 'text'], invocation: {
|
||||
...invocation('/v1/images/generations', 'file-route', { ...schema, properties: {
|
||||
image: { type: 'array', items: { type: 'string', pattern: '^https://' }, anyOf: [{ type: 'string' }, { type: 'array', maxItems: 4 }] },
|
||||
n: { const: 1 }, response_format: { enum: ['url', 'b64_json'] }, ...schema.properties,
|
||||
} }), content_type: 'multipart/form-data', request_example: example,
|
||||
} },
|
||||
] }],
|
||||
})
|
||||
|
||||
test('chooses explicit file reference transport in the default scope and preserves a fixed 4K request', () => {
|
||||
const model = fileRouteModel({ properties: { size: { enum: ['4096x4096'] }, stream: { enum: [false] } } }, { size: '4096x4096', stream: false })
|
||||
model.service_offers.unshift({ service_scope: { id: 'other-scope' }, public_operation_id: 'openai.images.generations.create', route_options: [
|
||||
{ id: 'wrong-scope', input_modes: ['file', 'image_url'], invocation: invocation('/v1/images/generations', 'wrong-scope', { properties: { image: { type: 'array' } } }) } as any,
|
||||
] })
|
||||
const parsed = parseAiModelProtocols(model)
|
||||
expect(parsed).toEqual([{
|
||||
id: 'openai-image-generate', path: '/v1/images/generations', headers: { 'X-Relay-Route-Option': 'file-route' },
|
||||
supportsImageInput: true, inputField: 'image', maxImages: 4, scopeId: 'scope-one', routeId: 'file-route', operationId: 'openai.images.generations.create',
|
||||
referenceTransport: 'multipart', requestParameters: { n: 1, response_format: 'b64_json', size: '4096x4096', stream: false },
|
||||
}])
|
||||
expect(validateStoredAiModelProtocols(JSON.parse(JSON.stringify(parsed)))).toEqual(parsed)
|
||||
})
|
||||
|
||||
test('published JSON reference route is preferred without changing service scope', () => {
|
||||
const model = fileRouteModel()
|
||||
Object.assign(model.service_offers[0].route_options[0], { input_modes: ['image_url', 'text'], invocation: invocation('/v1/images/generations', 'text-default', { properties: { image: { type: 'array', items: { type: 'string' } } } }) })
|
||||
model.capabilities.push('image_to_image')
|
||||
expect(parseAiModelProtocols(model)[0]).toMatchObject({ routeId: 'text-default', referenceTransport: 'json-inline' })
|
||||
})
|
||||
|
||||
test('file mode does not override the JSON invocation contract or require a combined image_to_image tag', async () => {
|
||||
const model = fileRouteModel({ properties: {
|
||||
image: { type: 'array', items: { type: 'string', format: 'uri' }, maxItems: 2 },
|
||||
size: { const: '4096x4096' }, version: { const: 'flare' },
|
||||
} })
|
||||
model.service_offers[0].route_options[1].invocation.content_type = 'application/json'
|
||||
const protocol = parseAiModelProtocols(model)[0]
|
||||
expect(protocol).toMatchObject({ referenceTransport: 'json-inline', inputField: 'image', maxImages: 2, requestParameters: { size: '4096x4096', version: 'flare' }, routeId: 'file-route', scopeId: 'scope-one' })
|
||||
expect(validateStoredAiModelProtocols(JSON.parse(JSON.stringify([protocol])))).toEqual([protocol])
|
||||
send.mockResolvedValue(Response.json({ data: [{ b64_json: png.toString('base64') }] }))
|
||||
await callAiModelProtocol({ ...base, protocol, images: [{ buffer: png, mimeType: 'image/png' }] })
|
||||
expect(send).toHaveBeenCalledTimes(1)
|
||||
expect(typeof send.mock.calls[0][3]).toBe('string')
|
||||
expect(JSON.parse(send.mock.calls[0][3])).toMatchObject({ image: [expect.stringMatching(/^data:image\/png;base64,/)], size: '4096x4096', version: 'flare', n: 1, response_format: 'b64_json' })
|
||||
expect(send.mock.calls[0][5]).toEqual({ 'X-Relay-Route-Option': 'file-route' })
|
||||
})
|
||||
|
||||
test('JSON-only routes reject HTTPS-only or conflicting reference item constraints before any invocation', () => {
|
||||
for (const schema of [
|
||||
{ properties: { image: { type: 'array', items: { type: 'string', pattern: '^https://' } } } },
|
||||
{ properties: { image: { type: 'array', items: { type: 'string' } } }, allOf: [{ properties: { image: { type: 'array', items: { type: 'string', pattern: '^https://' } } } }] },
|
||||
{ properties: { image: { type: 'array', items: { type: 'string', format: 'url' } } } },
|
||||
]) {
|
||||
const model = fileRouteModel(schema)
|
||||
model.service_offers[0].route_options[1].invocation.content_type = 'application/json'
|
||||
expect(parseAiModelProtocols(model)).toMatchObject([{ routeId: 'text-default', supportsImageInput: false }])
|
||||
}
|
||||
})
|
||||
|
||||
test('JSON file_urls without a declared image input mode does not become a reference adapter', () => {
|
||||
const model = fileRouteModel({ properties: { image: false, file_urls: { type: 'array', items: { type: 'string', pattern: '^(https://|data:image/)' } } } })
|
||||
const route = model.service_offers[0].route_options[1]
|
||||
route.invocation.content_type = 'application/json'
|
||||
route.input_modes = ['file', 'text']
|
||||
expect(parseAiModelProtocols(model)).toMatchObject([{ routeId: 'text-default', supportsImageInput: false }])
|
||||
})
|
||||
|
||||
test('a JSON reference rejection does not retry with multipart or another route', async () => {
|
||||
const model = fileRouteModel({ properties: { image: { type: 'array', items: { type: 'string' } } } })
|
||||
model.service_offers[0].route_options[1].invocation.content_type = 'application/json'
|
||||
send.mockResolvedValue(Response.json({ error: { code: 'invalid_request' } }, { status: 400, headers: { 'x-request-id': 'json-rejected' } }))
|
||||
await expect(callAiModelProtocol({ ...base, protocol: parseAiModelProtocols(model)[0], images: [{ buffer: png, mimeType: 'image/png' }] })).rejects.toMatchObject({ outcomeUnknown: false, requestId: 'json-rejected' })
|
||||
expect(send).toHaveBeenCalledTimes(1)
|
||||
expect(typeof send.mock.calls[0][3]).toBe('string')
|
||||
expect(send.mock.calls[0][5]).toEqual({ 'X-Relay-Route-Option': 'file-route' })
|
||||
})
|
||||
|
||||
test('multipart generations sends binary images and the exact declared size, not private data URIs', async () => {
|
||||
const protocol = parseAiModelProtocols(fileRouteModel({ properties: { size: { enum: ['4096x4096'] }, version: { const: 'sunburst' }, response_format: { const: 'url' }, background: { enum: ['opaque'] } } }, { background: 'opaque' }))[0]
|
||||
const generated = await sharp({ create: { width: 4096, height: 4096, channels: 3, background: '#00ff00' } }).png().toBuffer()
|
||||
send.mockResolvedValue(Response.json({ data: [{ b64_json: generated.toString('base64') }] }))
|
||||
const result = await callAiModelProtocol({ ...base, protocol, images: [{ buffer: png, mimeType: 'image/png' }, { buffer: png, mimeType: 'image/png' }] })
|
||||
expect(send).toHaveBeenCalledTimes(1)
|
||||
const body = send.mock.calls[0][3] as FormData
|
||||
expect(body).toBeInstanceOf(FormData)
|
||||
expect(body.get('size')).toBe('4096x4096')
|
||||
expect(body.get('version')).toBe('sunburst')
|
||||
expect(body.get('response_format')).toBe('url')
|
||||
expect(body.get('background')).toBe('opaque')
|
||||
expect(body.get('n')).toBe('1')
|
||||
expect(body.has('file_urls')).toBe(false)
|
||||
expect(body.getAll('image')).toHaveLength(2)
|
||||
for (const file of body.getAll('image')) {
|
||||
expect(file).toBeInstanceOf(Blob)
|
||||
expect((file as File).type).toBe('image/png')
|
||||
expect((await sharp(Buffer.from(await (file as File).arrayBuffer())).metadata()).width).toBe(32)
|
||||
}
|
||||
expect(send.mock.calls[0][5]).toEqual({ 'X-Relay-Route-Option': 'file-route' })
|
||||
expect(result.summary).toMatchObject({ referenceTransport: 'multipart', requestParameters: { size: '4096x4096' }, generatedWidth: 4096, generatedHeight: 4096, storedWidth: 1440, storedHeight: 1440 })
|
||||
})
|
||||
|
||||
test('selects the smallest declared generation size and honors allOf fixed values', () => {
|
||||
const protocol = parseAiModelProtocols(fileRouteModel({
|
||||
allOf: [{ properties: { version: { const: 'flare' }, quality: { enum: ['auto'] } } }],
|
||||
properties: { size: { enum: ['4096x4096', '2048x2048', '1024x1024'] }, version: { type: 'string' }, quality: { enum: ['auto', 'high'] } },
|
||||
}, { size: '4096x4096', version: 'sunburst', quality: 'high' }))[0]
|
||||
expect(protocol.requestParameters).toMatchObject({ size: '1024x1024', version: 'flare', quality: 'auto' })
|
||||
})
|
||||
|
||||
test.each([
|
||||
{ properties: { n: { const: 2 } } },
|
||||
{ properties: { n: false } },
|
||||
{ properties: { n: { type: 'integer', not: { const: 1 } } } },
|
||||
{ properties: { n: { oneOf: [{ const: 1 }, { type: 'integer' }] } } },
|
||||
{ not: { required: ['image'] } },
|
||||
{ oneOf: [{ required: ['image'] }, { required: ['model'] }] },
|
||||
{ properties: { size: { enum: ['8192x8192'] } } },
|
||||
{ required: ['callback_url'], properties: { callback_url: { type: 'string' } } },
|
||||
{ allOf: [{ properties: { version: { const: 'a' } } }], properties: { version: { const: 'b' } } },
|
||||
])('does not offer a reference route whose mandatory request constraints cannot be satisfied: %j', schema => {
|
||||
expect(parseAiModelProtocols(fileRouteModel(schema))).toMatchObject([{ routeId: 'text-default', supportsImageInput: false }])
|
||||
})
|
||||
|
||||
test.each([
|
||||
{ type: 'array', items: { type: 'string', not: { pattern: '^data:' } } },
|
||||
{ type: 'array', maxItems: 0, items: { type: 'string' } },
|
||||
{ type: 'array', items: { type: 'string' }, not: { minItems: 1 } },
|
||||
])('explicit negative array and item constraints never admit inline references: %j', image => {
|
||||
const model = fileRouteModel({ properties: { image } })
|
||||
model.service_offers[0].route_options[1].invocation.content_type = 'application/json'
|
||||
expect(parseAiModelProtocols(model)).toMatchObject([{ routeId: 'text-default', supportsImageInput: false }])
|
||||
})
|
||||
|
||||
test('file mode alone and undeclared reference fields remain awaiting adaptation', () => {
|
||||
const model = fileRouteModel({ properties: { image: false } })
|
||||
expect(parseAiModelProtocols(model)).toMatchObject([{ routeId: 'text-default', supportsImageInput: false }])
|
||||
model.service_offers[0].route_options[1].input_modes = ['image_url', 'text']
|
||||
expect(parseAiModelProtocols(model)).toMatchObject([{ supportsImageInput: false }])
|
||||
})
|
||||
|
||||
test('stored multipart request validation rejects arbitrary fields, type changes and impossible transport combinations', () => {
|
||||
const protocol = parseAiModelProtocols(fileRouteModel())[0]
|
||||
for (const requestParameters of [{ ...protocol.requestParameters, callback_url: 'https://example.com' }, { ...protocol.requestParameters, n: 2 }, { ...protocol.requestParameters, size: '8192x8192' }, { ...protocol.requestParameters, response_format: true }]) {
|
||||
expect(validateStoredAiModelProtocols([{ ...protocol, requestParameters }])).toEqual([])
|
||||
}
|
||||
expect(validateStoredAiModelProtocols([{ ...protocol, referenceTransport: 'json' }])).toEqual([])
|
||||
expect(validateStoredAiModelProtocols([{ ...protocol, inputField: 'file_urls' }])).toEqual([])
|
||||
expect(validateStoredAiModelProtocols([{ ...chat, referenceTransport: 'multipart', requestParameters: protocol.requestParameters }])).toEqual([])
|
||||
expect(validateStoredAiModelProtocols([{ ...protocol, requestParameters: undefined }])).toEqual([])
|
||||
})
|
||||
|
||||
const probeModel = (schema: any = { additionalProperties: true, properties: {} }, supported_fields: any[] = []) => ({
|
||||
type: 'image', modality: 'image', capabilities: ['openai.images.generations', 'text_to_image'],
|
||||
default_service_scope_id: 'scope-probe', default_route_option_id: 'route-probe',
|
||||
service_offers: [{ service_scope: { id: 'scope-probe' }, public_operation_id: 'openai.images.generations.create', route_options: [{
|
||||
id: 'route-probe', input_modes: ['text'], invocation: { ...invocation('/v1/images/generations', 'route-probe', schema), supported_fields },
|
||||
}] }],
|
||||
})
|
||||
|
||||
test('an explicitly open standard generations schema permits one bounded reference test with a persistent candidate marker', async () => {
|
||||
const protocol = parseAiModelProtocols(probeModel())[0]
|
||||
expect(protocol).toMatchObject({ id: 'openai-image-generate', supportsImageInput: true, inputField: 'image', referenceTransport: 'json-inline', referenceContract: 'probe-required', maxImages: 1, routeId: 'route-probe', scopeId: 'scope-probe', requestParameters: { n: 1, response_format: 'b64_json' } })
|
||||
expect(validateStoredAiModelProtocols(JSON.parse(JSON.stringify([protocol])))).toEqual([protocol])
|
||||
send.mockResolvedValue(Response.json({ data: [{ b64_json: png.toString('base64') }] }))
|
||||
const result = await callAiModelProtocol({ ...base, protocol, images: [{ buffer: png, mimeType: 'image/png' }] })
|
||||
expect(send).toHaveBeenCalledTimes(1)
|
||||
expect(JSON.parse(send.mock.calls[0][3])).toMatchObject({ image: [expect.stringMatching(/^data:image\/png;base64,/)] })
|
||||
expect(result.summary).toMatchObject({ referenceContract: 'probe-required', referenceTransport: 'json-inline', routeId: 'route-probe' })
|
||||
})
|
||||
|
||||
test.each([
|
||||
{},
|
||||
{ additionalProperties: false },
|
||||
{ additionalProperties: true, properties: { image: false } },
|
||||
{ additionalProperties: true, properties: { image: { type: 'array', items: { type: 'string', pattern: '^https://' } } } },
|
||||
{ additionalProperties: true, properties: { file_urls: { type: 'array', items: { type: 'string' } } } },
|
||||
{ additionalProperties: true, allOf: [{ additionalProperties: false }] },
|
||||
{ additionalProperties: true, allOf: [{ properties: { image: false } }] },
|
||||
{ additionalProperties: true, patternProperties: { '^image': false } },
|
||||
{ additionalProperties: true, propertyNames: { enum: ['model', 'prompt'] } },
|
||||
])('a candidate never bypasses an unknown or negative reference constraint: %j', schema => {
|
||||
expect(parseAiModelProtocols(probeModel(schema))).toMatchObject([{ supportsImageInput: false }])
|
||||
})
|
||||
|
||||
test('whole-request negations are rejected before declared or candidate reference mapping', () => {
|
||||
expect(parseAiModelProtocols(probeModel({ additionalProperties: true, not: { required: ['image'] } }))).toEqual([])
|
||||
})
|
||||
|
||||
test('an explicitly disabled reference field and a nondefault route do not become probe candidates', () => {
|
||||
expect(parseAiModelProtocols(probeModel(undefined, [{ name: 'image', state: 'offer_disabled' }]))).toMatchObject([{ supportsImageInput: false }])
|
||||
const model = probeModel()
|
||||
model.default_route_option_id = 'not-the-offered-route'
|
||||
expect(parseAiModelProtocols(model)).toMatchObject([{ supportsImageInput: false }])
|
||||
model.default_service_scope_id = 'different-scope'
|
||||
expect(parseAiModelProtocols(model)).toEqual([])
|
||||
})
|
||||
|
||||
test('deeply nested unknown constraints fail closed rather than disappearing from probe evidence', () => {
|
||||
let schema: any = { properties: { image: false } }
|
||||
for (let index = 0; index < 7; index++) schema = { allOf: [schema] }
|
||||
expect(parseAiModelProtocols(probeModel({ additionalProperties: true, allOf: [schema] }))).toEqual([])
|
||||
})
|
||||
|
||||
test('a declared reference route takes precedence over an open-schema default candidate', () => {
|
||||
const model = fileRouteModel()
|
||||
model.service_offers[0].route_options[0].invocation.input_schema = { additionalProperties: true, properties: {} }
|
||||
expect(parseAiModelProtocols(model)).toMatchObject([{ routeId: 'file-route', referenceTransport: 'multipart' }])
|
||||
expect(parseAiModelProtocols(model)[0].referenceContract).toBeUndefined()
|
||||
})
|
||||
|
||||
test('candidate storage cannot switch transport, endpoint, count or identity, and multi-reference probes never reach the API', async () => {
|
||||
const protocol = parseAiModelProtocols(probeModel())[0]
|
||||
for (const mutation of [{ referenceContract: 'verified' }, { referenceTransport: 'multipart' }, { maxImages: 8 }, { scopeId: undefined }, { routeId: undefined }, { inputField: 'file_urls' }, { id: 'openai-chat', path: '/v1/chat/completions' }]) {
|
||||
expect(validateStoredAiModelProtocols([{ ...protocol, ...mutation }])).toEqual([])
|
||||
}
|
||||
await expect(callAiModelProtocol({ ...base, protocol, images: [{ buffer: png, mimeType: 'image/png' }, { buffer: png, mimeType: 'image/png' }] })).rejects.toMatchObject({ outcomeUnknown: false, message: '当前模型最多可接收 1 张参考图，请减少图片' })
|
||||
expect(send).not.toHaveBeenCalled()
|
||||
})
|
||||
|
||||
test.each(['audio', 'video'])('does not turn %s models into chat or detail generation adapters', modality => {
|
||||
expect(parseAiModelProtocols({ type: modality, modality, invocation: { method: 'POST', path: '/v1/chat/completions', content_type: 'application/json', headers: {} } })).toEqual([])
|
||||
})
|
||||
@ -200,7 +424,7 @@ test('unreadable local image is a definite preflight failure with no paid reques
|
||||
|
||||
test('rejects async submission without checkpoint and preflights image and prompt limits', async () => {
|
||||
await expect(callAiModelProtocol({ ...base, protocol: asyncImage })).rejects.toMatchObject({ outcomeUnknown: false })
|
||||
await expect(callAiModelProtocol({ ...base, protocol: { ...chat, supportsImageInput: false }, images: [{ buffer: png, mimeType: 'image/png' }] })).rejects.toMatchObject({ outcomeUnknown: false })
|
||||
await expect(callAiModelProtocol({ ...base, protocol: { ...chat, supportsImageInput: false }, images: [{ buffer: png, mimeType: 'image/png' }] })).rejects.toMatchObject({ outcomeUnknown: false, message: '参考图传入方式尚未适配，请重新同步模型目录' })
|
||||
await expect(callAiModelProtocol({ ...base, protocol: { ...chat, maxPromptLength: 1 } })).rejects.toMatchObject({ outcomeUnknown: false })
|
||||
expect(send).not.toHaveBeenCalled()
|
||||
})
|
||||
|
||||
@ -215,6 +215,73 @@ test('stale enabled rows are filtered from menus, defaults and old project execu
|
||||
await expect(configureAiModels('admin', { revision: 1, providers: [], defaults: { analysis: null, copy: null, image: legacyModelKey } })).rejects.toMatchObject({ code: 'default_unavailable' })
|
||||
expect(requestAiModel).not.toHaveBeenCalled()
|
||||
})
|
||||
function referenceCandidateMetadata() {
|
||||
return { ...parseAiModelCapabilityMetadata({ input_modalities: ['text'], output_modalities: ['image'], endpoints: ['/images/generations'] })!,
|
||||
protocols: [{ id: 'openai-image-generate', path: '/v1/images/generations', headers: { 'X-Relay-Route-Option': 'candidate-route' },
|
||||
supportsImageInput: true, inputField: 'image', referenceTransport: 'json-inline', referenceContract: 'probe-required',
|
||||
maxImages: 1, scopeId: 'candidate-scope', routeId: 'candidate-route', requestParameters: { n: 1, response_format: 'b64_json' } }],
|
||||
}
|
||||
}
|
||||
|
||||
test.each(['untested', 'old-fingerprint'] as const)('a standard reference candidate stays unavailable and cannot be enabled with %s evidence', async state => {
|
||||
const previous = await listAiModelVerifications()
|
||||
const metadata = referenceCandidateMetadata()
|
||||
models[0].capabilityMetadata = metadata; models[0].stages = ['image']
|
||||
const priorMetadata = { ...metadata, protocols: metadata.protocols.map(({ referenceContract: _probe, ...protocol }) => protocol) }
|
||||
const oldContext = aiModelVerificationContext(providers[0].baseUrl, models[0].id, priorMetadata, 'synthetic-legacy-secret', getAiModelStageProtocol(priorMetadata, 'image')!)
|
||||
const currentContext = aiModelVerificationContext(providers[0].baseUrl, models[0].id, metadata, 'synthetic-legacy-secret', getAiModelStageProtocol(metadata, 'image')!)
|
||||
expect(oldContext).not.toBe(currentContext)
|
||||
const oldPassed = { ...previous.find(value => value.modelKey === legacyModelKey && value.stage === 'image')!, contextHash: oldContext }
|
||||
;(listAiModelVerifications as jest.Mock).mockResolvedValue([
|
||||
...previous.filter(value => value.modelKey !== legacyModelKey), ...(state === 'old-fingerprint' ? [oldPassed] : []),
|
||||
])
|
||||
const admin = (await getAiModelAdminState()).providers[0].models[0]
|
||||
expect(admin.capabilities.allowedStages).toEqual(['image']) // Testing admission is separate from runtime permission.
|
||||
expect(admin.verification.image.status).toBe(state === 'untested' ? 'untested' : 'stale')
|
||||
const runtime = await getAiModelRuntimeState()
|
||||
expect(runtime.models.image).not.toContain(legacyModelKey)
|
||||
expect(runtime.modelOptions.image.some(option => option.value === legacyModelKey)).toBe(false)
|
||||
expect(runtime.defaults.image).toBe('')
|
||||
await expect(resolveAiModel('image', legacyModelKey)).rejects.toMatchObject({ code: 'model_unavailable' })
|
||||
await expect(resolveAiModel('image', models[0].id)).rejects.toMatchObject({ code: 'model_unavailable' })
|
||||
models[0].enabled = false; models[0].stages = []
|
||||
await expect(configureAiModels('admin', { revision: 1, defaults: { analysis: null, copy: null, image: null },
|
||||
providers: [{ id: legacyId, revision: 1, models: [{ key: legacyModelKey, enabled: true, stages: ['image'] }] }],
|
||||
})).rejects.toMatchObject({ code: 'model_verification_required' })
|
||||
expect(mockDb.$executeRawUnsafe).not.toHaveBeenCalled()
|
||||
expect(saveAiModelDefaults).not.toHaveBeenCalled()
|
||||
expect(requestAiModel).not.toHaveBeenCalled()
|
||||
})
|
||||
|
||||
test('only current passed reference-candidate evidence permits explicit enablement and image selection', async () => {
|
||||
const previous = await listAiModelVerifications()
|
||||
const metadata = referenceCandidateMetadata()
|
||||
models[0].capabilityMetadata = metadata; models[0].enabled = false; models[0].stages = []
|
||||
const contextHash = aiModelVerificationContext(providers[0].baseUrl, models[0].id, metadata, 'synthetic-legacy-secret', getAiModelStageProtocol(metadata, 'image')!)
|
||||
const passed = { ...previous.find(value => value.modelKey === legacyModelKey && value.stage === 'image')!, id: 'candidate-current-pass', contextHash,
|
||||
evidence: { ruleVersion: 2, referenceImageAccepted: true, referenceLayoutMatched: true, imageDecoded: true },
|
||||
}
|
||||
;(listAiModelVerifications as jest.Mock).mockResolvedValue([...previous.filter(value => value.modelKey !== legacyModelKey), passed])
|
||||
expect((await getAiModelAdminState()).providers[0].models[0].verification.image.status).toBe('passed')
|
||||
expect((await getAiModelRuntimeState()).models.image).not.toContain(legacyModelKey)
|
||||
await expect(resolveAiModel('image', legacyModelKey)).rejects.toMatchObject({ code: 'model_unavailable' })
|
||||
await configureAiModels('admin', { revision: 1, defaults: { analysis: null, copy: null, image: null },
|
||||
providers: [{ id: legacyId, revision: 1, models: [{ key: legacyModelKey, enabled: true, stages: ['image'] }] }],
|
||||
})
|
||||
expect(mockDb.$executeRawUnsafe).toHaveBeenCalledWith(expect.stringContaining('UPDATE ai_model_catalog SET enabled=$2'), legacyModelKey, true, ['image'])
|
||||
// Reflect the asserted database update in this repository mock for the subsequent read.
|
||||
models[0].enabled = true; models[0].stages = ['image']
|
||||
const runtime = await getAiModelRuntimeState()
|
||||
expect(runtime.models.image).toContain(legacyModelKey)
|
||||
expect(runtime.modelOptions.image).toContainEqual(expect.objectContaining({ value: legacyModelKey }))
|
||||
expect(runtime.defaults.image).toBe(legacyModelKey)
|
||||
await expect(resolveAiModel('image', legacyModelKey)).resolves.toMatchObject({
|
||||
protocol: { referenceContract: 'probe-required', referenceTransport: 'json-inline' },
|
||||
binding: { modelKey: legacyModelKey, verificationId: passed.id },
|
||||
})
|
||||
expect(requestAiModel).not.toHaveBeenCalled()
|
||||
})
|
||||
|
||||
test('admin read removes invalid old stages without writes or raw capability metadata exposure', async () => {
|
||||
models[0].capabilityMetadata = null
|
||||
const state = await getAiModelAdminState()
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
import { randomUUID } from 'node:crypto'
|
||||
import { AiModelError, assertAiModelAdmin } from '@/lib/ai-model-access'
|
||||
import { verifyAiModel, refreshAiModelVerification, validateAiModelVerificationOutput } from '@/lib/ai-model-verification'
|
||||
import { validateAiModelCombinedVerificationOutput } from '@/lib/ai-model-verification-output'
|
||||
import { validateAiModelCombinedVerificationOutput, verificationReferenceImage } from '@/lib/ai-model-verification-output'
|
||||
import { callAiModelProtocol, AiModelProtocolError } from '@/lib/ai-model-protocol'
|
||||
import { resolveAiModelVerification } from '@/lib/ai-model-service'
|
||||
import { prisma } from '@/lib/prisma'
|
||||
@ -51,6 +51,83 @@ test('a real image-understanding response persists passed evidence without enabl
|
||||
expect(mockDb.$executeRawUnsafe.mock.calls.some(([sql]) => /UPDATE ai_model_catalog|ai_model_configuration/.test(sql))).toBe(false)
|
||||
expect(detailTransactionLock).toHaveBeenCalledWith(mockDb, 'detail-generator-execution')
|
||||
})
|
||||
test('reference-image acceptance requests an unchanged flat redraw without leaking the random color answers', async () => {
|
||||
;(callAiModelProtocol as jest.Mock).mockImplementation(async request => {
|
||||
await request.beforeRequest()
|
||||
return { image: await verificationReferenceImage(row.evidence.challenge), requestId: 'flat-reference-check', summary: {} }
|
||||
})
|
||||
await verifyAiModel('admin', providerId, modelKey, { ...input(), stage: 'image' })
|
||||
const request = (callAiModelProtocol as jest.Mock).mock.calls[0][0]
|
||||
expect(request.prompt).toBe('将参考图重新绘制为干净的平面图，严格保留两个方块各自的原始颜色、形状和相对位置，纯白背景；不要添加文字、标志、阴影、纹理、渐变、光照或其它内容。')
|
||||
expect(request.prompt).not.toMatch(/red|blue|green|yellow|红色|蓝色|绿色|黄色/i)
|
||||
expect(request.prompt).not.toContain(row.evidence.challenge.topLeft)
|
||||
expect(request.prompt).not.toContain(row.evidence.challenge.bottomRight)
|
||||
expect(request.images).toHaveLength(1)
|
||||
expect(request.images[0].buffer).toEqual(await verificationReferenceImage(row.evidence.challenge))
|
||||
expect(callAiModelProtocol).toHaveBeenCalledTimes(1)
|
||||
expect(mockDb.$executeRawUnsafe.mock.calls.some(([sql]) => sql.includes("status='passed'"))).toBe(true)
|
||||
})
|
||||
test.each(['multipart', 'json-inline'])('image acceptance preserves the bounded %s request and original versus stored dimensions', async referenceTransport => {
|
||||
const parameters = { n: 1, response_format: 'b64_json', size: '4096x4096', quality: 'high' }
|
||||
const context = await (resolveAiModelVerification as jest.Mock)()
|
||||
;(resolveAiModelVerification as jest.Mock).mockResolvedValue({ ...context, protocol: { id: 'openai-image-generate', path: '/v1/images/generations', headers: {}, supportsImageInput: true, inputField: 'image', referenceTransport, requestParameters: parameters } })
|
||||
;(callAiModelProtocol as jest.Mock).mockImplementation(async request => {
|
||||
await request.beforeRequest()
|
||||
const image = await sharp(await verificationReferenceImage(row.evidence.challenge)).resize(1440, 1440).png().toBuffer()
|
||||
return { image, requestId: 'actual-4k-request', summary: { referenceTransport, requestParameters: parameters,
|
||||
generatedWidth: 4096, generatedHeight: 4096, storedWidth: 1440, storedHeight: 1440,
|
||||
referenceImageAccepted: false, referenceLayoutMatched: false, imageDecoded: false, width: 9999,
|
||||
ruleVersion: -1, adapterVersion: -1, prompt: 'must not persist', secret: 'synthetic-test-secret' } }
|
||||
})
|
||||
await verifyAiModel('admin', providerId, modelKey, { ...input(), stage: 'image' })
|
||||
const passed = mockDb.$executeRawUnsafe.mock.calls.find(([sql]) => sql.includes("status='passed'"))!
|
||||
expect(passed[2]).toBe('actual-4k-request')
|
||||
const evidence = JSON.parse(passed[3])
|
||||
expect(evidence).toMatchObject({ referenceTransport, requestParameters: parameters,
|
||||
generatedWidth: 4096, generatedHeight: 4096, storedWidth: 1440, storedHeight: 1440, width: 1440, height: 1440,
|
||||
referenceImageAccepted: true, referenceLayoutMatched: true, imageDecoded: true })
|
||||
expect(evidence.ruleVersion).toBeGreaterThan(0); expect(evidence.adapterVersion).toBeGreaterThan(0)
|
||||
expect(evidence).not.toHaveProperty('prompt'); expect(evidence).not.toHaveProperty('secret')
|
||||
expect(passed[3]).not.toContain('synthetic-test-secret')
|
||||
expect((aiModelAudit as jest.Mock).mock.calls.find(([, , action]) => action === 'model.verification-passed')?.[4].evidence).toMatchObject({ generatedWidth: 4096, storedWidth: 1440 })
|
||||
})
|
||||
test('an unexpected reference transport cannot replace the locked native route evidence', async () => {
|
||||
const context = await (resolveAiModelVerification as jest.Mock)()
|
||||
const parameters = { n: 1, response_format: 'b64_json' }
|
||||
;(resolveAiModelVerification as jest.Mock).mockResolvedValue({ ...context, protocol: { id: 'openai-image-generate', path: '/v1/images/generations', headers: {}, supportsImageInput: true, inputField: 'image', referenceTransport: 'json-inline', requestParameters: parameters } })
|
||||
;(callAiModelProtocol as jest.Mock).mockImplementation(async request => {
|
||||
await request.beforeRequest()
|
||||
return { image: await verificationReferenceImage(row.evidence.challenge), requestId: 'safe-result', summary: { referenceTransport: 'multipart', requestParameters: parameters } }
|
||||
})
|
||||
await verifyAiModel('admin', providerId, modelKey, { ...input(), stage: 'image' })
|
||||
const evidence = JSON.parse(mockDb.$executeRawUnsafe.mock.calls.find(([sql]) => sql.includes("status='passed'"))![3])
|
||||
expect(evidence.referenceLayoutMatched).toBe(true)
|
||||
expect(evidence).not.toHaveProperty('referenceTransport')
|
||||
expect(evidence).not.toHaveProperty('requestParameters')
|
||||
})
|
||||
test.each([
|
||||
{ n: 1, response_format: 'b64_json', callback_url: 'https://example.com/private' },
|
||||
{ n: 2, response_format: 'b64_json', size: '4096x4096' },
|
||||
{ n: 1, response_format: 'b64_json', size: '8192x8192' },
|
||||
{ n: 1, response_format: 'b64_json', size: '2048x2048' },
|
||||
])('adapter evidence rejects unknown, unbounded or changed request parameters: %j', async requestParameters => {
|
||||
const context = await (resolveAiModelVerification as jest.Mock)()
|
||||
;(resolveAiModelVerification as jest.Mock).mockResolvedValue({ ...context, protocol: { id: 'openai-image-generate', path: '/v1/images/generations', headers: {}, supportsImageInput: true, inputField: 'image', referenceTransport: 'multipart', requestParameters: { n: 1, response_format: 'b64_json', size: '4096x4096' } } })
|
||||
;(callAiModelProtocol as jest.Mock).mockImplementation(async request => {
|
||||
await request.beforeRequest()
|
||||
return { image: await verificationReferenceImage(row.evidence.challenge), requestId: 'safe-result', summary: {
|
||||
referenceTransport: 'multipart', requestParameters, generatedWidth: Infinity, generatedHeight: 1,
|
||||
storedWidth: 512, storedHeight: -1, referenceLayoutMatched: false,
|
||||
} }
|
||||
})
|
||||
await verifyAiModel('admin', providerId, modelKey, { ...input(), stage: 'image' })
|
||||
const evidence = JSON.parse(mockDb.$executeRawUnsafe.mock.calls.find(([sql]) => sql.includes("status='passed'"))![3])
|
||||
expect(evidence.referenceLayoutMatched).toBe(true)
|
||||
expect(evidence.referenceTransport).toBe('multipart')
|
||||
expect(evidence).not.toHaveProperty('requestParameters')
|
||||
expect(evidence).not.toHaveProperty('generatedWidth'); expect(evidence).not.toHaveProperty('generatedHeight')
|
||||
expect(evidence).not.toHaveProperty('storedWidth'); expect(evidence).not.toHaveProperty('storedHeight')
|
||||
})
|
||||
test('repeated idempotency keys and unresolved requests cannot submit another model call', async () => {
|
||||
duplicate = true
|
||||
await verifyAiModel('admin', providerId, modelKey, input())
|
||||
@ -165,11 +242,19 @@ test('a closed verification stops dispatch and a late response cannot append a f
|
||||
expect((aiModelAudit as jest.Mock).mock.calls.some(([, , action]) => action === 'model.verification-passed')).toBe(false)
|
||||
})
|
||||
test('a terminal decoded image that fails reference validation is failed, not an endless unknown task', async () => {
|
||||
const image = await sharp({ create: { width: 128, height: 128, channels: 3, background: '#ffffff' } }).png().toBuffer()
|
||||
;(callAiModelProtocol as jest.Mock).mockImplementation(async request => { await request.beforeRequest(); await request.onTaskSubmitted('task-ended'); return { image, requestId: 'known-result', summary: {} } })
|
||||
;(callAiModelProtocol as jest.Mock).mockImplementation(async request => {
|
||||
await request.beforeRequest(); await request.onTaskSubmitted('task-ended')
|
||||
const challenge = row.evidence.challenge
|
||||
const image = await verificationReferenceImage({ topLeft: challenge.bottomRight, bottomRight: challenge.topLeft })
|
||||
return { image, requestId: 'known-result', summary: {} }
|
||||
})
|
||||
row.stage = 'image'
|
||||
await verifyAiModel('admin', providerId, modelKey, { ...input(), stage: 'image' })
|
||||
expect(mockDb.$executeRawUnsafe.mock.calls.find(([sql]) => sql.includes('status=$2'))?.[2]).toBe('failed')
|
||||
const failed = mockDb.$executeRawUnsafe.mock.calls.find(([sql]) => sql.includes('status=$2'))!
|
||||
expect(failed[2]).toBe('failed')
|
||||
expect(failed[3]).toBe('known-result')
|
||||
expect(failed[4]).toBe('task-ended')
|
||||
expect(failed[5]).toContain('颜色与相对位置')
|
||||
expect(callAiModelProtocol).toHaveBeenCalledTimes(1)
|
||||
})
|
||||
|
||||
|
||||
@ -71,17 +71,28 @@ export function parseAiModelPublicCapabilityCatalog(value: unknown): Map<string,
|
||||
if (!model || typeof model.id !== 'string' || !model.id || model.id.length > 200 || result.has(model.id)) throw new AiModelError('上游能力目录模型编号无效或重复', 502, 'capability_catalog_invalid')
|
||||
const offers = Array.isArray(model.service_offers) ? model.service_offers.map(object).filter((offer): offer is Record<string, unknown> => Boolean(offer)) : []
|
||||
// Do not combine non-default service offers or route-specific protocols into the default request path.
|
||||
const selected = offers.find(offer => offer.is_default === true && object(offer.service_scope)?.id === model.default_service_scope_id)
|
||||
const rawInputModes = selected?.input_modes ?? model.input_modes, rawOutputModes = selected?.output_modes ?? model.output_modes
|
||||
const protocols = parseAiModelProtocols(model)
|
||||
const selectedProtocol = protocols[0]
|
||||
const selected = offers.find(offer => object(offer.service_scope)?.id === selectedProtocol?.scopeId &&
|
||||
Array.isArray(offer.route_options) && offer.route_options.some(route => object(route)?.id === selectedProtocol?.routeId)) ||
|
||||
offers.find(offer => offer.is_default === true && object(offer.service_scope)?.id === model.default_service_scope_id)
|
||||
const selectedRoute = selectedProtocol?.routeId && Array.isArray(selected?.route_options)
|
||||
? selected.route_options.map(object).find(route => route?.id === selectedProtocol.routeId) : null
|
||||
// The adapter selects a concrete route. Its declarations take precedence
|
||||
// over the model/offer summary, which can describe a different route.
|
||||
const rawInputModes = selectedRoute?.input_modes ?? selected?.input_modes ?? model.input_modes
|
||||
const rawOutputModes = selectedRoute?.output_modes ?? selected?.output_modes ?? model.output_modes
|
||||
const inputModes = strings(rawInputModes), outputModes = strings(rawOutputModes)
|
||||
if (rawInputModes !== undefined && inputModes === null || rawOutputModes !== undefined && outputModes === null) throw new AiModelError('上游能力目录模态字段无效', 502, 'capability_catalog_invalid')
|
||||
const textModel = model.type === 'model' && ['text', 'multimodal'].includes(String(model.modality)) && section.id === 'chat'
|
||||
const declaredCapabilities = strings(model.capabilities) || []
|
||||
const imageModel = (model.type === 'image' || model.modality === 'image') && declaredCapabilities.some(capability => ['text_to_image', 'openai.images.generations', 'openai.images.edits'].includes(capability))
|
||||
const imageModel = (model.type === 'image' || model.modality === 'image') && declaredCapabilities.some(capability => ['text_to_image', 'image_to_image', 'openai.images.generations', 'openai.images.edits'].includes(capability))
|
||||
const normalizeModes = (modes: string[]) => Array.from(new Set(modes.map(mode => ['image_url', 'image_base64'].includes(mode) ? 'image' : mode === 'prompt' ? 'text' : mode))).sort()
|
||||
const inputModalities = normalizeModes(inputModes || (textModel ? ['text'] : []))
|
||||
// A generic URL mode alone proves nothing about file type. The parsed
|
||||
// image adapter has already validated the concrete reference parameter.
|
||||
const inputModalities = normalizeModes([...(inputModes || (textModel ? ['text'] : [])),
|
||||
...(imageModel && selectedProtocol?.supportsImageInput ? ['image'] : [])])
|
||||
const outputModalities = normalizeModes(outputModes || (textModel ? ['text'] : imageModel ? ['image'] : []))
|
||||
const protocols = parseAiModelProtocols(model)
|
||||
const endpoints = protocols.map(protocol => protocol.path)
|
||||
const kindHint = publicKind(model, section, inputModes || [], outputModes || [])
|
||||
result.set(model.id, { version: 1, inputModalities, outputModalities, endpoints, protocols, sourceUrl: AI_MODEL_PUBLIC_CATALOG_URL,
|
||||
@ -134,16 +145,18 @@ export function getAiModelCapabilities(baseUrl: string, modelId: string, metadat
|
||||
// the analysis protocol; its explicit input flag may differ on legacy catalogs.
|
||||
const transport = (protocol: AiModelProtocol | null) => protocol && JSON.stringify({ ...protocol, supportsImageInput: undefined })
|
||||
const combinedTextTest = allowedStages.includes('analysis') && allowedStages.includes('copy') && !!analysisProtocol?.supportsImageInput && transport(analysisProtocol) === transport(copyProtocol)
|
||||
const referenceProbe = allowedStages.includes('image') && getAiModelStageProtocol(metadata, 'image')?.referenceContract === 'probe-required'
|
||||
const reason = evidence.unavailable ? '上游当前不可用，暂不能测试或开放'
|
||||
: referenceProbe ? '参考图采用标准图片参数；通过实际测试后可开放'
|
||||
: allowedStages.length ? '接口已适配；通过对应用途的实际测试后可开放'
|
||||
: kind === 'image' ? evidence.inputDeclared && !imageInput && !imageEdit ? '仅支持文生图，不能接收产品参考图'
|
||||
: protocols.some(protocol => protocol.id === 'openai-image-generate') ? '上游未提供可用的参考图参数，暂不能接收产品参考图'
|
||||
: '上游尚未提供已适配的参考图接口'
|
||||
: kind === 'image' ? imageEdit ? '参考图待适配：当前路线的输出声明与生图要求不一致'
|
||||
: imageInput ? '参考图待适配：目录已声明图片输入，当前传入方式尚未接入'
|
||||
: '参考图待适配：当前目录尚未确认可用的参考图传入方式'
|
||||
: ['text', 'vision'].includes(kind) ? '输入输出或接口不符合详情页分析、文案要求'
|
||||
: '该类型不适用于详情页的分析、文案或生图'
|
||||
return { kind, allowedStages, combinedTextTest, source: 'upstream', protocols: Object.fromEntries(allowedStages.map(stage => {
|
||||
const protocol = getAiModelStageProtocol(metadata, stage)!
|
||||
return [stage, { id: protocol.id, path: protocol.path, scopeId: protocol.scopeId, routeId: protocol.routeId, operationId: protocol.operationId }]
|
||||
return [stage, { id: protocol.id, path: protocol.path, scopeId: protocol.scopeId, routeId: protocol.routeId, operationId: protocol.operationId, ...(protocol.maxImages ? { maxImages: protocol.maxImages } : {}) }]
|
||||
})), reason }
|
||||
}
|
||||
|
||||
|
||||
@ -14,6 +14,9 @@ export interface AiModelProtocol {
|
||||
maxImages?: number
|
||||
maxPromptLength?: number
|
||||
imageQuality?: 'medium'
|
||||
referenceTransport?: 'json-inline' | 'multipart'
|
||||
referenceContract?: 'probe-required'
|
||||
requestParameters?: Record<string, string | number | boolean>
|
||||
}
|
||||
const paths: Record<AiModelProtocol['id'], string> = { 'openai-chat': '/v1/chat/completions', 'anthropic-messages': '/v1/messages', 'openai-image-edit': '/v1/images/edits', 'openai-image-generate': '/v1/images/generations', 'async-image-task': '/extensions/v1/tasks' }
|
||||
function record(value: unknown): Record<string, any> | null { return value && typeof value === 'object' && !Array.isArray(value) ? value as Record<string, any> : null }
|
||||
@ -30,6 +33,111 @@ function safeHeaders(value: unknown): Record<string, string> | null {
|
||||
}
|
||||
function identifier(value: unknown): value is string { return typeof value === 'string' && /^[A-Za-z0-9._-]{1,160}$/.test(value) }
|
||||
|
||||
const imageParameterChecks: Record<string, (value: unknown) => boolean> = {
|
||||
n: value => value === 1,
|
||||
stream: value => value === false,
|
||||
response_format: value => typeof value === 'string' && ['url', 'b64_json'].includes(value),
|
||||
size: value => typeof value === 'string' && (value === 'auto' || /^\d{3,4}x\d{3,4}$/.test(value) && value.split('x').every(part => Number(part) >= 256 && Number(part) <= 4096)),
|
||||
quality: value => typeof value === 'string' && ['auto', 'low', 'medium', 'high', 'standard', 'hd', 'xhigh', 'max'].includes(value),
|
||||
background: value => typeof value === 'string' && ['transparent', 'opaque', 'auto'].includes(value),
|
||||
output_format: value => typeof value === 'string' && ['png', 'jpeg', 'webp'].includes(value),
|
||||
aspect_ratio: value => typeof value === 'string' && (value === 'auto' || /^\d{1,2}:\d{1,2}$/.test(value)),
|
||||
version: value => typeof value === 'string' && /^[A-Za-z0-9][A-Za-z0-9._-]{0,63}$/.test(value),
|
||||
thinkingLevel: value => typeof value === 'string' && ['minimal', 'low', 'medium', 'high'].includes(value),
|
||||
}
|
||||
function safeImageParameters(value: unknown): AiModelProtocol['requestParameters'] | null {
|
||||
const raw = record(value)
|
||||
if (!raw || Object.keys(raw).some(key => !Object.prototype.hasOwnProperty.call(imageParameterChecks, key) || !imageParameterChecks[key](raw[key]))) return null
|
||||
return Object.fromEntries(Object.entries(raw).sort(([a], [b]) => a.localeCompare(b)))
|
||||
}
|
||||
function schemaParts(value: unknown, depth = 0): Record<string, any>[] {
|
||||
const raw = record(value)
|
||||
if (!raw || depth > 5) return []
|
||||
return [raw, ...(Array.isArray(raw.allOf) ? raw.allOf.flatMap((item: unknown) => schemaParts(item, depth + 1)) : [])]
|
||||
}
|
||||
function inspectableSchema(value: unknown, depth = 0): boolean {
|
||||
if (depth > 5) return false
|
||||
if (value === undefined || value === true || value === false) return true
|
||||
const raw = record(value)
|
||||
if (!raw || ['if', 'then', 'else', '$ref', '$dynamicRef'].some(key => raw[key] !== undefined)) return false
|
||||
for (const key of ['allOf', 'anyOf', 'oneOf']) {
|
||||
if (raw[key] !== undefined && (!Array.isArray(raw[key]) || !raw[key].length || raw[key].length > 32 || !raw[key].every((item: unknown) => inspectableSchema(item, depth + 1)))) return false
|
||||
}
|
||||
return raw.not === undefined || inspectableSchema(raw.not, depth + 1)
|
||||
}
|
||||
function acceptsSchemaValue(value: unknown, schema: unknown, depth = 0): boolean {
|
||||
if (schema === false || depth > 5 || !inspectableSchema(schema)) return false
|
||||
const raw = record(schema)
|
||||
if (!raw) return true
|
||||
if (raw.const !== undefined && raw.const !== value || Array.isArray(raw.enum) && !raw.enum.includes(value)) return false
|
||||
const valueType = Array.isArray(value) ? 'array' : value === null ? 'null' : typeof value
|
||||
if (raw.type !== undefined && ![raw.type].flat().some(type => type === valueType || type === 'integer' && Number.isInteger(value))) return false
|
||||
if (typeof value === 'number' && (typeof raw.minimum === 'number' && value < raw.minimum || typeof raw.maximum === 'number' && value > raw.maximum)) return false
|
||||
if (typeof value === 'number' && (typeof raw.exclusiveMinimum === 'number' && value <= raw.exclusiveMinimum || typeof raw.exclusiveMaximum === 'number' && value >= raw.exclusiveMaximum || typeof raw.multipleOf === 'number' && (raw.multipleOf <= 0 || value % raw.multipleOf !== 0))) return false
|
||||
if (typeof value === 'string' && (typeof raw.minLength === 'number' && value.length < raw.minLength || typeof raw.maxLength === 'number' && value.length > raw.maxLength)) return false
|
||||
if (Array.isArray(value)) {
|
||||
if (typeof raw.minItems === 'number' && value.length < raw.minItems || typeof raw.maxItems === 'number' && value.length > raw.maxItems || raw.prefixItems !== undefined || raw.contains !== undefined) return false
|
||||
if (raw.items !== undefined && !value.every(item => acceptsSchemaValue(item, raw.items, depth + 1))) return false
|
||||
}
|
||||
if (typeof value === 'string' && typeof raw.pattern === 'string') {
|
||||
if (raw.pattern.length > 512) return false
|
||||
try { if (!new RegExp(raw.pattern).test(value)) return false } catch { return false }
|
||||
}
|
||||
if (Array.isArray(raw.allOf) && !raw.allOf.every(item => acceptsSchemaValue(value, item, depth + 1))) return false
|
||||
if (Array.isArray(raw.anyOf) && !raw.anyOf.some(item => acceptsSchemaValue(value, item, depth + 1))) return false
|
||||
if (Array.isArray(raw.oneOf) && raw.oneOf.filter(item => acceptsSchemaValue(value, item, depth + 1)).length !== 1) return false
|
||||
if (raw.not !== undefined && acceptsSchemaValue(value, raw.not, depth + 1)) return false
|
||||
return true
|
||||
}
|
||||
/** Keep only bounded public request fields; never carry arbitrary metadata or callback URLs. */
|
||||
function imageRequestParameters(schema: Record<string, any> | null, example: unknown): AiModelProtocol['requestParameters'] | null {
|
||||
if (!inspectableSchema(schema)) return null
|
||||
const parts = schemaParts(schema), sample = record(example) ?? {}, parameters: Record<string, string | number | boolean> = {}
|
||||
const names = new Set(parts.flatMap(part => Object.keys(record(part.properties) ?? {})))
|
||||
for (const name of Object.keys(imageParameterChecks)) {
|
||||
const constraints = parts.flatMap(part => Object.prototype.hasOwnProperty.call(record(part.properties) ?? {}, name) ? [part.properties[name]] : [])
|
||||
if (constraints.some(item => item === false)) {
|
||||
if (['n', 'response_format'].includes(name)) return null
|
||||
continue
|
||||
}
|
||||
if (!constraints.length && !['n', 'response_format'].includes(name)) continue
|
||||
if (!constraints.length && parts.some(part => part.additionalProperties === false && !part.properties?.[name])) return null
|
||||
const required = parts.some(part => list(part.required).includes(name)) || ['n', 'response_format'].includes(name)
|
||||
const declared = constraints.flatMap(item => schemaParts(item))
|
||||
const choices = declared.flatMap(item => Array.isArray(item.enum) ? item.enum : [])
|
||||
if (name === 'size') choices.sort((a, b) => {
|
||||
const area = (value: unknown) => typeof value === 'string' && /^\d+x\d+$/.test(value) ? value.split('x').map(Number).reduce((x, y) => x * y) : Number.MAX_SAFE_INTEGER
|
||||
return area(a) - area(b)
|
||||
})
|
||||
const candidates = name === 'n' ? [1] : name === 'response_format' ? ['b64_json', 'url'] : [
|
||||
...declared.filter(item => item.const !== undefined).map(item => item.const),
|
||||
...declared.filter(item => item.default !== undefined).map(item => item.default),
|
||||
...(name === 'size' ? choices : []), sample[name], ...choices,
|
||||
]
|
||||
const chosen = candidates.find(value => imageParameterChecks[name](value) && constraints.every(item => acceptsSchemaValue(value, item)))
|
||||
if (chosen !== undefined) parameters[name] = chosen
|
||||
else if (required || declared.some(item => item.const !== undefined)) return null
|
||||
}
|
||||
const required = parts.flatMap(part => list(part.required))
|
||||
if (required.some(name => !['model', 'prompt', 'image', 'images', 'file_urls'].includes(name) && parameters[name] === undefined)) return null
|
||||
// These aliases describe the same resolution. Use the canonical size from the published route.
|
||||
if (names.has('size') && parameters.size === undefined && parts.some(part => part.properties?.size?.enum || part.properties?.size?.const)) return null
|
||||
return safeImageParameters(parameters)
|
||||
}
|
||||
|
||||
/** An open JSON schema allows a bounded standard-field probe, never an override of a declared restriction. */
|
||||
function canProbeImageReference(schema: Record<string, any> | null, supportedFields: unknown): boolean {
|
||||
if (schema?.additionalProperties !== true || !inspectableSchema(schema)) return false
|
||||
const referenceFields = ['image', 'image[]', 'images', 'file_urls', 'input_image', 'input_images']
|
||||
for (const part of schemaParts(schema)) {
|
||||
if (part.additionalProperties !== undefined && part.additionalProperties !== true || part.unevaluatedProperties !== undefined && part.unevaluatedProperties !== true) return false
|
||||
if (['not', 'oneOf', 'anyOf', 'if', 'then', 'else', 'propertyNames'].some(name => part[name] !== undefined)) return false
|
||||
if (Object.keys(record(part.patternProperties) ?? {}).length) return false
|
||||
if (referenceFields.some(name => Object.prototype.hasOwnProperty.call(record(part.properties) ?? {}, name))) return false
|
||||
}
|
||||
return !Array.isArray(supportedFields) || !supportedFields.some(item => referenceFields.includes(record(item)?.name))
|
||||
}
|
||||
|
||||
/** Parse only a known operation, keeping route evidence separate from a model's combined capabilities. */
|
||||
function parseInvocation(model: Record<string, any>, offer: Record<string, any>, route: Record<string, any>): AiModelProtocol | null {
|
||||
const invocation = record(route.invocation ?? offer.invocation ?? model.invocation)
|
||||
@ -37,17 +145,23 @@ function parseInvocation(model: Record<string, any>, offer: Record<string, any>,
|
||||
const id = (Object.keys(paths) as AiModelProtocol['id'][]).find(key => paths[key] === invocation.path)
|
||||
const headers = safeHeaders(invocation.headers)
|
||||
if (!id || !headers) return null
|
||||
if (invocation.content_type !== (id === 'openai-image-edit' ? 'multipart/form-data' : 'application/json')) return null
|
||||
if (invocation.content_type !== (id === 'openai-image-edit' ? 'multipart/form-data' : 'application/json') && !(id === 'openai-image-generate' && invocation.content_type === 'multipart/form-data')) return null
|
||||
const scopeId = record(offer.service_scope)?.id, routeId = route.id
|
||||
if (scopeId !== undefined && !identifier(scopeId) || routeId !== undefined && !identifier(routeId)) return null
|
||||
if (routeId && headers['X-Relay-Route-Option'] !== routeId) return null
|
||||
const schema = record(invocation.input_schema ?? route.input_schema)
|
||||
const properties = record(schema?.properties) ?? {}
|
||||
const schemaNodes = schemaParts(schema)
|
||||
// Whole-request alternatives/negations are not implemented by the field mapper.
|
||||
// Refuse such contracts rather than checking individual fields and bypassing them.
|
||||
if (id === 'openai-image-generate' && schema && (!inspectableSchema(schema) || schemaNodes.some(part => ['not', 'oneOf', 'anyOf'].some(name => part[name] !== undefined)))) return null
|
||||
const referenceSchemas = (field: string) => schemaNodes.flatMap(part => Object.prototype.hasOwnProperty.call(record(part.properties) ?? {}, field) ? [part.properties[field]] : [])
|
||||
const modes = list(route.input_modes ?? offer.input_modes ?? model.input_modes)
|
||||
const capabilities = list(offer.capabilities ?? model.capabilities)
|
||||
const isTextModel = ['text', 'multimodal'].includes(model.modality) && model.type === 'model'
|
||||
const isImageModel = model.modality === 'image' && [...capabilities, ...list(model.capabilities)].some(value => ['text_to_image', 'image_to_image', 'openai.images.generations', 'openai.images.edits'].includes(value))
|
||||
let inputField: string | undefined, supportsImageInput = false
|
||||
let referenceTransport: AiModelProtocol['referenceTransport'], referenceContract: AiModelProtocol['referenceContract'], requestParameters: AiModelProtocol['requestParameters']
|
||||
if (id === 'openai-chat' || id === 'anthropic-messages') {
|
||||
if (!isTextModel) return null
|
||||
supportsImageInput = modes.some(mode => ['image', 'image_url', 'image_base64'].includes(mode)) || (!modes.length && model.modality === 'multimodal')
|
||||
@ -56,38 +170,68 @@ function parseInvocation(model: Record<string, any>, offer: Record<string, any>,
|
||||
if (id === 'openai-image-edit') {
|
||||
inputField = properties['image[]'] !== false && properties['image[]'] ? 'image[]' : properties.image !== false && properties.image ? 'image' : undefined
|
||||
supportsImageInput = Boolean(inputField)
|
||||
} else if (id === 'openai-image-generate' && invocation.content_type === 'multipart/form-data' && modes.includes('file') && !referenceSchemas('image').includes(false) && ['image', 'images', 'file_urls'].some(field => referenceSchemas(field).some(record) && !referenceSchemas(field).includes(false))) {
|
||||
// Use multipart only when the operation explicitly publishes that content type.
|
||||
// A file input mode alone does not change an application/json contract.
|
||||
inputField = 'image'
|
||||
supportsImageInput = true
|
||||
referenceTransport = 'multipart'
|
||||
} else {
|
||||
if (id === 'openai-image-generate' && invocation.content_type !== 'application/json') return null
|
||||
// A declared image-URL array accepts a data URI unless its schema excludes it.
|
||||
// Keep private image bytes inline; never expose a product file through public storage.
|
||||
inputField = ['file_urls', 'images', 'image'].find(field => {
|
||||
const property = record(properties[field]), items = record(property?.items)
|
||||
const pattern = items?.pattern
|
||||
const declared = referenceSchemas(field)
|
||||
if (declared.includes(false)) return false
|
||||
if (items?.type !== 'string' || !(property?.type === 'array' || Array.isArray(property?.type) && property.type.includes('array'))) return false
|
||||
if (items.format !== undefined && !['uri', 'uri-reference'].includes(items.format)) return false
|
||||
const declaredImageInput = modes.some(mode => ['image', 'image_url', 'image_base64'].includes(mode))
|
||||
if (modes.length && !declaredImageInput) return false
|
||||
if (pattern === undefined) return declaredImageInput && [...capabilities, ...list(model.capabilities)].includes('image_to_image')
|
||||
if ((id === 'openai-image-generate' || modes.length) && !declaredImageInput) return false
|
||||
if (!declared.every(item => acceptsSchemaValue(['data:image/png;base64,AA=='], item))) return false
|
||||
const itemSchemas = declared.flatMap(item => schemaParts(item)).flatMap(item => item.items !== undefined ? [item.items] : [])
|
||||
if (!itemSchemas.every(item => acceptsSchemaValue('data:image/png;base64,AA==', item) && schemaParts(item).every(part => part.format === undefined || ['uri', 'uri-reference'].includes(part.format)))) return false
|
||||
if (pattern === undefined) return declaredImageInput
|
||||
if (typeof pattern !== 'string' || pattern.length > 512 || !pattern.includes('data:image/')) return false
|
||||
try { return new RegExp(pattern).test('data:image/png;base64,AA==') } catch { return false }
|
||||
})
|
||||
supportsImageInput = Boolean(inputField)
|
||||
if (id === 'openai-image-generate' && supportsImageInput) referenceTransport = 'json-inline'
|
||||
}
|
||||
if (id === 'openai-image-generate' && !supportsImageInput && invocation.content_type === 'application/json' && identifier(routeId) && routeId === model.default_route_option_id && canProbeImageReference(schema, invocation.supported_fields)) {
|
||||
inputField = 'image'
|
||||
supportsImageInput = true
|
||||
referenceTransport = 'json-inline'
|
||||
referenceContract = 'probe-required'
|
||||
}
|
||||
if (referenceTransport) {
|
||||
requestParameters = imageRequestParameters(schema, invocation.request_example) ?? undefined
|
||||
if (!requestParameters) return null
|
||||
}
|
||||
if (id === 'async-image-task' && !supportsImageInput) return null
|
||||
}
|
||||
const maxImages = inputField && record(properties[inputField])?.maxItems
|
||||
const imageConstraints = inputField && (referenceTransport === 'multipart' ? ['image', 'images', 'file_urls'].flatMap(field => referenceSchemas(field).flatMap(value => schemaParts(value))) : referenceSchemas(inputField).flatMap(value => schemaParts(value)))
|
||||
if (imageConstraints && imageConstraints.some(part => part.maxItems !== undefined && (!Number.isInteger(part.maxItems) || part.maxItems < 1))) return null
|
||||
const declaredLimits = imageConstraints ? imageConstraints.flatMap(part => [part.maxItems, ...(Array.isArray(part.anyOf) ? part.anyOf.map((entry: any) => record(entry)?.maxItems) : [])]).filter(value => Number.isInteger(value) && value > 0) : []
|
||||
// Without a published image-array contract, only the single-image challenge is verified.
|
||||
const maxImages = referenceContract === 'probe-required' ? 1 : declaredLimits.length ? Math.min(...declaredLimits) : undefined
|
||||
const maxPromptLength = record(properties.prompt)?.maxLength
|
||||
// The editor has no quality control: choose an explicit supported working
|
||||
// quality instead of relying on an undocumented upstream default.
|
||||
const quality = record(properties.quality)
|
||||
const imageQuality = id === 'openai-image-generate' && list(quality?.enum).includes('medium') &&
|
||||
const imageQuality = id === 'openai-image-generate' && referenceTransport !== 'multipart' && list(quality?.enum).includes('medium') &&
|
||||
(quality?.const === undefined || quality.const === 'medium') &&
|
||||
(quality?.default === undefined || quality.default === 'medium') ? 'medium' as const : undefined
|
||||
(quality?.default === undefined || quality.default === 'medium') && referenceSchemas('quality').every(item => acceptsSchemaValue('medium', item)) ? 'medium' as const : undefined
|
||||
if (requestParameters && imageQuality) requestParameters = safeImageParameters({ ...requestParameters, quality: imageQuality }) ?? undefined
|
||||
return { id, path: paths[id], headers, supportsImageInput, ...(inputField ? { inputField } : {}),
|
||||
...(scopeId ? { scopeId } : {}), ...(routeId ? { routeId } : {}),
|
||||
...(identifier(offer.public_operation_id) ? { operationId: offer.public_operation_id } : {}),
|
||||
...(Number.isInteger(maxImages) && maxImages > 0 ? { maxImages: Math.min(8, maxImages) } : {}),
|
||||
...(typeof maxImages === 'number' && Number.isInteger(maxImages) && maxImages > 0 ? { maxImages: Math.min(8, maxImages) } : {}),
|
||||
...(Number.isInteger(maxPromptLength) && maxPromptLength > 0 ? { maxPromptLength } : {}),
|
||||
...(imageQuality ? { imageQuality } : {}) }
|
||||
...(imageQuality ? { imageQuality } : {}),
|
||||
...(referenceContract ? { referenceContract } : {}),
|
||||
...(referenceTransport ? { referenceTransport, requestParameters } : {}) }
|
||||
}
|
||||
|
||||
/** At setup time pick a concrete compatible route in the default scope; no runtime routing fallback. */
|
||||
@ -103,10 +247,10 @@ export function parseAiModelProtocols(value: unknown): AiModelProtocol[] {
|
||||
const routes = Array.isArray(offer.route_options) ? offer.route_options.map(record).filter(Boolean) as Record<string, any>[] : []
|
||||
const eligible = routes.filter(route => route.available !== false).sort((a, b) => Number(b.id === model.default_route_option_id) - Number(a.id === model.default_route_option_id))
|
||||
const protocols = eligible.map(route => parseInvocation(model, offer, route)).filter(Boolean) as AiModelProtocol[]
|
||||
const chosen = protocols.find(protocol => protocol.supportsImageInput) ?? protocols[0]
|
||||
const chosen = protocols.find(protocol => !protocol.referenceContract && protocol.referenceTransport === 'json-inline') ?? protocols.find(protocol => !protocol.referenceContract && protocol.supportsImageInput) ?? protocols.find(protocol => protocol.supportsImageInput) ?? protocols[0]
|
||||
if (chosen && !found.some(protocol => JSON.stringify(protocol) === JSON.stringify(chosen))) found.push(chosen)
|
||||
}
|
||||
return found.sort((a, b) => Number(b.supportsImageInput) - Number(a.supportsImageInput))
|
||||
return found.sort((a, b) => Number(b.supportsImageInput) - Number(a.supportsImageInput) || Number(Boolean(a.referenceContract)) - Number(Boolean(b.referenceContract)) || Number(b.referenceTransport === 'json-inline') - Number(a.referenceTransport === 'json-inline'))
|
||||
}
|
||||
|
||||
export function validateStoredAiModelProtocols(value: unknown): AiModelProtocol[] {
|
||||
@ -120,9 +264,14 @@ export function validateStoredAiModelProtocols(value: unknown): AiModelProtocol[
|
||||
if (raw.inputField !== undefined && !['image', 'image[]', 'images', 'file_urls'].includes(raw.inputField)) return []
|
||||
if (['maxImages', 'maxPromptLength'].some(field => raw[field] !== undefined && (!Number.isInteger(raw[field]) || raw[field] < 1 || raw[field] > (field === 'maxImages' ? 8 : 1000000)))) return []
|
||||
if (raw.imageQuality !== undefined && (raw.id !== 'openai-image-generate' || raw.imageQuality !== 'medium')) return []
|
||||
if (raw.referenceTransport !== undefined && (!['json-inline', 'multipart'].includes(raw.referenceTransport) || raw.id !== 'openai-image-generate' || !raw.supportsImageInput || !['image', 'images', 'file_urls'].includes(raw.inputField) || raw.referenceTransport === 'multipart' && raw.inputField !== 'image')) return []
|
||||
if (raw.referenceContract !== undefined && (raw.referenceContract !== 'probe-required' || raw.id !== 'openai-image-generate' || raw.referenceTransport !== 'json-inline' || raw.inputField !== 'image' || raw.maxImages !== 1 || !raw.routeId || !raw.scopeId)) return []
|
||||
const requestParameters = raw.requestParameters !== undefined ? safeImageParameters(raw.requestParameters) : undefined
|
||||
if (raw.referenceTransport !== undefined && (!requestParameters || requestParameters.n !== 1 || !requestParameters.response_format) || raw.requestParameters !== undefined && (raw.referenceTransport === undefined || !requestParameters)) return []
|
||||
if (raw.id === 'async-image-task' && (!raw.supportsImageInput || raw.inputField !== 'file_urls') || raw.id === 'openai-image-generate' && raw.supportsImageInput && !raw.inputField || raw.id === 'openai-image-edit' && (!raw.supportsImageInput || !['image', 'image[]'].includes(raw.inputField))) return []
|
||||
result.push({ id: raw.id, path: raw.path, headers, supportsImageInput: raw.supportsImageInput,
|
||||
...Object.fromEntries(['inputField', 'scopeId', 'routeId', 'operationId', 'maxImages', 'maxPromptLength', 'imageQuality'].filter(key => raw[key] !== undefined).map(key => [key, raw[key]])) })
|
||||
...Object.fromEntries(['inputField', 'scopeId', 'routeId', 'operationId', 'maxImages', 'maxPromptLength', 'imageQuality', 'referenceTransport', 'referenceContract'].filter(key => raw[key] !== undefined).map(key => [key, raw[key]])),
|
||||
...(requestParameters ? { requestParameters } : {}) })
|
||||
}
|
||||
return result
|
||||
}
|
||||
@ -168,7 +317,7 @@ async function normalizeImages(images: AiModelProtocolInput['images'], forAnalys
|
||||
return forAnalysis ? pipeline.flatten({ background: '#ffffff' }).jpeg({ quality: 85 }).toBuffer() : pipeline.png().toBuffer()
|
||||
}))
|
||||
}
|
||||
async function decodeImage(data: any, input: AiModelProtocolInput, taskId: string | null): Promise<Buffer> {
|
||||
async function decodeImage(data: any, input: AiModelProtocolInput, taskId: string | null, summary: Record<string, unknown>): Promise<Buffer> {
|
||||
let bytes: Buffer
|
||||
if (typeof data?.b64_json === 'string' && /^[A-Za-z0-9+/]+={0,2}$/.test(data.b64_json) && data.b64_json.length <= 28 * 1024 * 1024) bytes = Buffer.from(data.b64_json, 'base64')
|
||||
else if (typeof data?.url === 'string') {
|
||||
@ -177,8 +326,11 @@ async function decodeImage(data: any, input: AiModelProtocolInput, taskId: strin
|
||||
bytes = Buffer.from(await response.arrayBuffer())
|
||||
} else throw new AiModelProtocolError('模型没有返回可保存的图片', false, null, taskId)
|
||||
try {
|
||||
const source = await sharp(bytes, { limitInputPixels: 40_000_000 }).metadata()
|
||||
const output = await sharp(bytes, { limitInputPixels: 40_000_000 }).rotate().resize(1440, 1440, { fit: 'inside', withoutEnlargement: true }).png().toBuffer()
|
||||
if (output.length > 20 * 1024 * 1024) throw new Error('size')
|
||||
const stored = await sharp(output).metadata()
|
||||
Object.assign(summary, { generatedWidth: source.width, generatedHeight: source.height, storedWidth: stored.width, storedHeight: stored.height })
|
||||
return output
|
||||
} catch { throw new AiModelProtocolError('模型图片无法解码或超出限制', false, null, taskId) }
|
||||
}
|
||||
@ -187,12 +339,15 @@ export async function callAiModelProtocol(input: AiModelProtocolInput): Promise<
|
||||
const protocol = validateStoredAiModelProtocols([input.protocol])[0]
|
||||
if (!protocol) throw new AiModelProtocolError('模型协议无效，请重新同步', false)
|
||||
const imageCount = input.images?.length ?? 0
|
||||
if (imageCount > (protocol.maxImages ?? 8) || imageCount && !protocol.supportsImageInput) throw new AiModelProtocolError('该模型接口不支持当前参考图片', false)
|
||||
if (imageCount && !protocol.supportsImageInput) throw new AiModelProtocolError('参考图传入方式尚未适配，请重新同步模型目录', false)
|
||||
if (imageCount > (protocol.maxImages ?? 8)) throw new AiModelProtocolError(`当前模型最多可接收 ${protocol.maxImages ?? 8} 张参考图，请减少图片`, false)
|
||||
if (protocol.maxPromptLength && input.prompt.length > protocol.maxPromptLength) throw new AiModelProtocolError(`该模型提示词最多 ${protocol.maxPromptLength} 字，请精简内容`, false)
|
||||
if (protocol.id === 'async-image-task' && !input.onTaskSubmitted) throw new AiModelProtocolError('异步任务缺少持久化检查点', false)
|
||||
if (input.resumeTaskId && (!/^[A-Za-z0-9_-]{1,160}$/.test(input.resumeTaskId) || protocol.id !== 'async-image-task')) throw new AiModelProtocolError('模型任务编号无效', false)
|
||||
let taskId = input.resumeTaskId ?? null, requestId: string | null = null, dispatched = Boolean(input.resumeTaskId)
|
||||
const summary = { adapterVersion: AI_MODEL_ADAPTER_VERSION, protocol: protocol.id, routeId: protocol.routeId ?? null }
|
||||
const summary: Record<string, unknown> = { adapterVersion: AI_MODEL_ADAPTER_VERSION, protocol: protocol.id, routeId: protocol.routeId ?? null,
|
||||
...(protocol.referenceTransport ? { referenceTransport: protocol.referenceTransport, requestParameters: protocol.requestParameters } : {}),
|
||||
...(protocol.referenceContract ? { referenceContract: protocol.referenceContract } : {}) }
|
||||
try {
|
||||
if (!taskId) {
|
||||
const forAnalysis = protocol.id === 'openai-chat' || protocol.id === 'anthropic-messages'
|
||||
@ -204,10 +359,11 @@ export async function callAiModelProtocol(input: AiModelProtocolInput): Promise<
|
||||
const maxTokens = input.maxOutputTokens && Number.isInteger(input.maxOutputTokens) ? Math.max(32, Math.min(8000, input.maxOutputTokens)) : 8000
|
||||
if (protocol.id === 'openai-chat') body = JSON.stringify({ model: input.modelId, messages: [{ role: 'user', content: images.length ? [{ type: 'text', text: input.prompt }, ...dataUris.map(url => ({ type: 'image_url', image_url: { url } }))] : input.prompt }], max_tokens: maxTokens, stream: false })
|
||||
else if (protocol.id === 'anthropic-messages') body = JSON.stringify({ model: input.modelId, messages: [{ role: 'user', content: [{ type: 'text', text: input.prompt }, ...images.map(image => ({ type: 'image', source: { type: 'base64', media_type: imageMimeType, data: image.toString('base64') } }))] }], max_tokens: maxTokens, stream: false })
|
||||
else if (protocol.id === 'openai-image-edit') {
|
||||
body = new FormData(); body.set('model', input.modelId); body.set('prompt', input.prompt); body.set('n', '1'); body.set('response_format', 'b64_json')
|
||||
else if (protocol.id === 'openai-image-edit' || protocol.referenceTransport === 'multipart') {
|
||||
body = new FormData(); body.set('model', input.modelId); body.set('prompt', input.prompt)
|
||||
for (const [field, value] of Object.entries(protocol.requestParameters ?? { n: 1, response_format: 'b64_json' })) body.set(field, String(value))
|
||||
images.forEach((image, index) => (body as FormData).append(protocol.inputField!, new Blob([new Uint8Array(image)], { type: 'image/png' }), `reference-${index + 1}.png`))
|
||||
} else body = JSON.stringify({ model: input.modelId, prompt: input.prompt, ...(images.length ? { [protocol.inputField!]: dataUris } : {}), ...(protocol.id === 'openai-image-generate' ? { n: 1, response_format: 'b64_json', ...(protocol.imageQuality ? { quality: protocol.imageQuality } : {}) } : {}) })
|
||||
} else body = JSON.stringify({ model: input.modelId, prompt: input.prompt, ...(images.length ? { [protocol.inputField!]: dataUris } : {}), ...(protocol.id === 'openai-image-generate' ? { n: 1, response_format: 'b64_json', ...(protocol.imageQuality ? { quality: protocol.imageQuality } : {}), ...protocol.requestParameters } : {}) })
|
||||
dispatched = true
|
||||
const response = await requestAiModelOperation(input.baseUrl, input.apiKey, protocol.path, body, input.idempotencyKey, protocol.headers, input.beforeRequest)
|
||||
const parsed = await readJson(response, input.apiKey); requestId = parsed.requestId
|
||||
@ -220,7 +376,7 @@ export async function callAiModelProtocol(input: AiModelProtocolInput): Promise<
|
||||
const text = typeof content === 'string' ? content : Array.isArray(content) ? content.filter(item => item?.type === 'text' && typeof item.text === 'string').map(item => item.text).join('\n') : ''
|
||||
if (!text.trim()) throw new AiModelProtocolError('模型未返回可用文字', false, requestId)
|
||||
return { text, requestId, summary }
|
||||
} else return { image: await decodeImage(parsed.data?.data?.[0], input, null), requestId, summary }
|
||||
} else return { image: await decodeImage(parsed.data?.data?.[0], input, null, summary), requestId, summary }
|
||||
}
|
||||
const deadline = Date.now() + 40_000
|
||||
for (let attempt = 0; attempt < 20 && Date.now() < deadline; attempt++) {
|
||||
@ -231,7 +387,7 @@ export async function callAiModelProtocol(input: AiModelProtocolInput): Promise<
|
||||
const outputs = parsed.data?.result?.outputs
|
||||
const image = Array.isArray(outputs) ? outputs.find(item => item?.type === 'image' && (typeof item.url === 'string' || typeof item.b64_json === 'string')) : null
|
||||
if (!image) throw new AiModelProtocolError('模型任务没有图片产物', false, requestId, taskId)
|
||||
return { image: await decodeImage(image, input, taskId), requestId, summary: { ...summary, taskId } }
|
||||
return { image: await decodeImage(image, input, taskId, summary), requestId, summary: { ...summary, taskId } }
|
||||
}
|
||||
if (response.status !== 202 || parsed.data?.ready !== false) throw new AiModelProtocolError('模型任务状态无法确认，已保留任务', true, requestId, taskId)
|
||||
await new Promise(resolve => setTimeout(resolve, 2000))
|
||||
|
||||
@ -24,7 +24,7 @@ export interface AiModelCapabilities {
|
||||
allowedStages: AiModelStage[]
|
||||
source: 'upstream' | 'verified' | 'unknown'
|
||||
reason: string
|
||||
protocols?: Partial<Record<AiModelStage, { id: string; path: string; scopeId?: string; routeId?: string; operationId?: string }>>
|
||||
protocols?: Partial<Record<AiModelStage, { id: string; path: string; scopeId?: string; routeId?: string; operationId?: string; maxImages?: number }>>
|
||||
combinedTextTest?: boolean
|
||||
}
|
||||
export interface AiModelCatalogEntry {
|
||||
|
||||
@ -3,7 +3,7 @@ import { z } from 'zod'
|
||||
import { AiModelError, assertAiModelAdmin, assertAiModelAdminForWrite } from './ai-model-access'
|
||||
import { aiModelAudit, aiModelTransaction } from './ai-model-repository'
|
||||
import { getAiModelAdminState, resolveAiModelVerification } from './ai-model-service'
|
||||
import { AI_MODEL_ADAPTER_VERSION, AiModelProtocolError, callAiModelProtocol } from './ai-model-protocol'
|
||||
import { AI_MODEL_ADAPTER_VERSION, AiModelProtocolError, callAiModelProtocol, validateStoredAiModelProtocols, type AiModelProtocol } from './ai-model-protocol'
|
||||
import { AI_MODEL_TEST_PURPOSES, aiModelTestStages } from './ai-model-types'
|
||||
import { AI_MODEL_VERIFICATION_COLUMNS, lockAiModelVerificationExecution, verificationExecutionId, type AiModelVerificationRow } from './ai-model-verification-state'
|
||||
import { prisma } from './prisma'
|
||||
@ -14,6 +14,28 @@ import type { AiModelDatabase } from './ai-model-repository'
|
||||
|
||||
const requestSchema = z.object({ stage: z.enum(AI_MODEL_TEST_PURPOSES), revision: z.number().int().positive(), idempotencyKey: z.string().uuid() }).strict()
|
||||
function safeId(value: unknown, secret: string): string | null { return typeof value === 'string' && /^[\w.-]{1,160}$/.test(value) && !value.includes(secret) ? value : null }
|
||||
/** Persist only bounded adapter facts; model output cannot supply or override validation evidence. */
|
||||
function imageTransportEvidence(value: unknown, protocol: AiModelProtocol, secret: string): Record<string, unknown> {
|
||||
if (!value || typeof value !== 'object' || Array.isArray(value)) return {}
|
||||
const summary = value as Record<string, unknown>, evidence: Record<string, unknown> = {}
|
||||
if ((summary.referenceTransport === 'multipart' || summary.referenceTransport === 'json-inline') && summary.referenceTransport === protocol.referenceTransport) {
|
||||
evidence.referenceTransport = summary.referenceTransport
|
||||
const actual = validateStoredAiModelProtocols([{ ...protocol, requestParameters: summary.requestParameters }])[0]
|
||||
const expected = validateStoredAiModelProtocols([protocol])[0]
|
||||
// Use the adapter's existing parameter allowlist, and require the actual
|
||||
// values to match the route whose context was locked for this execution.
|
||||
if (actual?.requestParameters && expected?.requestParameters && JSON.stringify(actual.requestParameters) === JSON.stringify(expected.requestParameters) &&
|
||||
(!secret || !JSON.stringify(actual.requestParameters).includes(secret))) evidence.requestParameters = actual.requestParameters
|
||||
}
|
||||
for (const [widthKey, heightKey, maximum] of [['generatedWidth', 'generatedHeight', 40_000_000], ['storedWidth', 'storedHeight', 1440]] as const) {
|
||||
const width = summary[widthKey], height = summary[heightKey]
|
||||
if (typeof width === 'number' && typeof height === 'number' && Number.isInteger(width) && Number.isInteger(height) &&
|
||||
width > 0 && height > 0 && width <= maximum && height <= maximum && width * height <= 40_000_000) {
|
||||
evidence[widthKey] = width; evidence[heightKey] = height
|
||||
}
|
||||
}
|
||||
return evidence
|
||||
}
|
||||
async function assertExecutionSlot(db: AiModelDatabase, actorId: string, resumeId: string | null = null): Promise<void> {
|
||||
await detailTransactionLock(db, 'detail-generator-execution')
|
||||
const rows = await db.$queryRawUnsafe<Array<{ count: number; own: number }>>(`SELECT COUNT(*)::int AS count,COUNT(*) FILTER (WHERE actor_id=$1)::int AS own FROM (
|
||||
@ -30,7 +52,7 @@ function assertCombinedContexts(contexts: VerificationContext[]): void {
|
||||
}
|
||||
async function executeVerification(rows: AiModelVerificationRow[], userId: string, providerId: string, resume: boolean): Promise<void> {
|
||||
const row = rows[0], combined = rows.length === 2
|
||||
let sent = false, resultReceived = false, apiKey = '', knownTaskId = row.taskId
|
||||
let sent = false, resultReceived = false, apiKey = '', knownTaskId = row.taskId, knownRequestId: string | null = null
|
||||
const beforeRequest = async () => {
|
||||
await assertAiModelAdmin(userId)
|
||||
const current = await Promise.all(rows.map(async item => {
|
||||
@ -52,12 +74,13 @@ async function executeVerification(rows: AiModelVerificationRow[], userId: strin
|
||||
if (combined) assertCombinedContexts(contexts)
|
||||
const context = contexts[0]
|
||||
apiKey = context.apiKey
|
||||
knownRequestId = safeId(row.requestId, apiKey)
|
||||
if (context.contextHash !== row.contextHash) throw new AiModelError('验证对应的连接或路由已改变，不能继续此任务', 409, 'verification_context_changed')
|
||||
const challenge = readVerificationChallenge(row.evidence?.challenge)
|
||||
const prompt = combined ? '一次完成两项独立任务。analysis：必须读取参考图，识别左上与右下方块的颜色，以英文小写填写。copy：真实产品资料为型号TEST、颜色蓝色，图片只是读图测试图，不是该产品外观；只依据这两条真实资料写一屏中文详情页，不编造材质、尺寸、性能或认证。严格只输出一个JSON对象：{"analysis":{"topLeft":"英文小写颜色","bottomRight":"英文小写颜色"},"copy":{"sellingPoints":["卖点"],"warnings":[],"screens":[{"title":"标题","body":"正文","imagePrompt":"无文字的场景描述"}]}}。'
|
||||
: row.stage === 'analysis' ? '观察图片：左上方方块与右下方方块分别是什么颜色？只输出 JSON：{"topLeft":"英文小写颜色","bottomRight":"英文小写颜色"}。不要根据文字猜测，必须读取图片。'
|
||||
: row.stage === 'copy' ? '真实产品资料：型号TEST，颜色蓝色。不要编造其他规格。写一屏中文详情页，严格只输出 JSON：{"sellingPoints":["卖点"],"warnings":[],"screens":[{"title":"标题","body":"正文","imagePrompt":"无文字的场景描述"}]}。'
|
||||
: '依据参考图片生成一张简洁的产品展示图，保留两个方块的颜色与相对位置，白色背景，不要加入文字或标志。'
|
||||
: '将参考图重新绘制为干净的平面图，严格保留两个方块各自的原始颜色、形状和相对位置，纯白背景；不要添加文字、标志、阴影、纹理、渐变、光照或其它内容。'
|
||||
const result = await callAiModelProtocol({ baseUrl: context.provider.baseUrl, apiKey: context.apiKey, modelId: context.model.id, protocol: context.protocol,
|
||||
prompt, ...(row.stage !== 'copy' ? { images: [{ buffer: await verificationReferenceImage(challenge), mimeType: 'image/png' }] } : {}), maxOutputTokens: combined ? 1800 : row.stage === 'copy' ? 1200 : 1024,
|
||||
idempotencyKey: row.idempotencyKey, beforeRequest, ...(resume ? { resumeTaskId: row.taskId! } : {}),
|
||||
@ -72,8 +95,12 @@ async function executeVerification(rows: AiModelVerificationRow[], userId: strin
|
||||
})
|
||||
} })
|
||||
resultReceived = true // The native protocol has returned a terminal, decoded result.
|
||||
knownRequestId = safeId(result.requestId, context.apiKey) || knownRequestId
|
||||
const checks: Partial<Record<AiModelVerificationRow['stage'], AiModelTextCheck>> = combined ? await validateAiModelCombinedVerificationOutput(result, challenge)
|
||||
: { [row.stage]: { status: 'passed', message: '实际接口测试通过', evidence: await validateAiModelVerificationOutput(row.stage, result, challenge) } }
|
||||
: { [row.stage]: { status: 'passed', message: '实际接口测试通过', evidence: {
|
||||
...(row.stage === 'image' ? imageTransportEvidence(result.summary, context.protocol, context.apiKey) : {}),
|
||||
...await validateAiModelVerificationOutput(row.stage, result, challenge),
|
||||
} } }
|
||||
await assertAiModelAdmin(userId)
|
||||
await aiModelTransaction(async db => {
|
||||
await assertAiModelAdminForWrite(userId, db)
|
||||
@ -101,7 +128,7 @@ async function executeVerification(rows: AiModelVerificationRow[], userId: strin
|
||||
const message = safeProtocolMessage || (unknown ? '请求结果尚未确认，请核查上游记录；禁止重复提交' : error instanceof AiModelError ? error.message : '模型测试未通过')
|
||||
await aiModelTransaction(async db => {
|
||||
for (const item of rows) {
|
||||
const updated = await db.$executeRawUnsafe(`UPDATE ai_model_verifications SET status=$2,request_id=$3,task_id=COALESCE(task_id,$4),message=$5,finished_at=NOW() WHERE id=$1 AND status='running'`, item.id, unknown ? 'unknown' : 'failed', safeId(outcome.requestId, apiKey), safeId(outcome.taskId || knownTaskId, apiKey), message)
|
||||
const updated = await db.$executeRawUnsafe(`UPDATE ai_model_verifications SET status=$2,request_id=$3,task_id=COALESCE(task_id,$4),message=$5,finished_at=NOW() WHERE id=$1 AND status='running'`, item.id, unknown ? 'unknown' : 'failed', safeId(outcome.requestId, apiKey) || knownRequestId, safeId(outcome.taskId || knownTaskId, apiKey), message)
|
||||
if (updated === 1) await aiModelAudit(db, userId, unknown ? 'model.verification-unknown' : 'model.verification-failed', providerId, { verificationId: item.id, modelKey: item.modelKey, stage: item.stage, ...(combined ? { executionGroupId: verificationExecutionId(item) } : {}) })
|
||||
}
|
||||
})
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user