fix(bom-costing): 修正多模态事实结果契约
This commit is contained in:
WuYongJie
parent
d9ebf7d9cb
commit
70a2d8eb55
@ -24,8 +24,23 @@ export const bomCostingImageFactKindSchema = z.enum([
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])
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const nullToUndefined = (value: unknown) => value === null ? undefined : value
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function parseFiniteJsonNumber(value: unknown) {
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if (typeof value === 'number') return value
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if (typeof value !== 'string') return value
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const normalized = value.trim().replace(/,/g, '')
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if (!normalized) return undefined
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if (!/^[+-]?(?:\d+(?:\.\d+)?|\.\d+)$/.test(normalized)) {
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return undefined
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}
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const parsed = Number(normalized)
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return Number.isFinite(parsed) ? parsed : undefined
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}
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const optionalFiniteNumberSchema = z.preprocess(
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nullToUndefined,
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(value) => parseFiniteJsonNumber(nullToUndefined(value)),
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z.number().finite().optional(),
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)
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const optionalUnitSchema = z.preprocess(
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@ -46,7 +61,10 @@ export const bomCostingExtractedFactSchema = z.object({
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),
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basis: z.enum(['IMAGE', 'SOURCE_TEXT']),
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note: z.string().trim().min(1).max(1_000),
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confidence: z.number().finite().min(0).max(1),
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confidence: z.preprocess(
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parseFiniteJsonNumber,
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z.number().finite().min(0).max(1),
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),
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})
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export const bomCostingImageFactRecordSchema = z.object({
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@ -221,6 +221,144 @@ describe('BOM 逐图事实提取', () => {
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})
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})
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it('把模型返回的数值字符串规范为数字，非单值文本保留原文但不拒绝整批', async () => {
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const singleImageSnapshot: MarketDetailSnapshot = {
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...snapshot,
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mainImages: ['https://img.example.com/numeric-fact.jpg'],
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skuImages: [],
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detailImages: [],
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skus: snapshot.skus.map((sku) => ({ ...sku, imageUrl: null })),
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}
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let request: ReturnType<typeof readBatchRequest> | null = null
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jest.spyOn(global, 'fetch').mockImplementation(async (_input, init) => {
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request = readBatchRequest(init)
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return createStreamingResponse(JSON.stringify({
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images: [{
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imageId: request.imageIds[0],
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observedTexts: ['内空34.4cm', '整机74*80*62cm'],
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facts: [{
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factId: 'inner-width',
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kind: 'DIMENSION',
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subject: '侧面内空',
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value: '内空34.4cm',
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numericValue: '344',
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unit: 'mm',
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relatedSkuIds: [],
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basis: 'SOURCE_TEXT',
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note: '图片尺寸标注',
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confidence: '0.96',
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}, {
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factId: 'overall-size-text',
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kind: 'DIMENSION',
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subject: '整机尺寸原文',
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value: '74*80*62cm',
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numericValue: '74*80*62',
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unit: null,
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relatedSkuIds: [],
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basis: 'SOURCE_TEXT',
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note: '这是多维尺寸，不应压成单一数值',
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confidence: 0.9,
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}],
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}],
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}))
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})
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const output = await extractBomCostingImageFacts({
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config: providerConfig,
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snapshot: singleImageSnapshot,
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})
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expect(request?.prompt.requiredOutputShape.images[0].facts[0]).toMatchObject({
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numericValue: 344,
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confidence: 0.85,
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})
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expect(request?.prompt.outputFieldRules.numericValue).toContain('JSON number')
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expect(output.snapshot.imageFacts[0]?.facts).toEqual([
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expect.objectContaining({
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factId: 'inner-width',
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numericValue: 344,
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confidence: 0.96,
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}),
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expect.objectContaining({
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factId: 'overall-size-text',
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value: '74*80*62cm',
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numericValue: undefined,
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}),
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])
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})
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it('同批部分图片格式异常时先保存有效图片，续跑只重新分析未完成图片', async () => {
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process.env.BOM_COSTING_AI_FACT_BATCH_SIZE = '3'
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const threeImageSnapshot: MarketDetailSnapshot = {
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...snapshot,
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mainImages: [
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'https://img.example.com/partial-1.jpg',
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'https://img.example.com/partial-2.jpg',
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'https://img.example.com/partial-3.jpg',
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],
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skuImages: [],
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detailImages: [],
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skus: snapshot.skus.map((sku) => ({ ...sku, imageUrl: null })),
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}
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const sentBatches: string[][] = []
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let partialSnapshot: BomCostingSourceFactSnapshot | null = null
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jest.spyOn(console, 'warn').mockImplementation(() => undefined)
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jest.spyOn(global, 'fetch').mockImplementation(async (_input, init) => {
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const batch = readBatchRequest(init)
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sentBatches.push(batch.imageUrls)
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if (sentBatches.length === 1) {
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return createStreamingResponse(JSON.stringify({
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images: batch.imageIds.map((imageId, index) => ({
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imageId,
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observedTexts: [],
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facts: index < 2
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? []
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: [{
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factId: 'invalid-optional-number',
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kind: 'DIMENSION',
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subject: '尺寸',
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value: '尺寸信息',
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numericValue: { raw: '34.4cm' },
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unit: 'mm',
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relatedSkuIds: [],
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basis: 'SOURCE_TEXT',
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note: '图片标注',
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confidence: 0.8,
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}],
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})),
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}))
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}
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return createStreamingResponse(JSON.stringify({
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images: batch.imageIds.map((imageId) => ({
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imageId,
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observedTexts: [],
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facts: [],
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})),
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}))
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})
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await expect(extractBomCostingImageFacts({
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config: providerConfig,
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snapshot: threeImageSnapshot,
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onBatchCompleted: async (progress) => {
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partialSnapshot = progress.snapshot
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},
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})).rejects.toThrow('其余 2 张已保存，继续处理时只分析未完成图片')
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expect(partialSnapshot?.imageFacts).toHaveLength(2)
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const output = await extractBomCostingImageFacts({
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config: providerConfig,
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snapshot: threeImageSnapshot,
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existingSnapshot: partialSnapshot ?? undefined,
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})
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expect(sentBatches).toEqual([
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threeImageSnapshot.mainImages,
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[threeImageSnapshot.mainImages[2]],
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])
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expect(output.snapshot.imageFacts).toHaveLength(3)
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})
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it('断点续跑只发送尚未保存事实的图片', async () => {
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const sentUrls: string[] = []
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const fetchSpy = jest.spyOn(global, 'fetch').mockImplementation(
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@ -50,8 +50,12 @@ const aiImageFactResultSchema = z.object({
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),
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})
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const aiFactBatchPayloadSchema = z.object({
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images: z.array(aiImageFactResultSchema).min(1).max(8),
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const aiFactBatchEnvelopeSchema = z.object({
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images: z.array(z.unknown()).min(1).max(8),
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})
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const aiFactBatchImageIdentitySchema = z.object({
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imageId: z.string().trim().min(1).max(80),
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})
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export interface BomCostingMultimodalInputImage {
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@ -270,22 +274,32 @@ function buildFactBatchUserText(
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: undefined,
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requiredOutputShape: {
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images: [{
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imageId: '必须来自本批输入',
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observedTexts: ['图片中与产品和成本有关的原文'],
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imageId: 'IMG_0001',
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observedTexts: ['内空34.4cm'],
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facts: [{
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factId: '当前图片内唯一',
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kind: 'DIMENSION | MATERIAL | PART | STRUCTURE | MECHANISM | HARDWARE | PACKAGING | PROCESS | WEIGHT | SKU_DIFFERENCE | TEXT',
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subject: '事实主体',
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value: '事实内容',
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numericValue: '明确数值 optional',
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unit: 'mm | kg | 个 | 件等 optional',
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relatedSkuIds: ['只能来自 SKU 真相'],
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basis: 'IMAGE | SOURCE_TEXT',
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note: '证据位置、可见程度或待确认说明',
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confidence: '0 to 1',
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factId: 'inner_width_1',
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kind: 'DIMENSION',
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subject: '侧面内空',
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value: '图片标注内空34.4cm',
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numericValue: 344,
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unit: 'mm',
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relatedSkuIds: [],
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basis: 'SOURCE_TEXT',
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note: '尺寸标注位于图片右侧',
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confidence: 0.85,
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}],
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}],
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},
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outputFieldRules: {
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imageId: '必须逐一使用本批 batchImages 中的真实 imageId，不得照抄示例',
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factId: '同一图片内唯一，使用简短稳定的英文或数字标识',
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kind: '只能使用系统提示中列出的事实类型',
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numericValue:
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'必须是 JSON number，不得加引号；没有单一明确数值时省略或返回 null，并把原文保留在 value',
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confidence: '必须是 0 到 1 的 JSON number，不得加引号',
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relatedSkuIds: '只能使用 skuTruth 中存在的真实 externalSkuId；无法绑定时返回空数组',
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optionalFields: '可选字段没有证据时省略或返回 null，不得填写 optional 等说明文字',
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},
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productTruth: {
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itemId: snapshot.itemId,
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channel: snapshot.channel,
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@ -305,36 +319,115 @@ function buildFactBatchUserText(
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})
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}
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function assertExactImageCoverage(
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expectedImages: BomCostingMultimodalInputImage[],
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actualImages: z.infer<typeof aiImageFactResultSchema>[],
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) {
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const expectedIds = new Set(expectedImages.map((image) => image.imageId))
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const actualIds = new Set(actualImages.map((image) => image.imageId))
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if (actualIds.size !== actualImages.length) {
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throw new Error('模型返回了重复图片事实')
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}
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const missing = Array.from(expectedIds).filter((id) => !actualIds.has(id))
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const unexpected = Array.from(actualIds).filter((id) => !expectedIds.has(id))
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if (missing.length > 0 || unexpected.length > 0) {
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throw new Error(
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`图片事实覆盖不一致，缺少 ${missing.length} 张，多出 ${unexpected.length} 张`,
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)
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}
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interface RejectedFactImage {
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imageId: string
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reason:
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| 'MISSING'
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| 'DUPLICATE'
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| 'INVALID_SHAPE'
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| 'INVALID_SKU_REFERENCE'
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| 'DUPLICATE_FACT_ID'
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issuePaths: string[]
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issueCodes: string[]
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}
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function assertFactSkuReferences(
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function validateFactBatchImages(
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snapshot: MarketDetailSnapshot,
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images: z.infer<typeof aiImageFactResultSchema>[],
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expectedImages: BomCostingMultimodalInputImage[],
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rawImages: unknown[],
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) {
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const skuIds = new Set(snapshot.skus.map((sku) => sku.externalSkuId))
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for (const image of images) {
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for (const fact of image.facts) {
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const unexpected = fact.relatedSkuIds.find((id) => !skuIds.has(id))
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if (unexpected) {
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throw new Error(`图片事实引用了不存在的 SKU ${unexpected}`)
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}
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const expectedIds = new Set(expectedImages.map((image) => image.imageId))
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const rawByImageId = new Map<string, unknown[]>()
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const unexpectedImageIds: string[] = []
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let unreadableImageCount = 0
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for (const rawImage of rawImages) {
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const identity = aiFactBatchImageIdentitySchema.safeParse(rawImage)
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if (!identity.success) {
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unreadableImageCount += 1
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continue
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}
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if (!expectedIds.has(identity.data.imageId)) {
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unexpectedImageIds.push(identity.data.imageId)
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continue
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}
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const entries = rawByImageId.get(identity.data.imageId) ?? []
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entries.push(rawImage)
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rawByImageId.set(identity.data.imageId, entries)
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}
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const skuIds = new Set(snapshot.skus.map((sku) => sku.externalSkuId))
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const acceptedImages: z.infer<typeof aiImageFactResultSchema>[] = []
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const rejectedImages: RejectedFactImage[] = []
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for (const expectedImage of expectedImages) {
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const entries = rawByImageId.get(expectedImage.imageId) ?? []
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if (entries.length === 0) {
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rejectedImages.push({
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imageId: expectedImage.imageId,
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reason: 'MISSING',
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issuePaths: [],
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issueCodes: [],
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})
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continue
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}
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if (entries.length > 1) {
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rejectedImages.push({
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imageId: expectedImage.imageId,
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reason: 'DUPLICATE',
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issuePaths: [],
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issueCodes: [],
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})
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continue
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}
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const parsed = aiImageFactResultSchema.safeParse(entries[0])
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if (!parsed.success) {
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rejectedImages.push({
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imageId: expectedImage.imageId,
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reason: 'INVALID_SHAPE',
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issuePaths: parsed.error.issues
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.slice(0, 8)
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.map((issue) => issue.path.join('.')),
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issueCodes: parsed.error.issues
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.slice(0, 8)
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.map((issue) => issue.code),
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})
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continue
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}
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const hasInvalidSkuReference = parsed.data.facts.some((fact) =>
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fact.relatedSkuIds.some((id) => !skuIds.has(id)),
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)
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if (hasInvalidSkuReference) {
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rejectedImages.push({
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imageId: expectedImage.imageId,
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reason: 'INVALID_SKU_REFERENCE',
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issuePaths: [],
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issueCodes: [],
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})
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continue
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}
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const factIds = parsed.data.facts.map((fact) => fact.factId)
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if (new Set(factIds).size !== factIds.length) {
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rejectedImages.push({
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imageId: expectedImage.imageId,
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reason: 'DUPLICATE_FACT_ID',
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issuePaths: [],
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issueCodes: [],
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})
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continue
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}
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acceptedImages.push(parsed.data)
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}
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return {
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acceptedImages,
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rejectedImages,
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unexpectedImageIds,
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unreadableImageCount,
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}
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}
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@ -367,6 +460,92 @@ function createTrustedImageFacts(
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})
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}
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function createFactSnapshot(
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images: BomCostingMultimodalInputImage[],
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completedByIdentity: Map<
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string,
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BomCostingSourceFactSnapshot['imageFacts'][number]
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>,
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extractedAt = new Date().toISOString(),
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) {
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return bomCostingSourceFactSnapshotSchema.parse({
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schemaVersion: BOM_COSTING_FACT_SCHEMA_VERSION,
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imageFacts: images
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.map((image) =>
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completedByIdentity.get(buildBomCostingImageIdentity(image.url)),
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)
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.filter(Boolean),
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extractedAt,
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})
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}
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function describeRejectedFactBatch(
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batchIndex: number,
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acceptedImageCount: number,
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rejectedImageCount: number,
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) {
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if (acceptedImageCount === 0) {
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return `第 ${batchIndex} 批有 ${rejectedImageCount} 张图片结果格式不完整，没有产生可保存的图片事实`
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}
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return (
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`第 ${batchIndex} 批有 ${rejectedImageCount} 张图片结果格式不完整；` +
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`其余 ${acceptedImageCount} 张已保存，继续处理时只分析未完成图片`
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)
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}
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function logRejectedFactBatch(input: {
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batchIndex: number
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totalBatches: number
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batchImageCount: number
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model: string
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callId: string | null
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finishReason: string | null
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rejectedImages: RejectedFactImage[]
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unexpectedImageIds: string[]
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unreadableImageCount: number
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}) {
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console.warn('[BOM costing] image fact contract partially rejected', {
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batchIndex: input.batchIndex,
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totalBatches: input.totalBatches,
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imageCount: input.batchImageCount,
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model: input.model,
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callId: input.callId,
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finishReason: input.finishReason,
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rejectedImages: input.rejectedImages.slice(0, 8),
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unexpectedImageIds: input.unexpectedImageIds.slice(0, 8),
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unreadableImageCount: input.unreadableImageCount,
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})
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}
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function logUnreadableFactEnvelope(input: {
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batchIndex: number
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totalBatches: number
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batchImageCount: number
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model: string
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callId: string | null
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finishReason: string | null
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error: unknown
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}) {
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console.warn('[BOM costing] image fact envelope rejected', {
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batchIndex: input.batchIndex,
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totalBatches: input.totalBatches,
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imageCount: input.batchImageCount,
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model: input.model,
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callId: input.callId,
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finishReason: input.finishReason,
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issueCount: input.error instanceof z.ZodError ? input.error.issues.length : null,
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issuePaths:
|
||||
input.error instanceof z.ZodError
|
||||
? input.error.issues.slice(0, 8).map((issue) => issue.path.join('.'))
|
||||
: [],
|
||||
issueCodes:
|
||||
input.error instanceof z.ZodError
|
||||
? input.error.issues.slice(0, 8).map((issue) => issue.code)
|
||||
: [],
|
||||
errorName: input.error instanceof Error ? input.error.name : 'UnknownError',
|
||||
})
|
||||
}
|
||||
|
||||
function chunk<T>(items: T[], size: number): T[][] {
|
||||
const result: T[][] = []
|
||||
for (let index = 0; index < items.length; index += size) {
|
||||
@ -495,82 +674,97 @@ export async function extractBomCostingImageFacts(input: {
|
||||
maxTokens,
|
||||
timeoutMs,
|
||||
})
|
||||
let parsed: z.infer<typeof aiFactBatchPayloadSchema>
|
||||
let rawImages: unknown[]
|
||||
try {
|
||||
parsed = aiFactBatchPayloadSchema.parse(
|
||||
rawImages = aiFactBatchEnvelopeSchema.parse(
|
||||
parseBomCostingJsonObject(call.content),
|
||||
)
|
||||
assertExactImageCoverage(batchImages, parsed.images)
|
||||
assertFactSkuReferences(input.snapshot, parsed.images)
|
||||
).images
|
||||
} catch (error) {
|
||||
console.warn('[BOM costing] image fact contract rejected', {
|
||||
logUnreadableFactEnvelope({
|
||||
batchIndex,
|
||||
totalBatches: batches.length,
|
||||
imageCount: batchImages.length,
|
||||
batchImageCount: batchImages.length,
|
||||
model: call.model,
|
||||
callId: call.id,
|
||||
finishReason: call.finishReason,
|
||||
issueCount: error instanceof z.ZodError ? error.issues.length : null,
|
||||
issuePaths:
|
||||
error instanceof z.ZodError
|
||||
? error.issues.slice(0, 8).map((issue) => issue.path.join('.'))
|
||||
: [],
|
||||
issueCodes:
|
||||
error instanceof z.ZodError
|
||||
? error.issues.slice(0, 8).map((issue) => issue.code)
|
||||
: [],
|
||||
errorName: error instanceof Error ? error.name : 'UnknownError',
|
||||
error,
|
||||
})
|
||||
throw new AppError(
|
||||
ErrorCode.BOM_COSTING_INVALID_PROVIDER,
|
||||
`第 ${batchIndex} 批图片事实返回不完整，已完成批次不会重复分析`,
|
||||
`第 ${batchIndex} 批图片事实无法读取，本次没有产生可保存的图片结果`,
|
||||
)
|
||||
}
|
||||
|
||||
const trustedFacts = createTrustedImageFacts(batchImages, parsed.images)
|
||||
const validation = validateFactBatchImages(
|
||||
input.snapshot,
|
||||
batchImages,
|
||||
rawImages,
|
||||
)
|
||||
const acceptedImageIds = new Set(
|
||||
validation.acceptedImages.map((image) => image.imageId),
|
||||
)
|
||||
const acceptedBatchImages = batchImages.filter((image) =>
|
||||
acceptedImageIds.has(image.imageId),
|
||||
)
|
||||
const trustedFacts = createTrustedImageFacts(
|
||||
acceptedBatchImages,
|
||||
validation.acceptedImages,
|
||||
)
|
||||
trustedFacts.forEach((image) => {
|
||||
completedByIdentity.set(
|
||||
buildBomCostingImageIdentity(image.sourceImageUrl),
|
||||
image,
|
||||
)
|
||||
})
|
||||
analyzedImageCount += batchImages.length
|
||||
analyzedImageCount += trustedFacts.length
|
||||
if (call.id) callIds.push(call.id)
|
||||
promptTokens = addNullableNumber(promptTokens, call.promptTokens)
|
||||
completionTokens = addNullableNumber(
|
||||
completionTokens,
|
||||
call.completionTokens,
|
||||
)
|
||||
const snapshot = bomCostingSourceFactSnapshotSchema.parse({
|
||||
schemaVersion: BOM_COSTING_FACT_SCHEMA_VERSION,
|
||||
imageFacts: images
|
||||
.map((image) =>
|
||||
completedByIdentity.get(buildBomCostingImageIdentity(image.url)),
|
||||
)
|
||||
.filter(Boolean),
|
||||
extractedAt: new Date().toISOString(),
|
||||
})
|
||||
await input.onBatchCompleted?.({
|
||||
batchIndex,
|
||||
totalBatches: batches.length,
|
||||
batchImages,
|
||||
completedImageCount: completedByIdentity.size,
|
||||
totalImageCount: images.length,
|
||||
snapshot,
|
||||
call,
|
||||
})
|
||||
if (trustedFacts.length > 0) {
|
||||
await input.onBatchCompleted?.({
|
||||
batchIndex,
|
||||
totalBatches: batches.length,
|
||||
batchImages: acceptedBatchImages,
|
||||
completedImageCount: completedByIdentity.size,
|
||||
totalImageCount: images.length,
|
||||
snapshot: createFactSnapshot(images, completedByIdentity),
|
||||
call,
|
||||
})
|
||||
}
|
||||
|
||||
if (validation.rejectedImages.length > 0) {
|
||||
logRejectedFactBatch({
|
||||
batchIndex,
|
||||
totalBatches: batches.length,
|
||||
batchImageCount: batchImages.length,
|
||||
model: call.model,
|
||||
callId: call.id,
|
||||
finishReason: call.finishReason,
|
||||
rejectedImages: validation.rejectedImages,
|
||||
unexpectedImageIds: validation.unexpectedImageIds,
|
||||
unreadableImageCount: validation.unreadableImageCount,
|
||||
})
|
||||
throw new AppError(
|
||||
ErrorCode.BOM_COSTING_INVALID_PROVIDER,
|
||||
describeRejectedFactBatch(
|
||||
batchIndex,
|
||||
trustedFacts.length,
|
||||
validation.rejectedImages.length,
|
||||
),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
const snapshot = bomCostingSourceFactSnapshotSchema.parse({
|
||||
schemaVersion: BOM_COSTING_FACT_SCHEMA_VERSION,
|
||||
imageFacts: images.map((image) =>
|
||||
completedByIdentity.get(buildBomCostingImageIdentity(image.url)),
|
||||
),
|
||||
extractedAt:
|
||||
batches.length > 0
|
||||
? new Date().toISOString()
|
||||
: input.existingSnapshot?.extractedAt ?? new Date().toISOString(),
|
||||
})
|
||||
const snapshot = createFactSnapshot(
|
||||
images,
|
||||
completedByIdentity,
|
||||
batches.length > 0
|
||||
? new Date().toISOString()
|
||||
: input.existingSnapshot?.extractedAt ?? new Date().toISOString(),
|
||||
)
|
||||
return {
|
||||
snapshot,
|
||||
trace: {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user