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https://wget.la/https://github.com/Wxw-Gu/WechatExplorer
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Wxw-Gu
1337bcb7de
- 图片文字索引性能与进度诚实化 - 问问微信：证据卡区分「消息类型」与「派生来源」，派生命中内容自报来源 - 问问微信：回答规则禁止未真实执行的多轮承诺 - 本地图片文字识别：支持 macOS 系统 OCR（Apple Vision）
390 lines
14 KiB
TypeScript
390 lines
14 KiB
TypeScript
/**
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* 图片 OCR 来源语义的 **deterministic synthetic E2E**。
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*
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* 硬要求是"不依赖真实线上 AI 模型也能 PASS"，所以这里把两个外部边界**确定性**地固定住：
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* - WCDB（chat-service）→ 用合成联系人 / 合成消息；
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* - Knowledge 检索 → 用 fake 直接返回合成证据（形状与真实 `KnowledgeEvidence` 一致，
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* 包括**未清理**的 `searchable_text`，用来验证内部前缀确实被剥掉）。
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*
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* 链路上真正的被测代码仍然是生产实现：
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* LocalQueryApiService.search() ← 真实 scope 解析 / 证据映射 / 前缀剥离
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* createLocalQueryToolExecutor() ← 真实 Tool 执行
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* QueryAgentService.run() ← 真实 Agent 循环 / tool result 组装
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*
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* 断言的 10 项对应需求：FOUND=YES / sourceKind=image / derived source=image_ocr /
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* conversation scope=技术交流群 / Evidence messageRef=原始图片消息 /
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* Evidence UI=图片文字 / jump target=原始图片消息 / 不产生虚构 OCR 消息。
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*/
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import { beforeEach, describe, expect, it, vi } from 'vitest'
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import { decodeMessageRef } from '../../src/shared/local-query-api'
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process.env.TZ = 'Asia/Shanghai'
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const GROUP_MD5 = 'md5-tech-group'
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const GROUP_NAME = '技术交流群'
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const IMAGE_MESSAGE_ID = 'local:9001'
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const TEXT_MESSAGE_ID = 'local:9002'
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const OCR_TEXT = 'OpenAI ChatGPT Plus $20 Pro $200'
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/** Knowledge 侧的原始 searchable_text：带内部标签，绝不该出现在 Evidence 里。 */
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const RAW_SEARCHABLE = `图片文字：${OCR_TEXT}`
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const fixture = vi.hoisted(() => {
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const imageTimestamp = Date.parse('2026-09-03T14:32:00+08:00')
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const textTimestamp = Date.parse('2026-09-03T14:30:00+08:00')
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return {
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imageTimestamp,
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textTimestamp,
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contacts: [
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{
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m_nsUsrName: 'wxid-tech-group',
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m_nsNickName: '技术交流群',
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md5: 'md5-tech-group',
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type: 'group' as const
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}
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],
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messages: [
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{
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id: '9002',
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localId: '9002',
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from: 'user',
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type: '文本',
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datetime: '2026/9/3 14:30:00',
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content: '今天正常讨论一下 API',
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isSender: false,
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name: '张三',
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createTime: Math.floor(textTimestamp / 1000)
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},
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{
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id: '9001',
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localId: '9001',
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from: 'user',
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type: '图片',
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datetime: '2026/9/3 14:32:00',
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content: '',
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contentData: { type: 'image', md5: 'image-md5-fixture', datName: 'dat-fixture' },
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isSender: false,
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name: '张三',
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createTime: Math.floor(imageTimestamp / 1000)
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}
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]
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}
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})
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const IMAGE_TIMESTAMP = fixture.imageTimestamp
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vi.mock('../../src/main/services/chat-service', () => ({
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isReady: () => true,
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listContactsAsync: vi.fn(async () => fixture.contacts),
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listMessagesAsync: vi.fn(async () => fixture.messages)
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}))
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import { LocalQueryApiService } from '../../src/main/services/local-query-api-service'
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import { createLocalQueryToolExecutor } from '../../src/main/services/local-query-tool-executor'
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import {
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QueryAgentService,
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type QueryAgentProvider
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} from '../../src/main/services/query-agent-service'
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/** 与真实 Knowledge 检索返回的证据形状一致（含原始未清理文本）。 */
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function syntheticKnowledgeEvidence() {
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return [
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{
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chunkId: 'chunk-1',
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conversationId: GROUP_MD5,
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startTime: IMAGE_TIMESTAMP,
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endTime: IMAGE_TIMESTAMP,
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messageId: IMAGE_MESSAGE_ID,
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senderId: 'fixture-member',
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sender: '张三',
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timestamp: IMAGE_TIMESTAMP,
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messageIds: [IMAGE_MESSAGE_ID],
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sourceKind: 'image' as const,
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text: RAW_SEARCHABLE,
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imageOcrText: OCR_TEXT,
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derivedSource: 'image_ocr' as const
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}
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]
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}
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function makeKnowledge() {
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return {
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search: vi.fn(async () => ({
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state: 'ready',
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evidence: syntheticKnowledgeEvidence(),
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conversationRetrieval: { totalMessages: 2, chunkCount: 1, complete: true },
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voiceCoverage: undefined
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})),
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requestCatchUp: vi.fn(() => ({ triggered: false, inProgress: false })),
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waitForIndexingComplete: vi.fn(async () => false),
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lastPassDurationMs: vi.fn(() => 0),
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beginInteractiveQuery: vi.fn(),
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endInteractiveQuery: vi.fn()
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} as never
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}
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const NOW = new Date('2026-09-16T09:00:00+08:00')
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describe('图片文字索引 synthetic E2E（确定性，不依赖真模型）', () => {
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let knowledge: ReturnType<typeof makeKnowledge>
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let service: LocalQueryApiService
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beforeEach(() => {
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knowledge = makeKnowledge()
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service = new LocalQueryApiService(knowledge, () => NOW)
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})
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it('Question Tool 链路：命中图片文字的 Evidence 指向原始图片消息，且不泄露内部前缀', async () => {
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const result = await service.search({
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target: { query: GROUP_NAME },
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timeRange: { kind: 'all' },
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query: 'ChatGPT 价格',
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variants: ['ChatGPT']
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})
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expect(result.status).toBe('completed')
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// FOUND = YES
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expect(result.evidenceCount).toBe(1)
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expect(result.evidence).toHaveLength(1)
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const evidence = result.evidence![0]
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// sourceKind = image（原始消息是什么）
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expect(evidence.sourceKind).toBe('image')
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// derived source = image_ocr（靠什么搜到的）
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expect(evidence.derivedSource).toBe('image_ocr')
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// OCR 片段只作命中解释
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expect(evidence.imageOcrText).toBe(OCR_TEXT)
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// conversation scope = 技术交流群：target 把检索范围真正收敛到这一个会话
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expect(result.target).toEqual({ displayName: GROUP_NAME, type: 'group' })
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expect(evidence.conversationName).toBe(GROUP_NAME)
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expect(evidence.conversationType).toBe('group')
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expect(knowledge.search).toHaveBeenCalledTimes(2)
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for (const call of knowledge.search.mock.calls) {
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expect((call[0] as { conversationIds?: string[] }).conversationIds).toEqual([GROUP_MD5])
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}
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// sender / createTime 来自原始消息
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expect(evidence.sender).toBe('张三')
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expect(evidence.timestamp).toBe(IMAGE_TIMESTAMP)
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// Evidence messageRef = 原始 image message（jump target 就是它）。
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// 注意 `local:` 只是 WCDB 侧的本地 id 装饰，不属于身份本身，所以还原后是裸 id。
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const identity = decodeMessageRef(evidence.messageRef)
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expect(identity).toEqual({ conversationId: GROUP_MD5, messageId: '9001' })
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// 不能产生"OCR 消息"：证据集合里不存在任何非原始消息的身份
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expect(result.evidence!.every((item) => decodeMessageRef(item.messageRef)?.messageId === '9001')).toBe(true)
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expect(result.evidence!.some((item) => decodeMessageRef(item.messageRef)?.messageId === '9002')).toBe(false)
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// 内部前缀绝不泄露给用户（模型侧与 UI 侧都不允许）
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expect(evidence.text).not.toContain('图片文字：')
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expect(evidence.text).not.toContain('OCR:')
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expect(evidence.text).not.toContain('system-ocr')
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expect(evidence.text).toContain(OCR_TEXT)
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})
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it('Query Agent 链路：来源语义进入 tool result，OCR 片段不进模型上下文', async () => {
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const executor = createLocalQueryToolExecutor(service)
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const responses: Array<Awaited<ReturnType<QueryAgentProvider['chatWithTools']>>> = [
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{
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success: true,
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toolCalls: [
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{
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id: 'call-1',
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name: 'search_messages',
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arguments: JSON.stringify({
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target: { query: GROUP_NAME },
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timeRange: { kind: 'all' },
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queries: ['ChatGPT 价格']
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})
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}
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]
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},
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{ success: true, data: '找到了：技术交流群发过一张 ChatGPT 价格的图片。' }
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]
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const provider: QueryAgentProvider = {
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getRuntimeConfig: () => ({
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configured: true,
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providerName: 'Fixture Provider',
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model: 'fixture-model',
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modelName: 'Fixture Model'
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}),
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chatWithTools: vi.fn(async () => responses.shift() || { success: true, data: 'done' })
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}
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const agentResult = await new QueryAgentService(provider, executor).run(
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'技术交流群之前是不是发过 ChatGPT 价格的图片？'
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)
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// 模型实际看到的 tool result
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const toolMessage = vi
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.mocked(provider.chatWithTools)
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.mock.calls[1]?.[0].find((message) => message.role === 'tool')
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const presented = JSON.parse(String(toolMessage?.content)) as Record<string, any>
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const presentedEvidence = presented.evidence?.[0]
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expect(presentedEvidence.sourceKind).toBe('image')
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expect(presentedEvidence.derivedSource).toBe('image_ocr')
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// 片段的内容已经在 text 里，不再重复塞进上下文（避免无谓 token）。
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expect(presentedEvidence.imageOcrText).toBeUndefined()
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expect(presentedEvidence.text).not.toContain('图片文字：')
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// messageRef 指向原始图片消息（模型只拿到 opaque ref，看不到会话身份）。
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expect(decodeMessageRef(presentedEvidence.messageRef)).toEqual({
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conversationId: GROUP_MD5,
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messageId: '9001'
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})
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// 暴露给 UI 的证据保留来源语义与片段
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const uiEvidence = agentResult.evidence.find((item) => item.messageRef === presentedEvidence.messageRef)
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expect(uiEvidence?.messageType).toBe('image')
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expect(uiEvidence?.derivedSource).toBe('image_ocr')
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expect(uiEvidence?.imageOcrText).toBe(OCR_TEXT)
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expect(uiEvidence?.text).not.toContain('图片文字：')
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expect(uiEvidence?.conversationName).toBe(GROUP_NAME)
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})
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})
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describe('partial coverage honesty（确定性，不依赖真模型）', () => {
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const NOT_INDEXED_KEYWORD = 'TRACE_NOT_YET_INDEXED_IMAGE'
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function partialImageCoverage() {
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return {
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totalImageMessages: 100,
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processed: 30,
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indexed: 28,
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empty: 2,
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missing: 0,
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failed: 0,
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pending: 70,
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established: true,
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complete: false,
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countedAt: Date.parse('2026-09-16T08:00:00+08:00')
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}
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}
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beforeEach(() => {
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vi.clearAllMocks()
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})
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it('已处理的 30 张里搜不到关键词时，覆盖度必须带上"不能断言没有"的语义', async () => {
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const knowledge = makeKnowledge()
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// 关键：已建立的 30 张里确实没有这个关键词 → 检索结果为空。
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knowledge.search.mockImplementation(async () => ({
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state: 'ready',
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evidence: [],
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// 文字索引这一维是**完整**的（噪音）：证明图片维度不会被文字维度"带过"。
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indexLatestAt: NOW.getTime(),
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sourceLatestAt: NOW.getTime(),
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conversationRetrieval: { totalMessages: 2, chunkCount: 1, complete: true },
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voiceCoverage: undefined
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}))
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const service = new LocalQueryApiService(knowledge, () => NOW)
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// 图片文字索引建立过，但只完成 30 / 100。
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service.setImageTextCoverageProvider(() => partialImageCoverage())
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const result = await service.search({
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target: { query: GROUP_NAME },
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timeRange: { kind: 'all' },
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query: NOT_INDEXED_KEYWORD
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})
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expect(result.status).toBe('completed')
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expect(result.evidenceCount).toBe(0)
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// 文字索引这一维是完整的（噪音），图片这一维才是缺口。
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expect(result.coverage).toEqual({ state: 'complete' })
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expect(result.imageOcrCoverage).toMatchObject({
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state: 'partial',
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totalImageMessages: 100,
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processed: 30,
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pending: 70
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})
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const summary = result.imageOcrCoverage!.summary
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expect(summary).toContain('30')
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expect(summary).toContain('100')
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expect(summary).toContain('不能因为没搜到就回答')
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// 覆盖度必须真的进入 Query Agent 的上下文，而不是只留在 Engine 里。
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const executor = createLocalQueryToolExecutor(service)
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const responses: Array<Awaited<ReturnType<QueryAgentProvider['chatWithTools']>>> = [
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{
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success: true,
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toolCalls: [
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{
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id: 'call-1',
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name: 'search_messages',
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arguments: JSON.stringify({
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target: { query: GROUP_NAME },
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timeRange: { kind: 'all' },
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queries: [NOT_INDEXED_KEYWORD]
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})
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}
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]
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},
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{
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success: true,
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data: '图片文字索引目前只处理 30 / 100 条图片消息，当前结果不完整，无法确认全部历史图片。'
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}
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]
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const provider: QueryAgentProvider = {
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getRuntimeConfig: () => ({
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configured: true,
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providerName: 'Fixture Provider',
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model: 'fixture-model',
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modelName: 'Fixture Model'
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}),
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chatWithTools: vi.fn(async () => responses.shift() || { success: true, data: 'done' })
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}
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const agentResult = await new QueryAgentService(provider, executor).run(
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`之前是不是有张图片写着 ${NOT_INDEXED_KEYWORD}？`
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)
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const calls = vi.mocked(provider.chatWithTools).mock.calls
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// 提示词里写死了零结果诚实性规则（不能指望模型自己想到）。
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expect(String(calls[0]?.[0]?.[0]?.content)).toContain('imageOcrCoverage')
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const presented = JSON.parse(
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String(calls[1]?.[0].find((message) => message.role === 'tool')?.content)
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) as Record<string, any>
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expect(presented.evidenceCount).toBe(0)
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expect(presented.imageOcrCoverage).toMatchObject({
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state: 'partial',
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totalImageMessages: 100,
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processed: 30,
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pending: 70
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})
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expect(presented.imageOcrCoverage.summary).toContain('不能因为没搜到就回答')
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// 最终回答本身必须是"覆盖不完整"，不是"没有"。
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expect(agentResult.answer).toContain('30')
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expect(agentResult.answer).toContain('100')
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expect(agentResult.answer).not.toBe('没有')
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})
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it('图片索引完整时不下发零结果约束（避免模型机械附加警告）', async () => {
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const knowledge = makeKnowledge()
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knowledge.search.mockImplementation(async () => ({
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state: 'ready',
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evidence: [],
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conversationRetrieval: { totalMessages: 2, chunkCount: 1, complete: true },
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voiceCoverage: undefined
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}))
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const service = new LocalQueryApiService(knowledge, () => NOW)
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service.setImageTextCoverageProvider(() => ({
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...partialImageCoverage(),
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processed: 100,
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indexed: 98,
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empty: 2,
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pending: 0,
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complete: true
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}))
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const result = await service.search({
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target: { query: GROUP_NAME },
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timeRange: { kind: 'all' },
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query: NOT_INDEXED_KEYWORD
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})
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expect(result.imageOcrCoverage?.state).toBe('complete')
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expect(result.imageOcrCoverage?.summary).not.toContain('不能因为没搜到就回答')
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})
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})
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