mirror of
https://wget.la/https://github.com/Wxw-Gu/WechatExplorer
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637 lines
21 KiB
TypeScript
637 lines
21 KiB
TypeScript
import { describe, expect, it } from 'vitest'
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import type {
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AiSearchFinalEvidence,
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AiSearchPipelineResult,
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AiSearchTimeRange
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} from '../../src/shared/ai-search'
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import type { KnowledgeRuntimeStatus } from '../../src/shared/knowledge'
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import type { Contact } from '../../src/shared/types'
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import {
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contactLabel,
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formatBytes,
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formatDuration,
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formatEvidenceTimestamp,
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formatMeasuredDuration,
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formatSearchTraceOverview,
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knowledgeStateLabel
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} from '../../src/renderer/src/components/search/searchFormatters'
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import {
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createSearchCacheRecord,
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mapCacheRecordToResult,
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mapEvidenceSenderNames,
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mapPipelineEvidence,
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mapPipelineEvidenceItem,
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mapPipelineResultToRendererResult,
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mapSearchResultToTrace
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} from '../../src/renderer/src/components/search/searchMappers'
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import {
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createSearchResultResetState,
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resolveSearchResultViewTransition
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} from '../../src/renderer/src/components/search/searchState'
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import { createSearchRequestContext } from '../../src/renderer/src/components/search/searchUtils'
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import type {
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AISearchCacheRecord,
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EvidenceItem,
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SearchTrace
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} from '../../src/renderer/src/components/search/searchTypes'
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import {
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aiSearchContact,
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aiSearchGroup,
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makeSearchResult
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} from '../component/support/ai-search-fixtures'
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const makeFinalEvidence = (
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index: number,
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overrides: Partial<AiSearchFinalEvidence> = {}
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): AiSearchFinalEvidence => ({
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id: `E${index}`,
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chunkId: `chunk-${index}`,
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conversationId: aiSearchContact.md5,
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conversationName: aiSearchContact.m_nsNickName,
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conversationType: aiSearchContact.type,
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startTime: 1_700_000_000_000 + index * 1_000,
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endTime: 1_700_000_000_000 + index * 1_000,
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messageId: `message-${index}`,
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senderId: `sender-${index}`,
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sender: `发送者 ${index}`,
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timestamp: 1_700_000_000_000 + index * 1_000,
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messageIds: [`message-${index}`],
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sourceKind: 'text',
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text: `证据 ${index}`,
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...overrides
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})
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const makeKnowledgeStatus = (state: KnowledgeRuntimeStatus['state']): KnowledgeRuntimeStatus => ({
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accountId: 'account-1',
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state,
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indexedMessageCount: 0,
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indexedChunkCount: 0,
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sourceMessageCount: null,
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processedMessages: 0,
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totalMessages: null,
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estimatedRemainingMs: null,
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databaseBytes: 0,
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walBytes: 0,
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shmBytes: 0
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})
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const makeTrace = (overrides: Partial<SearchTrace> = {}): SearchTrace => ({
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knowledgeMessages: 20,
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retrievedEvidence: 10,
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finalEvidence: 8,
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timings: {
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totalMs: 1_250,
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knowledgeSearchMs: 250,
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aiGenerationMs: 1_000
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} as SearchTrace['timings'],
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contextEvidence: 6,
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inputTokensEstimated: false,
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aggregation: {
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messageCount: 8,
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peopleCount: 1,
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conversationCount: 1,
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people: [],
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conversations: []
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},
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invalidCitationIds: [],
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agent: { mode: 'agent', toolCalls: 0, trace: [] },
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...overrides
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})
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describe('AI Search request context', () => {
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it('trims only the submitted query while keeping cache query normalization unchanged', () => {
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const context = createSearchRequestContext({
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query: ' Mixed Case 问题 ',
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scope: 'global',
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range: '30d'
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})
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expect(context.normalizedQuery).toBe('Mixed Case 问题')
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expect(context.cacheKey).toBe(JSON.stringify(['global', '', '30d', 'mixed case 问题']))
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})
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it.each(['global', 'groups', 'contacts'] as const)(
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'does not attach a conversation to %s scope',
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(scope) => {
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const context = createSearchRequestContext({
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query: '范围问题',
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scope,
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range: '7d',
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activeContactMd5: 'ignored-contact'
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})
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expect(context.conversationId).toBeUndefined()
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expect(context.cacheKey).toBe(JSON.stringify([scope, '', '7d', '范围问题']))
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}
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)
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it('uses the active contact for conversation request and cache identity', () => {
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const context = createSearchRequestContext({
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query: '会话问题',
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scope: 'conversation',
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range: 'today',
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activeContactMd5: aiSearchContact.md5
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})
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expect(context.conversationId).toBe(aiSearchContact.md5)
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expect(context.cacheKey).toBe(
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JSON.stringify(['conversation', aiSearchContact.md5, 'today', '会话问题'])
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)
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})
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it('keeps a missing conversation undefined while using the empty cache slot', () => {
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const context = createSearchRequestContext({
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query: '未选择会话',
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scope: 'conversation',
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range: 'all'
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})
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expect(context.conversationId).toBeUndefined()
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expect(context.cacheKey).toBe(JSON.stringify(['conversation', '', 'all', '未选择会话']))
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})
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it('uses retry range and retry time override when both are provided', () => {
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const currentOverride: AiSearchTimeRange = {
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label: '近 30 天',
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reason: '当前选择',
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source: 'user_selected'
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}
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const retryOverride: AiSearchTimeRange = {
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label: '全部历史',
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reason: '用户主动扩大到全部历史',
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source: 'user_retry'
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}
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const context = createSearchRequestContext({
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query: '重试问题',
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scope: 'global',
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range: '30d',
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timeRangeOverride: currentOverride,
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retry: { range: 'all', timeRangeOverride: retryOverride }
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})
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expect(context.effectiveRange).toBe('all')
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expect(context.effectiveTimeRangeOverride).toBe(retryOverride)
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expect(context.cacheKey).toBe(JSON.stringify(['global', '', 'all', '重试问题']))
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})
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it('falls back to the current time override when retry does not provide one', () => {
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const currentOverride: AiSearchTimeRange = {
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startTime: 123,
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label: '近 30 天',
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reason: '用户在界面选择的时间范围',
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source: 'user_selected'
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}
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const context = createSearchRequestContext({
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query: '保留当前时间范围',
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scope: 'global',
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range: '7d',
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timeRangeOverride: currentOverride,
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retry: { range: '30d', timeRangeOverride: undefined }
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})
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expect(context.effectiveRange).toBe('30d')
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expect(context.effectiveTimeRangeOverride).toBe(currentOverride)
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})
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it('keeps the current range and undefined override when no retry exists', () => {
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const context = createSearchRequestContext({
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query: '普通问题',
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scope: 'global',
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range: '7d'
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})
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expect(context.effectiveRange).toBe('7d')
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expect(context.effectiveTimeRangeOverride).toBeUndefined()
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})
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})
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describe('AI Search workspace pure formatters', () => {
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it('formats byte counts exactly as the workspace did', () => {
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expect(formatBytes(0)).toBe('0 B')
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expect(formatBytes(512)).toBe('512 B')
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expect(formatBytes(1_536)).toBe('1.5 KB')
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expect(formatBytes(2 * 1024 ** 3)).toBe('2.0 GB')
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})
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it('formats measured and unmeasured durations exactly as the workspace did', () => {
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expect(formatDuration(999)).toBe('999ms')
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expect(formatDuration(1_250)).toBe('1.3s')
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expect(formatMeasuredDuration(undefined)).toBe('未测量')
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expect(formatMeasuredDuration(1_000)).toBe('1.0s')
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})
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it('formats evidence timestamps with the existing zh-CN locale options', () => {
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const timestamp = 1_700_000_001_000
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expect(formatEvidenceTimestamp(timestamp)).toBe(
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new Date(timestamp).toLocaleString('zh-CN', { hour12: false })
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)
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})
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it('keeps every knowledge runtime state label unchanged', () => {
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expect(knowledgeStateLabel(null)).toBe('读取中')
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expect(knowledgeStateLabel(makeKnowledgeStatus('unavailable'))).toBe('未建立')
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expect(knowledgeStateLabel(makeKnowledgeStatus('building'))).toBe('建立中')
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expect(knowledgeStateLabel(makeKnowledgeStatus('syncing'))).toBe('增量同步')
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expect(knowledgeStateLabel(makeKnowledgeStatus('ready'))).toBe('已同步')
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expect(knowledgeStateLabel(makeKnowledgeStatus('error'))).toBe('异常')
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})
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it('keeps the existing contact label fallback order', () => {
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expect(contactLabel(aiSearchContact)).toBe('测试会话')
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expect(contactLabel({ ...aiSearchContact, m_nsNickName: '' })).toBe('测试联系人')
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expect(
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contactLabel({ ...aiSearchContact, m_nsNickName: '', remark: '', wechatNickname: '' })
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).toBe('wxid_fixture')
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expect(contactLabel(null)).toBe('未选择会话')
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})
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it('formats the compact search trace overview without changing labels or units', () => {
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expect(formatSearchTraceOverview(makeTrace())).toEqual({
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totalDuration: '1.3s',
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knowledgeDuration: '250ms',
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aiDuration: '1.0s',
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contextEvidence: '6 条'
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})
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})
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})
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describe('AI Search pipeline evidence mapping', () => {
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it('reuses the existing renderer contact object when the conversation is loaded', () => {
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const item = makeFinalEvidence(1)
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const mapped = mapPipelineEvidenceItem(item, new Map([[aiSearchContact.md5, aiSearchContact]]))
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expect(mapped.contact).toBe(aiSearchContact)
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expect(mapped).toEqual({
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evidenceId: 'E1',
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sourceKind: 'text',
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contact: aiSearchContact,
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message: {
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id: 'message-1',
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from: 'sender-1',
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type: '检索消息',
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datetime: formatEvidenceTimestamp(item.timestamp),
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content: '证据 1',
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isSender: false,
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name: '发送者 1',
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senderId: 'sender-1',
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createTime: Math.floor(item.timestamp / 1_000)
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}
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})
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})
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it('creates the existing group fallback and voice presentation for an unloaded conversation', () => {
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const item = makeFinalEvidence(2, {
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conversationId: 'missing@chatroom',
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conversationName: '未加载群',
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conversationType: 'group',
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sourceKind: 'voice',
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sender: '我'
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})
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const mapped = mapPipelineEvidenceItem(item, new Map())
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expect(mapped.contact).toEqual({
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md5: 'missing@chatroom',
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m_nsUsrName: 'missing@chatroom',
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m_nsNickName: '未加载群',
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type: 'group'
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})
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expect(mapped.message.type).toBe('语音转写')
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expect(mapped.message.isSender).toBe(true)
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})
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it('creates the existing user fallback and sender default when senderId is absent', () => {
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const item = makeFinalEvidence(3, {
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conversationId: 'missing-user',
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conversationName: '未加载联系人',
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conversationType: 'user',
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senderId: undefined
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})
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const mapped = mapPipelineEvidenceItem(item, new Map())
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expect(mapped.contact.type).toBe('user')
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expect(mapped.message.from).toBe('user')
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expect(mapped.message.senderId).toBeUndefined()
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})
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it('maps a collection in order and builds sender names with the existing overwrite behavior', () => {
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const items = [
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makeFinalEvidence(1, { senderId: 'same-sender', sender: '旧名称' }),
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makeFinalEvidence(2, { senderId: 'same-sender', sender: '新名称' }),
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makeFinalEvidence(3, { senderId: undefined, sender: '无 ID' })
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]
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const mapped = mapPipelineEvidence(items, [aiSearchContact])
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expect(mapped.map((item) => item.evidenceId)).toEqual(['E1', 'E2', 'E3'])
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expect(mapEvidenceSenderNames(mapped)).toEqual({ 'same-sender': '新名称' })
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})
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})
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describe('AI Search trace and cache mapping', () => {
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it('maps a pipeline result into the common Renderer state without mixing collection senders', () => {
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const finalEvidence = makeFinalEvidence(1, { senderId: 'final-sender', sender: '总结发送者' })
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const collectionEvidence = makeFinalEvidence(2, {
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senderId: 'collection-sender',
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sender: '浏览发送者'
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})
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const result = makeSearchResult({
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evidence: [finalEvidence],
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evidenceCollection: [finalEvidence, collectionEvidence]
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})
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const mapped = mapPipelineResultToRendererResult(result, [aiSearchContact])
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expect(mapped.evidence.map((item) => item.evidenceId)).toEqual(['E1'])
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expect(mapped.evidenceCollection.map((item) => item.evidenceId)).toEqual(['E1', 'E2'])
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expect(mapped.senderNames).toEqual({ 'final-sender': '总结发送者' })
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expect(mapped.messageCount).toBe(result.knowledge.totalMessages)
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expect(mapped.searchTrace).toEqual(mapSearchResultToTrace(result, 1))
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})
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it('falls back to Final Evidence only when the runtime collection is missing', () => {
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const evidence = [makeFinalEvidence(1)]
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const result = makeSearchResult({ evidence })
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;(
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result as AiSearchPipelineResult & { evidenceCollection?: AiSearchFinalEvidence[] }
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).evidenceCollection = undefined
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const mapped = mapPipelineResultToRendererResult(result, [aiSearchContact])
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expect(mapped.evidenceCollection).toEqual(mapped.evidence)
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})
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it('preserves an explicitly empty Evidence Collection without falling back', () => {
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const result = makeSearchResult({ evidence: [makeFinalEvidence(1)], evidenceCollection: [] })
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const mapped = mapPipelineResultToRendererResult(result, [aiSearchContact])
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expect(mapped.evidence).toHaveLength(1)
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expect(mapped.evidenceCollection).toEqual([])
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})
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it('maps pipeline trace fields and preserves the original default values', () => {
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const result: AiSearchPipelineResult = makeSearchResult()
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expect(mapSearchResultToTrace(result, 4)).toEqual({
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knowledgeMessages: 20,
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retrievedEvidence: 0,
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finalEvidence: 4,
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timings: result.timings,
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contextEvidence: 0,
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inputTokens: undefined,
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inputTokensEstimated: false,
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aggregation: result.aggregation,
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invalidCitationIds: [],
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agent: result.agent,
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voiceCoverage: undefined
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})
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})
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it('maps AI token, citation, and voice coverage details without transforming them', () => {
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const result: AiSearchPipelineResult = makeSearchResult()
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result.ai = {
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providerName: 'provider',
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modelName: 'model',
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inputTokens: 321,
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inputTokensEstimated: true
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}
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result.citationValidation = { status: 'sanitized', invalidCitationIds: ['E9'] }
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result.knowledge.voiceCoverage = {
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voiceMessageCount: 3,
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transcribedVoiceCount: 2,
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failedVoiceCount: 1,
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voiceCoverageComplete: true
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}
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const trace = mapSearchResultToTrace(result, 2)
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expect(trace.inputTokens).toBe(321)
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expect(trace.inputTokensEstimated).toBe(true)
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expect(trace.invalidCitationIds).toEqual(['E9'])
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expect(trace.voiceCoverage).toBe(result.knowledge.voiceCoverage)
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})
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it('maps a current cache record and limits only the visible collection to one page', () => {
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const evidence = mapPipelineEvidence([makeFinalEvidence(1)], [aiSearchContact])
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const evidenceCollection = mapPipelineEvidence(
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Array.from({ length: 10 }, (_, index) => makeFinalEvidence(index + 1)),
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[aiSearchContact]
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)
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const cached: AISearchCacheRecord = {
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version: 3,
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key: 'cache-key',
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createdAt: 123,
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answer: '缓存答案',
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evidence,
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evidenceCollection,
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senderNames: { 'sender-1': '发送者 1' },
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messageCount: 99
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}
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const mapped = mapCacheRecordToResult(cached, '恢复问题', 8)
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expect(mapped).toEqual({
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resultQuery: '恢复问题',
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answer: '缓存答案',
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evidence,
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evidenceCollection,
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visibleEvidenceCount: 8,
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senderNames: { 'sender-1': '发送者 1' },
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messageCount: 99,
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cachedAt: 123
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})
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})
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it('falls back to legacy cache evidence when evidenceCollection is absent', () => {
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const evidence = mapPipelineEvidence([makeFinalEvidence(1)], [aiSearchContact])
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const cached: AISearchCacheRecord = {
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version: 3,
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key: 'legacy-cache-key',
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createdAt: 456,
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answer: '旧缓存',
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evidence,
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senderNames: {},
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messageCount: 1
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}
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const mapped = mapCacheRecordToResult(cached, '旧问题', 8)
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expect(mapped.evidenceCollection).toBe(evidence)
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expect(mapped.visibleEvidenceCount).toBe(1)
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})
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it('creates the same compact version 3 cache record as the workspace did', () => {
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const fullContact: Contact = { ...aiSearchGroup, avatar: 'avatar', remark: '不应写入缓存' }
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const evidence: EvidenceItem[] = [
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{
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evidenceId: 'E1',
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sourceKind: 'voice',
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contact: fullContact,
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message: {
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id: 'message-1',
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from: 'sender-1',
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type: '语音转写',
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datetime: '2023/11/14 22:13:21',
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content: '证据 1',
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isSender: false,
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name: '发送者 1',
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senderId: 'sender-1',
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createTime: 1_700_000_001,
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sessionId: '不应写入缓存'
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}
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}
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]
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expect(
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createSearchCacheRecord({
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key: 'cache-key',
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createdAt: 789,
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answer: '答案',
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evidence,
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evidenceCollection: evidence,
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senderNames: { 'sender-1': '发送者 1' },
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messageCount: 20
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})
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).toEqual({
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version: 3,
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key: 'cache-key',
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createdAt: 789,
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answer: '答案',
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evidence: [
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{
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evidenceId: 'E1',
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contact: {
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md5: fullContact.md5,
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m_nsUsrName: fullContact.m_nsUsrName,
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m_nsNickName: fullContact.m_nsNickName,
|
|
type: 'group',
|
|
avatar: 'avatar'
|
|
},
|
|
message: {
|
|
id: 'message-1',
|
|
from: 'sender-1',
|
|
type: '语音转写',
|
|
datetime: '2023/11/14 22:13:21',
|
|
content: '证据 1',
|
|
isSender: false,
|
|
name: '发送者 1',
|
|
senderId: 'sender-1',
|
|
localId: undefined,
|
|
serverId: undefined,
|
|
createTime: 1_700_000_001
|
|
}
|
|
}
|
|
],
|
|
evidenceCollection: [
|
|
{
|
|
evidenceId: 'E1',
|
|
contact: {
|
|
md5: fullContact.md5,
|
|
m_nsUsrName: fullContact.m_nsUsrName,
|
|
m_nsNickName: fullContact.m_nsNickName,
|
|
type: 'group',
|
|
avatar: 'avatar'
|
|
},
|
|
message: {
|
|
id: 'message-1',
|
|
from: 'sender-1',
|
|
type: '语音转写',
|
|
datetime: '2023/11/14 22:13:21',
|
|
content: '证据 1',
|
|
isSender: false,
|
|
name: '发送者 1',
|
|
senderId: 'sender-1',
|
|
localId: undefined,
|
|
serverId: undefined,
|
|
createTime: 1_700_000_001
|
|
}
|
|
}
|
|
],
|
|
senderNames: { 'sender-1': '发送者 1' },
|
|
messageCount: 20
|
|
})
|
|
})
|
|
})
|
|
|
|
describe('AI Search result view transition', () => {
|
|
it('maps no Evidence to the range-specific insufficient message and ignores pipeline error', () => {
|
|
const result = makeSearchResult({ status: 'no_evidence', error: '不应使用的错误' })
|
|
|
|
expect(resolveSearchResultViewTransition(result, '7d')).toEqual({
|
|
stage: 'insufficient',
|
|
analysisError: '近 7 天内没有找到与问题相关的聊天消息。'
|
|
})
|
|
})
|
|
|
|
it.each([
|
|
['retrieval_incomplete', 'partial', '当前检索未完整覆盖聊天记录，未生成总结。'],
|
|
['failed', 'insufficient', '本地搜索暂时无法完成'],
|
|
['ai_failed', 'partial', '证据已找到，但 AI 暂时无法生成回答']
|
|
] as const)('maps %s to its existing fallback presentation', (status, stage, analysisError) => {
|
|
expect(resolveSearchResultViewTransition(makeSearchResult({ status }), '30d')).toEqual({
|
|
stage,
|
|
analysisError
|
|
})
|
|
})
|
|
|
|
it.each([
|
|
['retrieval_incomplete', 'partial'],
|
|
['failed', 'insufficient'],
|
|
['ai_failed', 'partial']
|
|
] as const)('preserves the pipeline error for %s', (status, stage) => {
|
|
expect(
|
|
resolveSearchResultViewTransition(
|
|
makeSearchResult({ status, error: '主进程返回的错误' }),
|
|
'30d'
|
|
)
|
|
).toEqual({
|
|
stage,
|
|
analysisError: '主进程返回的错误'
|
|
})
|
|
})
|
|
|
|
it('keeps a completed answer for the Workspace success path', () => {
|
|
expect(
|
|
resolveSearchResultViewTransition(makeSearchResult({ answer: '完整回答' }), '30d')
|
|
).toEqual({
|
|
stage: 'result',
|
|
analysisError: '',
|
|
answer: '完整回答'
|
|
})
|
|
})
|
|
|
|
it('leaves a missing completed answer for the existing Workspace guard', () => {
|
|
const result = { ...makeSearchResult(), answer: undefined }
|
|
|
|
expect(resolveSearchResultViewTransition(result, '30d')).toEqual({
|
|
stage: 'result',
|
|
analysisError: '',
|
|
answer: undefined
|
|
})
|
|
})
|
|
})
|
|
|
|
describe('AI Search result reset state', () => {
|
|
it('returns every existing search result reset value', () => {
|
|
expect(createSearchResultResetState()).toEqual({
|
|
analysisError: '',
|
|
answer: '',
|
|
evidence: [],
|
|
evidenceCollection: [],
|
|
visibleEvidenceCount: 0,
|
|
selectedEvidence: 0,
|
|
cachedAt: 0,
|
|
searchTrace: null,
|
|
searchProgress: {},
|
|
agentTrace: [],
|
|
searchDetailsOpen: false
|
|
})
|
|
})
|
|
|
|
it('returns independent arrays and progress objects for consecutive resets', () => {
|
|
const first = createSearchResultResetState()
|
|
const second = createSearchResultResetState()
|
|
|
|
expect(first.evidence).not.toBe(second.evidence)
|
|
expect(first.evidenceCollection).not.toBe(second.evidenceCollection)
|
|
expect(first.searchProgress).not.toBe(second.searchProgress)
|
|
expect(first.agentTrace).not.toBe(second.agentTrace)
|
|
})
|
|
})
|