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https://wget.la/https://github.com/Wxw-Gu/WechatExplorer
synced 2026-08-18 12:07:01 +08:00
test: 暂存代码
This commit is contained in:
电摇小子
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import { mkdtempSync, existsSync } from 'fs'
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import { rm } from 'fs/promises'
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import { tmpdir } from 'os'
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import { join } from 'path'
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import { afterEach, describe, expect, it } from 'vitest'
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import { DEFAULT_KNOWLEDGE_CHUNKER, type KnowledgeFtsConfig } from '../../src/shared/knowledge'
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import { chunkConversation } from '../../src/main/knowledge/chunker'
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import {
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estimateKnowledgeCapacityPreflight,
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getKnowledgeDatabasePath,
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KnowledgeStore,
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removeKnowledgeDatabase
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} from '../../src/main/knowledge/knowledge-store'
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import { normalizeKnowledgeMessage } from '../../src/main/knowledge/normalizer'
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import {
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createSyntheticConversation,
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FIXTURE_ACCOUNT_A,
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FIXTURE_ACCOUNT_B
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} from '../fixtures/knowledge-rag'
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const roots: string[] = []
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const fts: KnowledgeFtsConfig = {
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profileId: 'test-trigram-external-full',
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tokenizer: 'trigram',
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contentMode: 'external',
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detail: 'full',
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columnsize: 1
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}
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function makeRoot(): string {
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const root = mkdtempSync(join(tmpdir(), 'wxe-knowledge-'))
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roots.push(root)
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return root
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}
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afterEach(async () => {
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await Promise.all(roots.splice(0).map((root) => rm(root, { recursive: true, force: true })))
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})
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describe('knowledge normalizer and chunker', () => {
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it('indexes text, attachment metadata and existing voice transcripts without paths or binary data', () => {
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const normalized = normalizeKnowledgeMessage({
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accountId: FIXTURE_ACCOUNT_A,
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conversationId: 'conversation-a',
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messageId: 'message-a',
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createTime: 1,
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kind: 'voice',
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text: ' 原始说明 ',
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attachment: { name: 'plan.txt', kind: 'file' },
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voiceTranscript: ' 已完成语音转写 '
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})
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expect(normalized.searchableText).toContain('原始说明')
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expect(normalized.searchableText).toContain('附件：plan.txt')
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expect(normalized.searchableText).toContain('语音转写：已完成语音转写')
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})
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it('cuts on time gaps and preserves message evidence ids', () => {
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const source = createSyntheticConversation(
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FIXTURE_ACCOUNT_A,
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'conversation-a',
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0,
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4,
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'short'
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).messages
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source[3].createTime += 20 * 60 * 1000
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const chunks = chunkConversation(source.map(normalizeKnowledgeMessage), {
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...DEFAULT_KNOWLEDGE_CHUNKER,
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maxMessages: 12
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})
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expect(chunks).toHaveLength(2)
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expect(chunks.flatMap((chunk) => chunk.messageIds)).toEqual(
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source.map((item) => item.messageId)
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)
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})
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})
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describe('knowledge sqlite', () => {
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it('is idempotent, supports FTS evidence lookup, and does not mix accounts', async () => {
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const root = makeRoot()
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const source = createSyntheticConversation(FIXTURE_ACCOUNT_A, 'conversation-a', 0, 25, 'mixed')
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const store = new KnowledgeStore(root, FIXTURE_ACCOUNT_A, fts)
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const first = await store.index({ conversations: [source], chunker: DEFAULT_KNOWLEDGE_CHUNKER })
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const second = await store.index({
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conversations: [source],
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chunker: DEFAULT_KNOWLEDGE_CHUNKER
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})
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expect(first.updatedChunks).toBeGreaterThan(0)
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expect(second.updatedChunks).toBe(0)
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expect(second.unchangedConversations).toBe(1)
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const evidence = store.search({ accountId: FIXTURE_ACCOUNT_A, text: '本地知识库', limit: 10 })
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expect(evidence).not.toHaveLength(0)
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expect(evidence[0]).toMatchObject({
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messageId: expect.stringMatching(/^synthetic-mixed-/),
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conversationId: 'conversation-a',
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sender: expect.any(String),
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timestamp: expect.any(Number)
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})
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expect(
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evidence.every((item) => item.messageIds.every((id) => id.startsWith('synthetic-mixed-')))
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).toBe(true)
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expect(() =>
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store.search({ accountId: FIXTURE_ACCOUNT_B, text: '本地知识库', limit: 10 })
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).toThrow(/account/)
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store.close()
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})
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it('recovers safely after cancellation and only removes the derived database', async () => {
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const root = makeRoot()
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const source = createSyntheticConversation(
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FIXTURE_ACCOUNT_A,
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'conversation-a',
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0,
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2_000,
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'mixed'
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)
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const controller = new AbortController()
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const store = new KnowledgeStore(root, FIXTURE_ACCOUNT_A, fts)
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const cancelled = await store.index(
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{ conversations: [source], chunker: DEFAULT_KNOWLEDGE_CHUNKER },
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controller.signal,
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(progress) => {
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if (progress.processedMessages >= 501) controller.abort()
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}
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)
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expect(cancelled.cancelled).toBe(true)
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const resumed = await store.index({
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conversations: [source],
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chunker: DEFAULT_KNOWLEDGE_CHUNKER
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})
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expect(resumed.cancelled).toBe(false)
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const databasePath = getKnowledgeDatabasePath(root, FIXTURE_ACCOUNT_A)
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store.close()
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expect(existsSync(databasePath)).toBe(true)
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removeKnowledgeDatabase(root, FIXTURE_ACCOUNT_A)
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expect(existsSync(databasePath)).toBe(false)
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})
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it('uses a bounded exact fallback for two-character Chinese queries with the trigram profile', async () => {
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const root = makeRoot()
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const store = new KnowledgeStore(root, FIXTURE_ACCOUNT_A, fts)
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await store.index({
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conversations: [
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{
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conversationId: 'short-query',
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completeSnapshot: true,
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messages: [
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{
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accountId: FIXTURE_ACCOUNT_A,
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conversationId: 'short-query',
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messageId: 'short-query-message',
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createTime: Date.UTC(2026, 7, 5),
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senderId: 'fixture-member',
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senderName: '脱敏成员',
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kind: 'text',
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text: '收到，明早十点。'
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}
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]
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}
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],
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chunker: DEFAULT_KNOWLEDGE_CHUNKER
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})
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expect(
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store.search({ accountId: FIXTURE_ACCOUNT_A, text: '十点', terms: ['十点'], limit: 10 })
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).toEqual([
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expect.objectContaining({ messageId: 'short-query-message', conversationId: 'short-query' })
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])
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store.close()
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})
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it('keeps equal message ids from different conversations as separate Evidence', async () => {
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const root = makeRoot()
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const store = new KnowledgeStore(root, FIXTURE_ACCOUNT_A, fts)
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await store.index({
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conversations: ['conversation-a', 'conversation-b'].map((conversationId) => ({
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conversationId,
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completeSnapshot: true,
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messages: [
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{
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accountId: FIXTURE_ACCOUNT_A,
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conversationId,
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messageId: 'shared-message-id',
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createTime: Date.UTC(2026, 7, 5),
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senderId: `${conversationId}-sender`,
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senderName: conversationId,
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kind: 'text',
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text: '今天去健身。'
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}
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]
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})),
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chunker: DEFAULT_KNOWLEDGE_CHUNKER
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})
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const result = store.searchWithStatus({
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accountId: FIXTURE_ACCOUNT_A,
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text: '去健身',
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terms: ['去健身'],
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limit: 10
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})
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const evidence = result.evidence
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expect(evidence).toHaveLength(2)
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expect(evidence.map((item) => `${item.conversationId}:${item.messageId}`).sort()).toEqual([
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'conversation-a:shared-message-id',
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'conversation-b:shared-message-id'
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])
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expect(result.timings).toMatchObject({
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workerIpcMs: 0,
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ftsMs: expect.any(Number),
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messageLoadMs: expect.any(Number),
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chunkExpandMs: expect.any(Number),
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rankingMs: expect.any(Number),
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totalMs: expect.any(Number)
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})
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expect(result.timings.totalMs).toBeGreaterThanOrEqual(result.timings.ftsMs)
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store.close()
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})
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it('keeps conversation, sender and time filters when a participant question has no topic terms', async () => {
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const root = makeRoot()
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const store = new KnowledgeStore(root, FIXTURE_ACCOUNT_A, fts)
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await store.index({
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conversations: [
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{
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conversationId: 'participant-query',
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completeSnapshot: true,
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messages: [
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{
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accountId: FIXTURE_ACCOUNT_A,
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conversationId: 'participant-query',
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messageId: 'participant-a',
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createTime: Date.UTC(2026, 7, 5, 9),
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senderId: 'member-a',
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senderName: '成员甲',
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kind: 'text',
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text: '第一条讨论。'
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},
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{
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accountId: FIXTURE_ACCOUNT_A,
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conversationId: 'participant-query',
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messageId: 'participant-b',
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createTime: Date.UTC(2026, 7, 5, 10),
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senderId: 'member-b',
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senderName: '成员乙',
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kind: 'text',
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text: '第二条讨论。'
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}
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]
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}
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],
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chunker: DEFAULT_KNOWLEDGE_CHUNKER
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})
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expect(
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store.search({
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accountId: FIXTURE_ACCOUNT_A,
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text: '成员甲最近聊了什么',
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terms: [],
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conversationIds: ['participant-query'],
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senderIds: ['member-a'],
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startTime: Date.UTC(2026, 7, 5, 8),
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limit: 10
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})
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).toEqual([expect.objectContaining({ messageId: 'participant-a', sender: '成员甲' })])
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store.close()
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})
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it('compresses a single-conversation recap into time chunks and deprioritizes system messages', async () => {
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const root = makeRoot()
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const store = new KnowledgeStore(root, FIXTURE_ACCOUNT_A, fts)
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const base = Date.UTC(2026, 6, 1)
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await store.index({
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conversations: [
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{
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conversationId: 'recap-query',
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completeSnapshot: true,
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messages: Array.from({ length: 48 }, (_, index) => ({
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accountId: FIXTURE_ACCOUNT_A,
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conversationId: 'recap-query',
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messageId: `recap-${index}`,
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createTime: base + Math.floor(index / 12) * 3 * 3600 * 1000 + (index % 12) * 60_000,
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senderId: 'fixture-member',
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senderName: '脱敏成员',
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kind: index % 11 === 0 ? ('system' as const) : ('text' as const),
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text: index % 11 === 0 ? '对方撤回了一条消息' : `第 ${index} 条健身计划和饮食安排讨论。`
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}))
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}
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],
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chunker: DEFAULT_KNOWLEDGE_CHUNKER
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})
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const result = store.searchWithStatus({
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accountId: FIXTURE_ACCOUNT_A,
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text: '我和张三最近聊了什么',
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terms: [],
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conversationIds: ['recap-query'],
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startTime: base,
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limit: 100
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})
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expect(result.conversationRetrieval).toMatchObject({
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totalMessages: 48,
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chunkCount: 4,
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complete: true
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})
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expect(result.evidence.length).toBeLessThan(48)
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expect(new Set(result.evidence.map((item) => item.chunkId)).size).toBeGreaterThan(1)
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expect(result.evidence.filter((item) => item.text.includes('撤回')).length).toBeLessThan(5)
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store.close()
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})
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it('keeps late conversation slices when the recap candidate budget is reached', async () => {
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const root = makeRoot()
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const store = new KnowledgeStore(root, FIXTURE_ACCOUNT_A, fts)
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const base = Date.UTC(2026, 6, 1)
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await store.index({
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conversations: [
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{
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conversationId: 'long-recap-query',
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completeSnapshot: true,
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messages: Array.from({ length: 90 }, (_, index) => ({
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accountId: FIXTURE_ACCOUNT_A,
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conversationId: 'long-recap-query',
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messageId: `long-recap-${index}`,
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createTime: base + Math.floor(index / 3) * 3 * 3600 * 1000 + (index % 3) * 60_000,
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senderId: 'fixture-member',
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senderName: '脱敏成员',
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kind: 'text' as const,
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text: `第 ${index} 条近期聊天内容。`
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}))
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}
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],
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chunker: DEFAULT_KNOWLEDGE_CHUNKER
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})
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const result = store.searchWithStatus({
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accountId: FIXTURE_ACCOUNT_A,
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text: '我和张三最近聊了什么',
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terms: [],
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conversationIds: ['long-recap-query'],
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startTime: base,
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limit: 100
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})
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expect(result.conversationRetrieval).toMatchObject({ chunkCount: 30, candidateMessages: 60 })
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expect(Math.max(...result.evidence.map((item) => item.timestamp))).toBeGreaterThan(
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base + 28 * 3 * 3600 * 1000
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)
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store.close()
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})
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it('provides a read-only capacity preflight before a database exists', async () => {
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const root = makeRoot()
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const source = createSyntheticConversation(FIXTURE_ACCOUNT_A, 'conversation-a', 0, 20, 'long')
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const result = await estimateKnowledgeCapacityPreflight({
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accountId: FIXTURE_ACCOUNT_A,
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databaseRoot: root,
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conversations: [source],
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chunker: DEFAULT_KNOWLEDGE_CHUNKER,
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availableDiskBytes: 1
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})
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expect(result.sourceMessageCount).toBe(20)
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expect(result.voiceTranscriptCount).toBeGreaterThan(0)
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expect(result.hasSufficientDiskSpace).toBe(false)
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expect(existsSync(getKnowledgeDatabasePath(root, FIXTURE_ACCOUNT_A))).toBe(false)
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})
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it('indexes 100,000 desensitized messages without touching the main process database', async () => {
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const root = makeRoot()
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const store = new KnowledgeStore(root, FIXTURE_ACCOUNT_A, fts)
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const started = performance.now()
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for (let batch = 0; batch < 10; batch += 1) {
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const source = createSyntheticConversation(
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FIXTURE_ACCOUNT_A,
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`performance-${batch}`,
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batch * 10_000,
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10_000,
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'mixed'
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)
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await store.index({ conversations: [source], chunker: DEFAULT_KNOWLEDGE_CHUNKER })
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}
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const stats = store.getStorageStats()
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expect(stats.databaseBytes).toBeGreaterThan(0)
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expect(performance.now() - started).toBeLessThan(60_000)
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store.close()
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}, 70_000)
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})
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