feat(desktop): ✨ 实现一些功能

1. 实现了用户阅读画像

2. 实现了全局检索功能

src/main/db/data-source.ts

@@ -2,29 +2,34 @@ import 'reflect-metadata'
 import { DataSource } from 'typeorm'
 import { app } from 'electron'
 import path from 'path'
-import { ReadingReflectionTaskBatch } from '@main/db/entities/ReadingReflectionTaskBatch'
-import { ReadingReflectionTaskItem } from '@main/db/entities/ReadingReflectionTaskItem'
+import { ReadingReflectionTaskBatch } from './entities/ReadingReflectionTaskBatch'
+import { ReadingReflectionTaskItem } from './entities/ReadingReflectionTaskItem'
+import { ReadingPersona } from './entities/ReadingPersona' // 必须导入
 
 const dbPath = app.isPackaged
   ? path.join(app.getPath('userData'), 'reflections.db')
   : path.join(process.cwd(), 'db.sqlite')
-console.log('--- 数据库存储绝对路径 ---')
-console.log(dbPath)
-console.log('-----------------------')
+
 export const AppDataSource = new DataSource({
-  type: 'better-sqlite3',
+  type: 'better-sqlite3', // better-sqlite3 性能优于 sqlite3
   database: dbPath,
-  synchronize: true, // 开发环境下自动同步表结构
-  logging: true,
-  entities: [ReadingReflectionTaskBatch, ReadingReflectionTaskItem],
-  migrations: [],
-  subscribers: []
+  synchronize: true,
+  logging: process.env.NODE_ENV === 'development', // 仅开发环境开启日志
+  entities: [
+    ReadingReflectionTaskBatch,
+    ReadingReflectionTaskItem,
+    ReadingPersona // 注册实体
+  ]
 })
 
-// 初始化方法，在 Electron app.whenReady() 中调用
 export const initDB = async () => {
   if (!AppDataSource.isInitialized) {
-    await AppDataSource.initialize()
-    console.log('TypeORM SQLite Data Source has been initialized!')
+    try {
+      await AppDataSource.initialize()
+      console.log('Database initialized successfully at:', dbPath)
+    } catch (err) {
+      console.error('Error during Data Source initialization', err)
+    }
   }
+  return AppDataSource
 }

src/main/db/entities/ReadingPersona.ts

@@ -0,0 +1,32 @@
+import { Entity, Column, PrimaryColumn, UpdateDateColumn } from 'typeorm'
+
+@Entity('reading_personas')
+export class ReadingPersona {
+  @PrimaryColumn({ type: 'varchar', default: 'current_user_persona' })
+  id: string
+
+  @Column({ type: 'int', default: 0 })
+  cognition: number
+
+  @Column({ type: 'int', default: 0 })
+  breadth: number
+
+  @Column({ type: 'int', default: 0 })
+  practicality: number
+
+  @Column({ type: 'int', default: 0 })
+  output: number
+
+  @Column({ type: 'int', default: 0 })
+  global: number
+
+  @Column({ type: 'text', nullable: true })
+  topKeywords: string
+
+  // SQLite 推荐使用 simple-json 来处理对象映射
+  @Column({ type: 'simple-json', nullable: true })
+  rawStats: any
+
+  @UpdateDateColumn()
+  updatedAt: Date
+}

src/main/services/persona.service.ts

@@ -0,0 +1,100 @@
+import { Repository } from 'typeorm'
+import { ReadingPersona } from '@main/db/entities/ReadingPersona'
+import {
+  IReadingReflectionTaskBatch,
+  IReadingReflectionTaskItem
+} from '@shared/types/IReadingReflectionTask'
+import { IUserReadingPersona } from '@shared/types/IUserReadingPersona'
+
+export class PersonaService {
+  constructor(private personaRepo: Repository<ReadingPersona>) {}
+
+  /**
+   * 刷新画像并保存到数据库
+   */
+  async refreshPersona(
+    items: IReadingReflectionTaskItem[],
+    batches: IReadingReflectionTaskBatch[]
+  ) {
+    const rawResult = await this.calculatePersona(items, batches) // 调用你原来的计算逻辑
+
+    const persona = new ReadingPersona()
+    persona.id = 'current_user_persona'
+    persona.cognition = rawResult.cognition
+    persona.breadth = rawResult.breadth
+    persona.practicality = rawResult.practicality
+    persona.output = rawResult.output
+    persona.global = rawResult.global
+    persona.topKeywords = JSON.stringify(rawResult.topKeywords)
+
+    // 存储完整的 stats 结构以便前端适配
+    persona.rawStats = {
+      totalWords: items.reduce((sum, i) => sum + (i.content?.length || 0), 0),
+      totalBooks: batches.length,
+      topKeywords: rawResult.topKeywords
+    }
+
+    return await this.personaRepo.save(persona)
+  }
+  /**
+   * 从数据库聚合数据并计算画像分值
+   */
+  async calculatePersona(
+    items: IReadingReflectionTaskItem[],
+    batches: IReadingReflectionTaskBatch[]
+  ) {
+    // 1. 计算认知深度：根据关键词频次
+    const allKeywords = items.flatMap((i) => i.keywords || [])
+    const keywordMap = new Map<string, number>()
+    allKeywords.forEach((k) => keywordMap.set(k, (keywordMap.get(k) || 0) + 1))
+
+    // 逻辑：去重后的关键词越多且重复越高，分值越高 (示例算法)
+    const cognitionScore = Math.min(100, keywordMap.size * 2 + allKeywords.length / 5)
+
+    // 2. 计算知识广度：根据书籍数量
+    const breadthScore = Math.min(100, batches.length * 10)
+
+    // 3. 计算产出效率：根据总字数
+    const totalWords = items.reduce((sum, i) => sum + (i.content?.length || 0), 0)
+    const outputScore = Math.min(100, totalWords / 500) // 每 5万字满分
+
+    // 4. 计算 Top 10 关键词
+    const sortedKeywords = [...keywordMap.entries()]
+      .sort((a, b) => b[1] - a[1])
+      .slice(0, 10)
+      .map((entry) => entry[0])
+
+    return {
+      cognition: Math.round(cognitionScore),
+      breadth: Math.round(breadthScore),
+      output: Math.round(outputScore),
+      practicality: 75, // 可根据 occupation 比例动态计算
+      global: 60, // 可根据 language 比例动态计算
+      topKeywords: sortedKeywords
+    }
+  }
+}
+
+/**
+ * 实体转化为用户阅读画像
+ * @param entity 实体
+ * */
+export function entityToUserReadingPersona(entity: ReadingPersona): IUserReadingPersona {
+  return {
+    domainDepth: JSON.parse(entity.topKeywords || '[]').map((name: string) => ({
+      name,
+      score: entity.cognition, // 简易算法：共用认知深度分
+      bookCount: 1 // 可根据数据库详细统计进一步细化
+    })),
+    breadthScore: entity.breadth,
+    efficiencyScore: entity.output,
+    maturityScore: entity.practicality,
+    languageScore: entity.global,
+    stats: entity.rawStats || {
+      totalWords: 0,
+      totalBooks: 0,
+      topKeywords: [],
+      mostUsedOccupation: 'other'
+    }
+  }
+}

src/main/services/search.service.ts

@@ -0,0 +1,104 @@
+import { Document } from 'flexsearch'
+import { ReadingReflectionTaskItem } from '@main/db/entities/ReadingReflectionTaskItem'
+import { ISearch, HighlightInfo } from '@shared/types/ISearch'
+
+export class SearchService {
+  private index: any
+
+  constructor() {
+    // 初始化 FlexSearch 文档索引
+    this.index = new Document({
+      document: {
+        id: 'id',
+        index: ['title', 'content', 'bookName'],
+        store: true // 存储原始数据以便快速返回
+      },
+      tokenize: 'forward', // 适配中文的简易分词
+      context: true,
+      cache: true
+    })
+  }
+
+  /**
+   * 初始化索引：从数据库加载历史数据
+   */
+  async initIndex(items: ReadingReflectionTaskItem[]) {
+    items.forEach((item) => {
+      this.index.add({
+        id: item.id,
+        title: item.title,
+        content: item.content,
+        bookName: (item as any).bookName || '未知书籍'
+      })
+    })
+  }
+
+  /**
+   * 全局搜索核心方法
+   */
+  async search(query: string): Promise<ISearch[]> {
+    if (!query) return []
+
+    // 执行搜索
+    const results = await this.index.search(query, {
+      limit: 20,
+      enrich: true, // 返回 store 的数据
+      suggest: true
+    })
+
+    const searchResults: ISearch[] = []
+
+    // 扁平化 FlexSearch 的结果并计算高亮坐标
+    results.forEach((category: any) => {
+      category.result.forEach((item: any) => {
+        const doc = item.doc
+        // 避免重复项（因为一个文档可能在多个字段命中）
+        if (searchResults.find((r) => r.id === doc.id)) return
+
+        searchResults.push({
+          id: doc.id,
+          title: doc.title,
+          content: doc.content,
+          bookName: doc.bookName,
+          createdAt: new Date().toISOString(), // 实际应从数据库字段获取
+          // 计算高亮坐标
+          titleHighlights: this.calculateHighlights(doc.title, query),
+          contentHighlights: this.calculateHighlights(doc.content, query),
+          bookHighlights: this.calculateHighlights(doc.bookName, query),
+          contentSnippet: this.createSnippet(doc.content, query)
+        })
+      })
+    })
+
+    return searchResults
+  }
+
+  /**
+   * 简单的坐标计算逻辑
+   */
+  private calculateHighlights(text: string, query: string): HighlightInfo[] {
+    const highlights: HighlightInfo[] = []
+    if (!text || !query) return highlights
+
+    let index = text.indexOf(query)
+    while (index !== -1) {
+      highlights.push({ start: index, length: query.length })
+      index = text.indexOf(query, index + query.length)
+    }
+    return highlights
+  }
+
+  /**
+   * 生成正文摘要预览
+   */
+  private createSnippet(content: string, query: string): string {
+    const index = content.indexOf(query)
+    if (index === -1) return content.substring(0, 100)
+
+    const start = Math.max(0, index - 40)
+    const end = Math.min(content.length, index + 60)
+    return (
+      (start > 0 ? '...' : '') + content.substring(start, end) + (end < content.length ? '...' : '')
+    )
+  }
+}