feat: TypeScript + Electron v2 重构 - 纯桌面版

- 全面迁移到 TypeScript，严格类型定义
- 放弃 Web 版，专注 Electron 桌面应用
- 主进程模块化：main.ts, preload.ts, menu.ts, tray.ts, ipc.ts, utils.ts
- 渲染进程完整迁移所有功能组件
- 删除 PWA 相关文件 (sw.js, manifest.json)
- 删除 Web 版降级逻辑
- 保留所有核心功能：流式对话、多模型、Think推理、多模态、RAG知识库、Agent预设、历史管理
- 保留 Windows 11 Fluent Design 暗色主题样式

src/renderer/services/rag.ts

@@ -0,0 +1,145 @@
+/**
+ * RAG - 检索增强生成管线
+ */
+
+import { state, KEYS } from '../state/state.js';
+import { VectorStore } from './vector-store.js';
+import { chunkText, createChunkMetadata } from './document-processor.js';
+import type { OllamaAPI, SearchResult, VectorItem } from '../types.js';
+
+let vectorStore: VectorStore | null = null;
+
+export async function initVectorStore(): Promise<VectorStore> {
+  if (vectorStore) return vectorStore;
+  vectorStore = new VectorStore();
+  await vectorStore.init();
+  return vectorStore;
+}
+
+export function getVectorStore(): VectorStore | null {
+  return vectorStore;
+}
+
+export async function embedText(text: string, model: string): Promise<number[]> {
+  const api = state.get<OllamaAPI>(KEYS.API);
+  if (!api) throw new Error('Ollama API 未连接');
+
+  const result = await api.embed(model, text);
+  if (result.embedding) return result.embedding;
+  if (result.embeddings && result.embeddings[0]) return result.embeddings[0];
+  throw new Error('嵌入向量返回格式异常');
+}
+
+export async function embedBatch(texts: string[], model: string, onProgress?: (done: number, total: number) => void): Promise<number[][]> {
+  const results: number[][] = [];
+  const batchSize = 5;
+  for (let i = 0; i < texts.length; i += batchSize) {
+    const batch = texts.slice(i, i + batchSize);
+    const embeddings = await Promise.all(batch.map(t => embedText(t, model)));
+    results.push(...embeddings);
+    if (onProgress) onProgress(Math.min(i + batchSize, texts.length), texts.length);
+  }
+  return results;
+}
+
+export async function addDocumentToKB(
+  colId: string, filename: string, content: string, embedModel: string,
+  onProgress?: (done: number, total: number, msg: string) => void
+): Promise<{ docId: string; chunkCount: number }> {
+  const vs = await initVectorStore();
+
+  const textChunks = chunkText(content);
+  if (textChunks.length === 0) throw new Error('文档内容为空，无法处理');
+
+  const docId = `doc_${Date.now()}_${Math.random().toString(36).slice(2, 7)}`;
+
+  if (onProgress) onProgress(0, textChunks.length, '正在生成向量...');
+  const embeddings = await embedBatch(textChunks, embedModel, (done, total) => {
+    if (onProgress) onProgress(done, total, '正在生成向量...');
+  });
+
+  const vectors: VectorItem[] = textChunks.map((chunk, i) => {
+    const meta = createChunkMetadata(chunk, i, docId, filename);
+    return {
+      ...meta,
+      collectionId: colId,
+      embedding: embeddings[i]
+    };
+  });
+
+  if (onProgress) onProgress(textChunks.length, textChunks.length, '正在保存...');
+  await vs.addVectors(vectors);
+
+  const col = await vs.getCollection(colId);
+  if (col) {
+    col.docCount = (col.docCount || 0) + 1;
+    col.chunkCount = (col.chunkCount || 0) + textChunks.length;
+    col.embeddingModel = embedModel;
+    await vs.updateCollection(col);
+  }
+
+  return { docId, chunkCount: textChunks.length };
+}
+
+export async function removeDocumentFromKB(colId: string, docId: string): Promise<void> {
+  const vs = await initVectorStore();
+  const allVectors = await vs.getVectorsByCollection(colId);
+  const docVectors = allVectors.filter(v => v.docId === docId);
+
+  await vs.deleteVectorsByDocument(colId, docId);
+
+  const col = await vs.getCollection(colId);
+  if (col) {
+    col.chunkCount = Math.max(0, (col.chunkCount || 0) - docVectors.length);
+    col.docCount = Math.max(0, (col.docCount || 0) - 1);
+    await vs.updateCollection(col);
+  }
+}
+
+export async function getDocumentsInCollection(colId: string): Promise<Array<{ docId: string; filename: string; chunkCount: number }>> {
+  const vs = await initVectorStore();
+  const vectors = await vs.getVectorsByCollection(colId);
+  const docMap = new Map<string, { docId: string; filename: string; chunkCount: number }>();
+  for (const v of vectors) {
+    if (!docMap.has(v.docId)) {
+      docMap.set(v.docId, { docId: v.docId, filename: v.filename, chunkCount: 0 });
+    }
+    docMap.get(v.docId)!.chunkCount++;
+  }
+  return Array.from(docMap.values());
+}
+
+export async function retrieveContext(query: string, colId: string, topK = 5): Promise<{ context: string; results: SearchResult[] }> {
+  const vs = await initVectorStore();
+  const col = await vs.getCollection(colId);
+  if (!col) throw new Error('知识库集合不存在');
+
+  const embedModel = col.embeddingModel;
+  if (!embedModel) throw new Error('未配置嵌入模型');
+
+  const queryEmbedding = await embedText(query, embedModel);
+  const results = await vs.search(colId, queryEmbedding, topK);
+  if (results.length === 0) return { context: '', results: [] };
+
+  const contextParts = results.map((r, i) =>
+    `[来源 ${i + 1}: ${r.filename}]\n${r.text}`
+  );
+  const context = contextParts.join('\n\n---\n\n');
+
+  return { context, results };
+}
+
+export function buildRagSystemPrompt(context: string, originalSystemPrompt = ''): string {
+  const ragPrompt = `你是一个知识库问答助手。以下是检索到的相关文档片段，请基于这些内容回答用户的问题。如果检索到的内容无法回答问题，请如实说明。
+
+=== 检索到的相关内容 ===
+${context}
+=== 内容结束 ===
+
+请基于以上内容回答用户的问题。引用时请注明来源编号（如"来源 1"）。`;
+
+  if (originalSystemPrompt) {
+    return originalSystemPrompt + '\n\n' + ragPrompt;
+  }
+  return ragPrompt;
+}