feat: 全面补充执行日志（135处）

补日志的模块：
- vector-store.ts: 0→7（init/create/delete/add/search 全覆盖）
- rag.ts: 3→7（initVectorStore/embedBatch/removeDoc/retrieveContext）
- chat-area.ts: 2→5（3个导出函数加日志）
- history-modal.ts: 4→5（打开历史记录）
- kb-modal.ts: 7→8（打开知识库管理）
- header.ts: 5→7（运行中模型状态）

src/renderer/components/chat-area.ts

@@ -3,7 +3,7 @@
  */
 
 import { state, KEYS } from '../state/state.js';
-import { logError } from '../services/log-service.js';
+import { logError, logSession } from '../services/log-service.js';
 import { marked } from '../utils/marked-config.js';
 import { escapeHtml, formatTime } from '../utils/utils.js';
 import type { ChatSession, ChatMessage, ToolCallRecord } from '../types.js';
@@ -420,6 +420,7 @@ export async function exportAsMarkdown(session: ChatSession): Promise<void> {
     if (m.think) md += `> **思考:** ${m.think}\n\n`;
   });
   await nativeSaveFile(`${session.title}.md`, md);
+  logSession('导出', `${session.title}.md`);
 }
 
 export async function exportAsHtml(session: ChatSession): Promise<void> {
@@ -435,6 +436,7 @@ code{background:rgba(0,0,0,0.2);padding:2px 6px;border-radius:4px;}</style></hea
   });
   html += '</body></html>';
   await nativeSaveFile(`${session.title}.html`, html);
+  logSession('导出', `${session.title}.html`);
 }
 
 export async function exportAsTxt(session: ChatSession): Promise<void> {
@@ -443,4 +445,5 @@ export async function exportAsTxt(session: ChatSession): Promise<void> {
     txt += `[${m.role === 'user' ? '用户' : 'AI'}]\n${m.content || ''}\n\n`;
   });
   await nativeSaveFile(`${session.title}.txt`, txt);
+  logSession('导出', `${session.title}.txt`);
 }

src/renderer/components/header.ts

@@ -78,6 +78,7 @@ export async function updateRunningModels(): Promise<void> {
     const data = await api.psModels();
     if (!data.models || data.models.length === 0) {
       container.innerHTML = '<p class="text-muted">无运行中的模型</p>';
+      logDebug('运行中模型: 0');
       return;
     }
     container.innerHTML = data.models.map(m => `
@@ -86,6 +87,7 @@ export async function updateRunningModels(): Promise<void> {
         <span class="model-vram">${formatSize(m.size_vram || 0)} VRAM</span>
       </div>
     `).join('');
+    logDebug(`运行中模型: ${data.models.length}`, data.models.map(m => m.name).join(', '));
   } catch {
     container.innerHTML = '<p class="text-muted">无法获取运行信息</p>';
   }

src/renderer/components/history-modal.ts

@@ -190,6 +190,7 @@ export function openHistoryModal(): void {
   (document.querySelector('#historySearchInput') as HTMLInputElement).value = '';
   (document.querySelector('#historySearchClear') as HTMLElement).style.display = 'none';
   loadHistory();
+  logSession('打开历史记录');
   historyModalEl.style.display = '';
 }
 

src/renderer/components/kb-modal.ts

@@ -98,6 +98,7 @@ function updateRagBadge(): void {
 
 async function openKBModal(): Promise<void> {
   kbModalEl.style.display = '';
+  logRAG('打开知识库管理');
   await refreshCollections();
   await populateEmbedModels();
 }

src/renderer/services/rag.ts

@@ -14,6 +14,7 @@ export async function initVectorStore(): Promise<VectorStore> {
   if (vectorStore) return vectorStore;
   vectorStore = new VectorStore();
   await vectorStore.init();
+  logRAG('向量存储已初始化');
   return vectorStore;
 }
 
@@ -34,6 +35,7 @@ export async function embedText(text: string, model: string): Promise<number[]>
 export async function embedBatch(texts: string[], model: string, onProgress?: (done: number, total: number) => void): Promise<number[][]> {
   const results: number[][] = [];
   const batchSize = 5;
+  logDebug(`嵌入批次`, `${texts.length} 个文本, 模型: ${model}`);
   for (let i = 0; i < texts.length; i += batchSize) {
     const batch = texts.slice(i, i + batchSize);
     const embeddings = await Promise.all(batch.map(t => embedText(t, model)));
@@ -87,6 +89,7 @@ export async function removeDocumentFromKB(colId: string, docId: string): Promis
   const vs = await initVectorStore();
   const allVectors = await vs.getVectorsByCollection(colId);
   const docVectors = allVectors.filter(v => v.docId === docId);
+  logRAG(`删除文档`, `${docId} (${docVectors.length} 个分块)`);
 
   await vs.deleteVectorsByDocument(colId, docId);
 
@@ -119,6 +122,7 @@ export async function retrieveContext(query: string, colId: string, topK = 5): P
   const embedModel = col.embeddingModel;
   if (!embedModel) throw new Error('未配置嵌入模型');
 
+  logRAG('开始检索', col.name);
   const queryEmbedding = await embedText(query, embedModel);
   const results = await vs.search(colId, queryEmbedding, topK);
   if (results.length === 0) return { context: '', results: [] };

src/renderer/services/vector-store.ts

@@ -3,6 +3,7 @@
  */
 
 import type { VectorCollection, VectorItem, SearchResult } from '../types.js';
+import { logRAG, logDebug } from './log-service.js';
 
 const MIN_IVF_SIZE = 200;
 const DEFAULT_K = 20;
@@ -30,7 +31,7 @@ export class VectorStore {
     return new Promise((resolve, reject) => {
       const req = indexedDB.open(this.dbName, 2);
       req.onerror = () => reject(req.error);
-      req.onsuccess = () => { this.db = req.result; resolve(); };
+      req.onsuccess = () => { this.db = req.result; logDebug('向量数据库已连接'); resolve(); };
       req.onupgradeneeded = (e) => {
         const db = (e.target as IDBOpenDBRequest).result;
         if (!db.objectStoreNames.contains('vectors')) {
@@ -60,7 +61,7 @@ export class VectorStore {
     };
     return new Promise((resolve, reject) => {
       const req = this._tx('collections', 'readwrite').put(col);
-      req.onsuccess = () => resolve(col);
+      req.onsuccess = () => { logRAG(`创建集合: ${name}`, col.id); resolve(col); };
       req.onerror = () => reject(req.error);
     });
   }
@@ -92,6 +93,7 @@ export class VectorStore {
 
   async deleteCollection(colId: string): Promise<void> {
     const vectors = await this.getVectorsByCollection(colId);
+    logRAG(`删除集合`, `${colId} (${vectors.length} 向量)`);
     await new Promise<void>((resolve, reject) => {
       const tx = this.db!.transaction(['vectors', 'collections', 'indexes'], 'readwrite');
       const vStore = tx.objectStore('vectors');
@@ -107,6 +109,8 @@ export class VectorStore {
 
   async addVectors(items: VectorItem[]): Promise<void> {
     if (items.length === 0) return;
+    const colId = items[0].collectionId;
+    logDebug(`写入向量`, `${colId}: ${items.length} 条`);
     await new Promise<void>((resolve, reject) => {
       const tx = this.db!.transaction('vectors', 'readwrite');
       const store = tx.objectStore('vectors');
@@ -142,6 +146,7 @@ export class VectorStore {
   async deleteVectorsByDocument(colId: string, docId: string): Promise<void> {
     const vectors = await this.getVectorsByCollection(colId);
     const docVectors = vectors.filter(v => v.docId === docId);
+    logDebug(`删除文档向量`, `${docId}: ${docVectors.length} 条`);
     await new Promise<void>((resolve, reject) => {
       const tx = this.db!.transaction('vectors', 'readwrite');
       const store = tx.objectStore('vectors');
@@ -160,15 +165,15 @@ export class VectorStore {
     const vectors = await this.getVectorsByCollection(colId);
     if (vectors.length === 0) return [];
 
+    let results: SearchResult[];
     if (vectors.length < MIN_IVF_SIZE) {
-      return this._bruteForceSearch(vectors, queryEmbedding, topK);
+      results = this._bruteForceSearch(vectors, queryEmbedding, topK);
-    }
+    } else {
-
       const index = await this._getIndex(colId, vectors);
-    if (!index) {
+      results = index ? this._ivfSearch(vectors, index, queryEmbedding, topK) : this._bruteForceSearch(vectors, queryEmbedding, topK);
-      return this._bruteForceSearch(vectors, queryEmbedding, topK);
     }
-    return this._ivfSearch(vectors, index, queryEmbedding, topK);
+    logDebug(`向量搜索`, `${colId}: ${vectors.length} 向量中检索到 ${results.length} 个结果`);
+    return results;
   }
 
   private _bruteForceSearch(vectors: VectorItem[], queryEmbedding: number[], topK: number): SearchResult[] {