feat: 将记忆系统和知识库合并改造为长效向量记忆系统

- 移除知识库(KB/RAG)功能，删除 kb-modal.ts、rag.ts、memory-panel.ts - 新增 vector-memory.ts：记忆向量存储与检索引擎 - 新增 memory-modal.ts：大模态框布局的记忆管理面板 - 简化记忆分类为3类：fact(事实)、preference(偏好)、rule(规则) - 嵌入模型配置移至设置面板 - 无嵌入模型时自动降级为关键词搜索模式 - 向量搜索支持语义相似度检索 - 嵌入模型变化时自动重新索引所有记忆
2026-04-16 09:27:05 +08:00
parent 759b5fa8db
commit 8ec34106cd
14 changed files with 1108 additions and 1463 deletions
@@ -0,0 +1,171 @@
+/**
+ * VectorMemory - 记忆向量存储与检索
+ * 替代原 rag.ts，专为记忆系统提供向量能力
+ */
+
+import { state, KEYS } from '../state/state.js';
+import { VectorStore } from './vector-store.js';
+import { logMemory, logDebug, logError } from './log-service.js';
+import type { MemoryEntry, OllamaAPI, VectorItem, SearchResult } from '../types.js';
+
+const MEMORY_COLLECTION_NAME = '记忆向量索引';
+
+let vectorStore: VectorStore | null = null;
+let memoryCollectionId: string | null = null;
+
+// ── 初始化 ──
+
+export async function initMemoryVectorStore(): Promise<VectorStore> {
+  if (vectorStore) return vectorStore;
+  vectorStore = new VectorStore();
+  await vectorStore.init();
+  logDebug('记忆向量存储已初始化');
+  return vectorStore;
+}
+
+export function getMemoryVectorStore(): VectorStore | null {
+  return vectorStore;
+}
+
+// ── 集合管理 ──
+
+export async function getOrCreateMemoryCollection(embedModel: string): Promise<{ id: string; isNew: boolean }> {
+  const vs = await initMemoryVectorStore();
+
+  // 如果已有缓存的集合ID，验证它是否存在
+  if (memoryCollectionId) {
+    const existing = await vs.getCollection(memoryCollectionId);
+    if (existing) {
+      if (existing.embeddingModel !== embedModel) {
+        existing.embeddingModel = embedModel;
+        await vs.updateCollection(existing);
+      }
+      return { id: memoryCollectionId, isNew: false };
+    }
+    memoryCollectionId = null;
+  }
+
+  // 搜索现有集合
+  const collections = await vs.getCollections();
+  const found = collections.find(c => c.name === MEMORY_COLLECTION_NAME);
+  if (found) {
+    memoryCollectionId = found.id;
+    if (found.embeddingModel !== embedModel) {
+      found.embeddingModel = embedModel;
+      await vs.updateCollection(found);
+    }
+    return { id: found.id, isNew: false };
+  }
+
+  // 创建新集合
+  const col = await vs.createCollection(MEMORY_COLLECTION_NAME, embedModel);
+  memoryCollectionId = col.id;
+  logMemory('创建记忆向量集合', col.id);
+  return { id: col.id, isNew: true };
+}
+
+export function getMemoryCollectionId(): string | null {
+  return memoryCollectionId;
+}
+
+export function setMemoryCollectionId(id: string | null): void {
+  memoryCollectionId = id;
+}
+
+// ── 文本嵌入 ──
+
+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');
+}
+
+// ── 为记忆条目生成向量 ──
+
+export async function embedMemoryEntry(entry: MemoryEntry, model: string): Promise<number[]> {
+  const text = `[${entry.type}] ${entry.content} ${entry.tags.join(' ')}`;
+  return embedText(text, model);
+}
+
+// ── 添加记忆向量 ──
+
+export async function addMemoryVector(entry: MemoryEntry, embedding: number[], colId: string): Promise<void> {
+  const vs = await initMemoryVectorStore();
+  const item: VectorItem = {
+    id: `vec_${entry.id}`,
+    collectionId: colId,
+    docId: entry.id,
+    filename: entry.type,
+    chunkIndex: 0,
+    text: entry.content,
+    charCount: entry.content.length,
+    embedding
+  };
+  await vs.addVectors([item]);
+}
+
+// ── 更新记忆向量 ──
+
+export async function updateMemoryVector(entry: MemoryEntry, embedding: number[], colId: string): Promise<void> {
+  // 先删除旧的，再添加新的
+  await deleteMemoryVector(entry.id, colId);
+  await addMemoryVector(entry, embedding, colId);
+}
+
+// ── 删除记忆向量 ──
+
+export async function deleteMemoryVector(entryId: string, colId: string): Promise<void> {
+  const vs = await initMemoryVectorStore();
+  await vs.deleteVectorsByDocument(colId, entryId);
+}
+
+// ── 向量搜索记忆 ──
+
+export async function searchMemoriesByVector(query: string, colId: string, topK = 8): Promise<SearchResult[]> {
+  const vs = await initMemoryVectorStore();
+  const col = await vs.getCollection(colId);
+  if (!col || !col.embeddingModel) return [];
+
+  const queryEmbedding = await embedText(query, col.embeddingModel);
+  const results = await vs.search(colId, queryEmbedding, topK);
+  return results;
+}
+
+// ── 重新索引所有记忆 ──
+
+export async function reindexAllMemories(
+  entries: MemoryEntry[],
+  embedModel: string,
+  colId: string,
+  onProgress?: (done: number, total: number) => void
+): Promise<void> {
+  const vs = await initMemoryVectorStore();
+
+  // 清空集合中的旧向量
+  const oldVectors = await vs.getVectorsByCollection(colId);
+  if (oldVectors.length > 0) {
+    await vs.deleteCollection(colId);
+    const col = await vs.createCollection(MEMORY_COLLECTION_NAME, embedModel);
+    memoryCollectionId = col.id;
+    colId = col.id;
+  }
+
+  logMemory(`开始重新索引: ${entries.length} 条记忆`);
+
+  for (let i = 0; i < entries.length; i++) {
+    const entry = entries[i];
+    try {
+      const embedding = await embedMemoryEntry(entry, embedModel);
+      await addMemoryVector(entry, embedding, colId);
+    } catch (err) {
+      logError(`索引记忆失败: ${entry.id}`, (err as Error).message);
+    }
+    if (onProgress) onProgress(i + 1, entries.length);
+  }
+
+  logMemory(`重新索引完成: ${entries.length} 条`);
+}