feat: 升级至 v0.2.1 — 流式渲染修复、安全增强、工具自动执行
流式渲染修复： - runId 机制防止 abort 后旧流事件污染新 run - run lock 防止并发 run 污染引擎状态 - abort race 提前退出工具执行等待 - TERMINATED 状态通过 stateChange 发射 - tool_call_delta 流式参数拼接 + pending 占位替换 - 首轮卡片创建路径统一，traceStep 按 ID 精确匹配 - compressed 事件转发为 toast 通知 安全增强： - ConfirmationHook 支持持久化自动执行（跨会话） - 设置面板新增自动执行工具管理 UI - SandboxManager 双重安全校验 fail-closed - 审计日志链式哈希防篡改 - PromptInjectionDefender 中文注入标记清理 - scanCode 28 模式 + base64/$() 检测 - validatePath realpathSync 防符号链接逃逸 - code-search 使用 execFile 防命令注入 新增工具： - file_editor、code_search、task_manager、diff_viewer 其他： - Agent Loop 加 PARSING/REFLECTING 状态 + 指数退避重试 - MemoryManager TF-IDF 语义检索 - run_command Windows 中文编码修复（chcp 65001） - 版本号 0.2.0 → 0.2.1
This commit is contained in:
thzxx
@@ -0,0 +1,277 @@
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/**
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* Memory Consolidator — 会话结束记忆固化器
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*
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* 每次 Agent 完成一轮对话后，调用 LLM 判断本次对话中哪些内容
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* 值得持久化到工作空间根目录的 MEMORY.md。
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*
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* 工作流程：
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* 1. 收集本次对话的 user message + assistant final answer + tool calls 摘要
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* 2. 读取当前 MEMORY.md 内容作为参考（避免重复）
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* 3. 调用 LLM，要求其返回 JSON 数组 [{section, entry}]
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* 4. 解析响应，调用 WorkspaceService.appendMemory 追加
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*
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* 安全约束：
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* - 仅追加新条目，不修改已有内容
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* - section 限定为预定义的 5 个分区
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* - 单次最多追加 5 条记忆，避免 LLM 过度提取
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* - LLM 调用失败时静默降级（不影响主流程）
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*
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* @see docs/生产级通用 AI Agent 智能体桌面应用：完整设计与构建指南.html — 第六章
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*/
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import { nanoid } from 'nanoid';
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import log from 'electron-log';
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import type { IMetonaProviderAdapter } from '../types/metona-adapter';
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import type { MetonaRequest, MetonaMessage } from '../types';
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import type { WorkspaceService } from '../../services/workspace.service';
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import type { IterationStep } from '../agent-loop/types';
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/** 允许写入的 MEMORY.md 分区（与 WorkspaceService.MEMORY_TEMPLATE 对齐） */
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const ALLOWED_SECTIONS = ['用户偏好', '项目上下文', '重要决策', '待办事项', '已知问题'] as const;
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type AllowedSection = typeof ALLOWED_SECTIONS[number];
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/** 单次固化最多追加的条目数 */
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const MAX_ENTRIES_PER_CONSOLIDATION = 5;
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/** 单条记忆最大长度 */
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const MAX_ENTRY_LENGTH = 500;
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export interface ConsolidationResult {
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appended: number;
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entries: Array<{ section: string; entry: string }>;
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skipped: number;
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}
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export class MemoryConsolidator {
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constructor(
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private adapter: IMetonaProviderAdapter,
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private workspaceService: WorkspaceService,
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) {}
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/**
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* 热切换 Adapter（配置变更时由 main.ts 调用）
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*/
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setAdapter(adapter: IMetonaProviderAdapter): void {
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this.adapter = adapter;
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}
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/**
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* 固化本次对话的重要记忆到 MEMORY.md
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*
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* @param userMessage 用户原始消息
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* @param assistantAnswer Agent 最终回答
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* @param iterations Agent Loop 迭代步骤（用于提取工具调用摘要）
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* @returns 固化结果
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*/
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async consolidate(
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userMessage: string,
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assistantAnswer: string,
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iterations: IterationStep[],
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): Promise<ConsolidationResult> {
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try {
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// 1. 构建对话摘要
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const conversationDigest = this.buildConversationDigest(userMessage, assistantAnswer, iterations);
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if (!conversationDigest) {
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return { appended: 0, entries: [], skipped: 0 };
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}
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// 2. 读取当前 MEMORY.md 内容（供 LLM 去重）
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const currentMemory = this.workspaceService.getFiles().memory;
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const memoryDigest = this.truncateMemoryForPrompt(currentMemory);
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// 3. 调用 LLM 提取需要持久化的记忆
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const llmResponse = await this.callLLMForExtraction(conversationDigest, memoryDigest);
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if (!llmResponse) {
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return { appended: 0, entries: [], skipped: 0 };
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}
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// 4. 解析 JSON 响应
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const extracted = this.parseExtractionResponse(llmResponse);
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if (extracted.length === 0) {
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log.debug('[MemoryConsolidator] No memories worth persisting');
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return { appended: 0, entries: [], skipped: 0 };
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}
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// 5. 过滤 + 截断 + 追加到 MEMORY.md
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const validEntries: Array<{ section: string; entry: string }> = [];
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let skipped = 0;
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for (const item of extracted.slice(0, MAX_ENTRIES_PER_CONSOLIDATION)) {
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if (!this.isValidEntry(item)) {
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skipped++;
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continue;
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}
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const truncatedEntry = item.entry.slice(0, MAX_ENTRY_LENGTH);
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try {
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this.workspaceService.appendMemory(item.section, truncatedEntry);
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validEntries.push({ section: item.section, entry: truncatedEntry });
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} catch (err) {
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log.warn(`[MemoryConsolidator] Failed to append to section "${item.section}":`, err);
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skipped++;
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}
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}
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if (validEntries.length > 0) {
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log.info(`[MemoryConsolidator] Persisted ${validEntries.length} memories to MEMORY.md (skipped: ${skipped})`);
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}
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return { appended: validEntries.length, entries: validEntries, skipped };
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} catch (error) {
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log.error('[MemoryConsolidator] Consolidation failed:', error);
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return { appended: 0, entries: [], skipped: 0 };
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}
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}
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/**
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* 构建对话摘要（供 LLM 分析）
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*/
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private buildConversationDigest(
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userMessage: string,
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assistantAnswer: string,
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iterations: IterationStep[],
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): string {
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const parts: string[] = [];
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// 用户消息
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parts.push(`[USER] ${this.truncate(userMessage, 2000)}`);
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// 工具调用摘要（如果有）
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const toolSummaries: string[] = [];
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for (const iter of iterations) {
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if (!iter.toolCalls?.length) continue;
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for (let i = 0; i < iter.toolCalls.length; i++) {
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const tc = iter.toolCalls[i];
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const result = iter.toolResults?.find((r) => r.toolCallId === tc.id);
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const status = result?.success ? 'ok' : 'error';
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const resultPreview = result?.result
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? this.truncate(JSON.stringify(result.result), 200)
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: result?.error ?? '';
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toolSummaries.push(` - ${tc.name}(${this.truncate(JSON.stringify(tc.args), 100)}) [${status}]${resultPreview ? ': ' + resultPreview : ''}`);
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}
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}
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if (toolSummaries.length > 0) {
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parts.push(`[TOOLS]\n${toolSummaries.join('\n')}`);
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}
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// Agent 最终回答
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parts.push(`[ASSISTANT] ${this.truncate(assistantAnswer, 2000)}`);
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return parts.join('\n\n');
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}
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/**
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* 截断 MEMORY.md 内容用于 prompt（避免过长）
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*/
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private truncateMemoryForPrompt(memory: string): string {
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if (!memory) return '(empty)';
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// 截取前 3000 字符，保留分区结构概览
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if (memory.length <= 3000) return memory;
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return memory.slice(0, 3000) + '\n... (truncated)';
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}
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/**
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* 调用 LLM 提取需要持久化的记忆
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*/
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private async callLLMForExtraction(
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conversationDigest: string,
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currentMemory: string,
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): Promise<string | null> {
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const sectionsList = ALLOWED_SECTIONS.map((s) => `"${s}"`).join(', ');
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const request: MetonaRequest = {
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meta: {
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sessionId: 'memory-consolidation',
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iteration: 0,
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requestId: `mc_${nanoid(12)}`,
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timestamp: Date.now(),
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agentVersion: '1.0.0',
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},
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systemPrompt: {
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roleDefinition: 'You are a memory curator for an AI agent. Your job is to decide what information from the current conversation is worth persisting to the agent\'s long-term memory file (MEMORY.md) for future sessions.',
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outputConstraints: [
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'Analyze the conversation below and extract ONLY information that meets ALL of these criteria:',
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'1. Long-term value: will be useful in future conversations (not transient task state)',
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'2. Not already present in the current MEMORY.md (avoid duplicates)',
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'3. Concrete and actionable (not vague observations)',
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'',
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'Good candidates: user preferences, project facts, important decisions, pending todos, known issues.',
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'Bad candidates: temporary tool results, trivial conversation, already-known facts.',
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'',
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`Output a JSON array. Each element: {"section": one of ${sectionsList}, "entry": concise description (max 200 chars, in the conversation language)}`,
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'If nothing is worth persisting, output an empty array: []',
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'Output ONLY the JSON array, no markdown fences, no explanation.',
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].join('\n'),
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safetyGuidelines: 'Do not persist sensitive data (passwords, API keys, tokens). Do not persist user personal information beyond what is necessary for the agent to function.',
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},
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messages: [{
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role: 'user',
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content: `## Current MEMORY.md content:\n\n${currentMemory}\n\n## Current conversation:\n\n${conversationDigest}\n\n## Task:\nExtract information worth persisting. Output JSON array only.`,
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timestamp: Date.now(),
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}],
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params: {
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maxTokens: 1024,
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temperature: 0.0,
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stream: false,
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thinkingEnabled: false,
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thinkingEffort: 'low',
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},
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};
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try {
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const response = await this.adapter.chat(request);
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return response.content.trim();
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} catch (error) {
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log.warn('[MemoryConsolidator] LLM call failed:', (error as Error).message);
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return null;
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}
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}
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/**
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* 解析 LLM 的提取响应
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*/
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private parseExtractionResponse(raw: string): Array<{ section: string; entry: string }> {
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if (!raw) return [];
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// 尝试直接解析 JSON
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let cleaned = raw.trim();
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// 移除可能的 markdown 代码围栏
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if (cleaned.startsWith('```')) {
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cleaned = cleaned.replace(/^```(?:json)?\s*/i, '').replace(/\s*```$/, '').trim();
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}
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try {
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const parsed = JSON.parse(cleaned);
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if (!Array.isArray(parsed)) return [];
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return parsed
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.filter((item): item is { section: string; entry: string } =>
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typeof item === 'object' && item !== null &&
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typeof item.section === 'string' && typeof item.entry === 'string',
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)
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.map((item) => ({
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section: item.section.trim(),
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entry: item.entry.trim(),
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}))
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.filter((item) => item.section && item.entry);
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} catch {
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log.warn('[MemoryConsolidator] Failed to parse LLM response as JSON:', cleaned.slice(0, 200));
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return [];
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}
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}
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/**
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* 校验条目是否合法（section 在允许列表内）
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*/
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private isValidEntry(item: { section: string; entry: string }): boolean {
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return (ALLOWED_SECTIONS as readonly string[]).includes(item.section) && item.entry.length > 0;
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}
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/**
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* 截断字符串
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*/
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private truncate(text: string, maxLen: number): string {
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if (text.length <= maxLen) return text;
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return text.slice(0, maxLen) + '...';
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}
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}
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@@ -4,6 +4,11 @@
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* 基于 SQLite（better-sqlite3）的三层记忆系统。
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* 表结构由 DatabaseService 统一创建，此处不再重复。
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*
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* v0.2.0 增强：
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* - TF-IDF 语义检索替代 LIKE 关键词搜索
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* - 时间衰减策略：老旧记忆权重降低
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* - IDF 缓存：避免每次搜索重新计算
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*
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* @see docs/生产级通用 AI Agent 智能体桌面应用：完整设计与构建指南.html — 第六章
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* @see standard/开发规范.md — 使用 better-sqlite3（禁止自写数据库层）
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*/
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@@ -38,17 +43,258 @@ interface MemorySearchOptions {
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minImportance?: number;
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}
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/** 分词：将文本拆分为词项（支持中英文） */
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function tokenize(text: string): string[] {
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// 转小写，按非字母数字字符分割
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const lower = text.toLowerCase();
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// 英文词
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const words = lower.match(/[a-z][a-z0-9_-]{1,}/g) ?? [];
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// 中文 bigram（相邻两字组合），覆盖 CJK 统一表意、扩展 A、平假名/片假名、谚文
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const cjkChars = lower.match(/[\u4e00-\u9fff\u3400-\u4dbf\u3040-\u30ff\uac00-\ud7af]/g) ?? [];
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const bigrams: string[] = [];
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for (let i = 0; i < cjkChars.length - 1; i++) {
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bigrams.push(cjkChars[i] + cjkChars[i + 1]);
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}
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// 单字 CJK 补 unigram（避免单字文档无 token）
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if (cjkChars.length === 1) {
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bigrams.push(cjkChars[0]);
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}
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return [...words, ...bigrams];
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}
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/** 计算词频（TF） */
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function computeTF(tokens: string[]): Map<string, number> {
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const tf = new Map<string, number>();
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for (const token of tokens) {
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tf.set(token, (tf.get(token) ?? 0) + 1);
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}
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// 归一化
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const total = tokens.length || 1;
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for (const [key, val] of tf) {
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tf.set(key, val / total);
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}
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return tf;
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}
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/** 计算余弦相似度的点积部分 */
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function dotProduct(tf1: Map<string, number>, tf2: Map<string, number>, idf: Map<string, number>): number {
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let sum = 0;
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for (const [term, freq1] of tf1) {
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const freq2 = tf2.get(term);
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if (freq2 !== undefined) {
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const idfVal = idf.get(term) ?? 1;
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sum += freq1 * freq2 * idfVal * idfVal;
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}
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}
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return sum;
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}
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/** 计算向量模长 */
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function vectorNorm(tf: Map<string, number>, idf: Map<string, number>): number {
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let sum = 0;
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for (const [term, freq] of tf) {
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const idfVal = idf.get(term) ?? 1;
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sum += (freq * idfVal) ** 2;
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}
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return Math.sqrt(sum);
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}
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/** 时间衰减权重：30 天半衰期（age=30 时 weight=0.5） */
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function timeDecayWeight(createdAt: number, now: number = Date.now()): number {
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const ageDays = Math.max(0, (now - createdAt) / (24 * 60 * 60 * 1000));
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const halfLifeDays = 30;
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return Math.pow(0.5, ageDays / halfLifeDays);
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}
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/**
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* 记忆管理器
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*/
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export class MemoryManager {
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/** IDF 缓存：词项 -> 文档频率 */
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private idfCache = new Map<string, number>();
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/** 缓存的记忆总数 */
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private cachedDocCount = 0;
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/** 缓存最后更新时间 */
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private cacheUpdatedAt = 0;
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/** 缓存有效期（5 分钟） */
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private readonly CACHE_TTL = 5 * 60 * 1000;
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constructor(private getDB: () => Database.Database) {}
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/**
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* 初始化（表结构由 DatabaseService 创建）
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*/
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initialize(): void {
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log.info('MemoryManager initialized');
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log.info('MemoryManager initialized (v0.2.0: TF-IDF enabled)');
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}
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/**
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* 更新 IDF 缓存
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||||
*/
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private updateIdfCache(): void {
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const now = Date.now();
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if (now - this.cacheUpdatedAt < this.CACHE_TTL && this.cachedDocCount > 0) {
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||||
return; // 缓存未过期
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}
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||||
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const db = this.getDB();
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this.idfCache.clear();
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// 获取所有记忆内容（episodic + semantic + working）
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const episodicRows = db.prepare('SELECT content, summary FROM episodic_memories').all() as Array<{ content: string; summary: string | null }>;
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const semanticRows = db.prepare('SELECT value FROM semantic_memories').all() as Array<{ value: string }>;
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const workingRows = db.prepare('SELECT value FROM working_memories').all() as Array<{ value: string }>;
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||||
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const allDocs = [
|
||||
...episodicRows.map((r) => r.content + ' ' + (r.summary ?? '')),
|
||||
...semanticRows.map((r) => r.value),
|
||||
...workingRows.map((r) => r.value),
|
||||
];
|
||||
|
||||
this.cachedDocCount = allDocs.length;
|
||||
const docFreq = new Map<string, number>();
|
||||
|
||||
for (const doc of allDocs) {
|
||||
const tokens = new Set(tokenize(doc));
|
||||
for (const token of tokens) {
|
||||
docFreq.set(token, (docFreq.get(token) ?? 0) + 1);
|
||||
}
|
||||
}
|
||||
|
||||
// IDF = log((N+1)/(df+1)) + 1（Sklearn 风格平滑），确保非负
|
||||
for (const [term, df] of docFreq) {
|
||||
this.idfCache.set(term, Math.log((this.cachedDocCount + 1) / (df + 1)) + 1);
|
||||
}
|
||||
|
||||
this.cacheUpdatedAt = now;
|
||||
}
|
||||
|
||||
/**
|
||||
* TF-IDF 相似度搜索
|
||||
*/
|
||||
private tfidfSearch(query: string, options: MemorySearchOptions): SearchResult[] {
|
||||
const db = this.getDB();
|
||||
this.updateIdfCache();
|
||||
|
||||
const queryTokens = tokenize(query);
|
||||
if (queryTokens.length === 0) return [];
|
||||
|
||||
const queryTF = computeTF(queryTokens);
|
||||
const queryNorm = vectorNorm(queryTF, this.idfCache);
|
||||
if (queryNorm === 0) return [];
|
||||
|
||||
const { topK = 5, type, minImportance = 0 } = options;
|
||||
const results: SearchResult[] = [];
|
||||
const now = Date.now();
|
||||
|
||||
// 搜索 episodic 记忆
|
||||
if (!type || type === 'episodic') {
|
||||
const rows = db.prepare(`
|
||||
SELECT * FROM episodic_memories WHERE importance >= ?
|
||||
ORDER BY importance DESC, created_at DESC LIMIT ?
|
||||
`).all(minImportance, topK * 3) as Array<{
|
||||
id: string; session_id: string | null; content: string; summary: string | null;
|
||||
source: string; importance: number; created_at: number; expires_at: number | null;
|
||||
}>;
|
||||
|
||||
for (const row of rows) {
|
||||
const docText = row.content + ' ' + (row.summary ?? '');
|
||||
const docTokens = tokenize(docText);
|
||||
const docTF = computeTF(docTokens);
|
||||
const docNorm = vectorNorm(docTF, this.idfCache);
|
||||
|
||||
if (docNorm === 0) continue;
|
||||
|
||||
const dotProd = dotProduct(queryTF, docTF, this.idfCache);
|
||||
const cosineSim = dotProd / (queryNorm * docNorm);
|
||||
|
||||
// 时间衰减
|
||||
const decayWeight = timeDecayWeight(row.created_at, now);
|
||||
// 最终分数 = 余弦相似度 * 时间衰减 * 重要度权重
|
||||
const finalScore = cosineSim * decayWeight * (0.5 + row.importance * 0.5);
|
||||
|
||||
if (finalScore > 0) {
|
||||
results.push({
|
||||
id: row.id, type: 'episodic', content: row.content,
|
||||
summary: row.summary ?? undefined, source: row.source as MemorySource,
|
||||
importance: row.importance, sessionId: row.session_id ?? undefined,
|
||||
createdAt: row.created_at, expiresAt: row.expires_at ?? undefined,
|
||||
score: finalScore,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 搜索 semantic 记忆
|
||||
if (!type || type === 'semantic') {
|
||||
const rows = db.prepare(`
|
||||
SELECT * FROM semantic_memories WHERE confidence >= ?
|
||||
ORDER BY confidence DESC, access_count DESC LIMIT ?
|
||||
`).all(minImportance, Math.ceil(topK * 1.5)) as Array<{
|
||||
id: string; key: string; value: string; category: string | null;
|
||||
confidence: number; source_session: string | null; created_at: number;
|
||||
}>;
|
||||
|
||||
for (const row of rows) {
|
||||
const docText = row.key + ' ' + row.value;
|
||||
const docTokens = tokenize(docText);
|
||||
const docTF = computeTF(docTokens);
|
||||
const docNorm = vectorNorm(docTF, this.idfCache);
|
||||
|
||||
if (docNorm === 0) continue;
|
||||
|
||||
const dotProd = dotProduct(queryTF, docTF, this.idfCache);
|
||||
const cosineSim = dotProd / (queryNorm * docNorm);
|
||||
|
||||
const decayWeight = timeDecayWeight(row.created_at, now);
|
||||
const finalScore = cosineSim * decayWeight * (0.5 + row.confidence * 0.5);
|
||||
|
||||
if (finalScore > 0) {
|
||||
results.push({
|
||||
id: row.id, type: 'semantic', content: row.value,
|
||||
source: 'imported', importance: row.confidence,
|
||||
sessionId: row.source_session ?? undefined,
|
||||
createdAt: row.created_at, score: finalScore,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 搜索 working 记忆
|
||||
if (!type || type === 'working') {
|
||||
const rows = db.prepare(`
|
||||
SELECT * FROM working_memories ORDER BY updated_at DESC LIMIT ?
|
||||
`).all(topK * 3) as Array<{
|
||||
id: string; session_id: string; task_id: string;
|
||||
key: string; value: string; updated_at: number;
|
||||
}>;
|
||||
|
||||
for (const row of rows) {
|
||||
const docText = row.key + ' ' + row.value;
|
||||
const docTokens = tokenize(docText);
|
||||
const docTF = computeTF(docTokens);
|
||||
const docNorm = vectorNorm(docTF, this.idfCache);
|
||||
|
||||
if (docNorm === 0) continue;
|
||||
|
||||
const dotProd = dotProduct(queryTF, docTF, this.idfCache);
|
||||
const cosineSim = dotProd / (queryNorm * docNorm);
|
||||
|
||||
const decayWeight = timeDecayWeight(row.updated_at, now);
|
||||
const finalScore = cosineSim * decayWeight * 0.5;
|
||||
|
||||
if (finalScore > 0) {
|
||||
results.push({
|
||||
id: row.id, type: 'working', content: row.value,
|
||||
source: 'agent_thought', importance: 0.5,
|
||||
sessionId: row.session_id, createdAt: row.updated_at,
|
||||
score: finalScore,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return results.sort((a, b) => b.score - a.score).slice(0, topK);
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -81,26 +327,44 @@ export class MemoryManager {
|
||||
break;
|
||||
}
|
||||
|
||||
// 使 IDF 缓存失效
|
||||
this.cacheUpdatedAt = 0;
|
||||
|
||||
log.debug(`Memory stored: ${id} (${item.type})`);
|
||||
return id;
|
||||
}
|
||||
|
||||
/**
|
||||
* 检索记忆（关键词搜索）
|
||||
* 检索记忆（v0.2.0: TF-IDF 语义检索 + 时间衰减）
|
||||
*
|
||||
* v0.2.0 变更：
|
||||
* - 使用 TF-IDF 余弦相似度替代 LIKE 关键词搜索
|
||||
* - 支持中英文分词（英文按词，中文按 bigram）
|
||||
* - 时间衰减：30 天半衰期，老旧记忆权重降低
|
||||
* - IDF 缓存：5 分钟有效期，避免重复计算
|
||||
*/
|
||||
search(query: string, options: MemorySearchOptions = {}): SearchResult[] {
|
||||
const db = this.getDB();
|
||||
const { topK = 5, type, minImportance = 0 } = options;
|
||||
if (!query) return [];
|
||||
|
||||
const pattern = `%${query}%`;
|
||||
// v0.2.0: 优先使用 TF-IDF 语义搜索
|
||||
const tfidfResults = this.tfidfSearch(query, options);
|
||||
if (tfidfResults.length > 0) {
|
||||
return tfidfResults;
|
||||
}
|
||||
|
||||
// 回退：如果 TF-IDF 没有结果（如 IDF 缓存为空），使用 LIKE 关键词搜索
|
||||
// 转义 LIKE 通配符，避免用户输入的 % 和 _ 影响匹配
|
||||
const escapedQuery = query.replace(/[%_]/g, '\\$&');
|
||||
const pattern = `%${escapedQuery}%`;
|
||||
const results: SearchResult[] = [];
|
||||
|
||||
// 搜索情节记忆
|
||||
if (!type || type === 'episodic') {
|
||||
const rows = db.prepare(`
|
||||
SELECT * FROM episodic_memories
|
||||
WHERE (content LIKE ? OR summary LIKE ?) AND importance >= ?
|
||||
WHERE (content LIKE ? ESCAPE '\\' OR summary LIKE ? ESCAPE '\\') AND importance >= ?
|
||||
ORDER BY importance DESC, created_at DESC LIMIT ?
|
||||
`).all(pattern, pattern, minImportance, topK) as Array<{
|
||||
id: string; session_id: string | null; content: string; summary: string | null;
|
||||
@@ -112,7 +376,7 @@ export class MemoryManager {
|
||||
summary: row.summary ?? undefined, source: row.source as MemorySource,
|
||||
importance: row.importance, sessionId: row.session_id ?? undefined,
|
||||
createdAt: row.created_at, expiresAt: row.expires_at ?? undefined,
|
||||
score: row.importance,
|
||||
score: row.importance * timeDecayWeight(row.created_at),
|
||||
});
|
||||
}
|
||||
}
|
||||
@@ -121,7 +385,7 @@ export class MemoryManager {
|
||||
if (!type || type === 'semantic') {
|
||||
const rows = db.prepare(`
|
||||
SELECT * FROM semantic_memories
|
||||
WHERE (key LIKE ? OR value LIKE ?) AND confidence >= ?
|
||||
WHERE (key LIKE ? ESCAPE '\\' OR value LIKE ? ESCAPE '\\') AND confidence >= ?
|
||||
ORDER BY confidence DESC, access_count DESC LIMIT ?
|
||||
`).all(pattern, pattern, minImportance, Math.ceil(topK / 2)) as Array<{
|
||||
id: string; key: string; value: string; category: string | null;
|
||||
@@ -132,7 +396,7 @@ export class MemoryManager {
|
||||
id: row.id, type: 'semantic', content: row.value,
|
||||
source: 'imported', importance: row.confidence,
|
||||
sessionId: row.source_session ?? undefined,
|
||||
createdAt: row.created_at, score: row.confidence,
|
||||
createdAt: row.created_at, score: row.confidence * timeDecayWeight(row.created_at),
|
||||
});
|
||||
}
|
||||
}
|
||||
@@ -141,7 +405,7 @@ export class MemoryManager {
|
||||
if (type === 'working') {
|
||||
const rows = db.prepare(`
|
||||
SELECT * FROM working_memories
|
||||
WHERE (key LIKE ? OR value LIKE ?)
|
||||
WHERE (key LIKE ? ESCAPE '\\' OR value LIKE ? ESCAPE '\\')
|
||||
ORDER BY updated_at DESC LIMIT ?
|
||||
`).all(pattern, pattern, topK) as Array<{
|
||||
id: string; session_id: string; task_id: string;
|
||||
@@ -152,7 +416,7 @@ export class MemoryManager {
|
||||
id: row.id, type: 'working', content: row.value,
|
||||
source: 'agent_thought', importance: 0.5,
|
||||
sessionId: row.session_id, createdAt: row.updated_at,
|
||||
score: 0.3, // 工作记忆权重较低
|
||||
score: 0.3 * timeDecayWeight(row.updated_at),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user