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
This commit is contained in:
OpenClaw Agent
@@ -166,17 +166,6 @@ export function appendMessageDOM(msg: ChatMessage, index: number): void {
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contentHtml += `<div class="msg-stats">${stats.map((s, i) => `<span${statClasses[i] ? ` class="${statClasses[i]}"` : ''}>${s}</span>`).join(' · ')}</div>`;
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}
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if (msg.role === 'assistant' && msg.ragSources && msg.ragSources.length > 0) {
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const sourcesHtml = msg.ragSources.map((s, i) => {
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const scorePercent = s.score ? `${(s.score * 100).toFixed(0)}%` : '';
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return `<div class="rag-source-item">
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<span class="rag-source-name">📄 来源 ${i + 1}: ${escapeHtml(s.filename)}</span>
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${scorePercent ? `<span class="rag-source-score">${scorePercent}</span>` : ''}
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</div>`;
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}).join('');
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contentHtml += `<div class="rag-sources"><div class="rag-sources-header">🧠 基于知识库回答</div>${sourcesHtml}</div>`;
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}
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if (msg.role === 'assistant' && msg.toolCalls && msg.toolCalls.length > 0) {
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contentHtml += '<div class="tool-calls-container">';
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for (const tc of msg.toolCalls) {
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@@ -10,14 +10,13 @@ import {
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updateLastAssistantMessage, clearMessages, safeMarkdown, enableAutoScroll, updateTotalTokens
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} from './chat-area.js';
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import { showToast } from './toast.js';
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import { isRagEnabled, performRagRetrieval } from './kb-modal.js';
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import { ChatDB } from '../db/chat-db.js';
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import { OllamaAPI } from '../api/ollama.js';
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import { runAgentLoop } from '../services/agent-engine.js';
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import { searchMemories, buildMemoryContext, markMemoryUsed, extractMemoriesFromConversation, isMemoryEnabled } from '../services/memory-manager.js';
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import { showToolConfirm } from './tool-confirm-modal.js';
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import { logInfo, logStream, logError, logSuccess, logWarn } from '../services/log-service.js';
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import type { ChatSession, ChatMessage, OllamaStreamChunk, RagSource, FileContent, ChatFile, ToolCallRecord } from '../types.js';
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import type { ChatSession, ChatMessage, OllamaStreamChunk, FileContent, ChatFile, ToolCallRecord } from '../types.js';
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let chatInputEl: HTMLTextAreaElement;
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let btnSendEl: HTMLButtonElement;
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@@ -396,61 +395,7 @@ export async function sendMessage(): Promise<void> {
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}
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}
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let ragSources: RagSource[] | null = null;
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if (isRagEnabled()) {
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appendSystemMessage('🧠 正在检索知识库...', 'rag-status');
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try {
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const textForRag = text;
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const ragResult = await performRagRetrieval(textForRag);
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if (ragResult && ragResult.results && ragResult.results.length > 0) {
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const ctxLimit = numCtx || 24576;
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const outputReserve = 2048;
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const msgChars = apiMessages.reduce((sum, m) => sum + (m.content?.length || 0), 0);
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const systemChars = ((chatParams.system as string)?.length || 0);
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const usedTokens = Math.ceil((msgChars + systemChars) / 2);
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const ragBudget = Math.max(0, ctxLimit - outputReserve - usedTokens);
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const ragBudgetChars = ragBudget * 2;
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let ragContext = ragResult.results.map((r: any, i: number) =>
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`[来源 ${i + 1}: ${r.filename}]\n${r.text}`
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).join('\n\n---\n\n');
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const ragTemplate = `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。\n\n=== 检索到的相关内容 ===\n${ragContext}\n=== 内容结束 ===`;
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if (ragContext.length + ragTemplate.length > ragBudgetChars) {
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ragContext = ragContext.slice(0, Math.max(0, ragBudgetChars - ragTemplate.length - 50)) + '\n\n[知识库内容过长，已截取最相关的部分]';
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appendSystemMessage(`⚠️ 知识库内容已截取（预算 ${ragBudgetChars} 字符）`, 'rag-status');
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}
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const ragPrompt = `你是一个知识库问答助手。以下是检索到的相关文档片段，请基于这些内容回答用户的问题。如果检索到的内容无法回答问题，请如实说明。\n\n=== 检索到的相关内容 ===\n${ragContext}\n=== 内容结束 ===\n\n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"）。`;
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chatParams.system = chatParams.system
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? (chatParams.system as string) + '\n\n' + ragPrompt
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: ragPrompt;
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ragSources = ragResult.results.map((r: any) => ({
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filename: r.filename,
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score: r.score,
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text: truncate(r.text, 100)
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}));
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const sourceNames = [...new Set(ragSources.map(s => s.filename))];
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appendSystemMessage(`🧠 已检索 ${ragSources.length} 个相关片段（来源：${sourceNames.join('、')}）`, 'rag-status');
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} else {
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appendSystemMessage('🧠 知识库未检索到相关内容，使用通用知识回答', 'rag-status');
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}
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} catch (ragErr) {
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logError('RAG 检索失败', (ragErr as Error).message);
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appendSystemMessage(`🧠 知识库检索失败: ${(ragErr as Error).message}`, 'rag-status');
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}
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}
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let ragStatusCleared = false;
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await api.chatStream(chatParams as any, (chunk: OllamaStreamChunk) => {
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if (!ragStatusCleared && chunk.message?.content) {
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ragStatusCleared = true;
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document.querySelectorAll('.rag-status').forEach(el => el.remove());
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}
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if (chunk.message) {
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if (chunk.message.content) assistantContent += chunk.message.content;
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const think = chunk.message.thinking || chunk.message.reasoning_content;
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@@ -487,8 +432,7 @@ export async function sendMessage(): Promise<void> {
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timestamp: Date.now(),
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...(modelName && { model: modelName }),
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...(thinkContent && { think: thinkContent }),
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...(finalStats && { eval_count: finalStats.eval_count, total_duration: finalStats.total_duration }),
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...(ragSources && { ragSources })
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...(finalStats && { eval_count: finalStats.eval_count, total_duration: finalStats.total_duration })
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};
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state.update(KEYS.CURRENT_SESSION, (session: any) => ({
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...session,
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@@ -500,20 +444,6 @@ export async function sendMessage(): Promise<void> {
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updateLastAssistantMessage(assistantContent, thinkContent || null, finalStats, modelName || undefined);
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}
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if (ragSources && ragSources.length > 0) {
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const lastAssistant = document.querySelector('#messagesContainer .message.assistant:last-of-type');
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if (lastAssistant) {
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const sourcesHtml = ragSources.map((s, i) => {
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const scorePercent = s.score ? `${(s.score * 100).toFixed(0)}%` : '';
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return `<div class="rag-source-item"><span class="rag-source-name">📄 来源 ${i + 1}: ${escapeHtml(s.filename)}</span>${scorePercent ? `<span class="rag-source-score">${scorePercent}</span>` : ''}</div>`;
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}).join('');
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const ragDiv = document.createElement('div');
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ragDiv.className = 'rag-sources';
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ragDiv.innerHTML = `<div class="rag-sources-header">🧠 基于知识库回答</div>${sourcesHtml}`;
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lastAssistant.querySelector('.msg-body')!.appendChild(ragDiv);
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}
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}
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await saveCurrentSession();
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updateTotalTokens();
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@@ -532,8 +462,6 @@ export async function sendMessage(): Promise<void> {
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}
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}
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} catch (err) {
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document.querySelectorAll('.rag-status').forEach(el => el.remove());
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if ((err as Error).name === 'AbortError') {
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const placeholder = document.querySelector('#messagesContainer .message.assistant.loading') as HTMLElement;
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const loadingDots = placeholder?.querySelector('.loading-dots');
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@@ -596,7 +524,7 @@ export async function sendMessage(): Promise<void> {
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errMsg = '❌ 连接失败。请检查：\n1. Ollama 是否正在运行\n2. 地址是否正确\n3. 是否设置了 OLLAMA_ORIGINS="*"';
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logError('连接失败', 'Ollama 服务不可达');
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} else if ((err as Error).message.includes('400')) {
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errMsg = '❌ 请求参数错误，可能是知识库内容超出模型上下文限制';
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errMsg = '❌ 请求参数错误，可能是上下文过长';
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logError('请求参数错误 (400)', (err as Error).message);
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} else if ((err as Error).message.includes('500')) {
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errMsg = '❌ Ollama 服务器错误，请检查 Ollama 日志';
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@@ -1,326 +0,0 @@
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/**
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* KBModal - 知识库管理面板
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*/
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import { state, KEYS } from '../state/state.js';
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import {
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initVectorStore, getVectorStore, addDocumentToKB,
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removeDocumentFromKB, getDocumentsInCollection, retrieveContext,
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buildRagSystemPrompt
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} from '../services/rag.js';
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import { formatSize, escapeHtml, truncate } from '../utils/utils.js';
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import { showToast } from './toast.js';
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import { showConfirm } from './prompt-modal.js';
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import { OllamaAPI } from '../api/ollama.js';
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import { logRAG, logError, logWarn } from '../services/log-service.js';
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import type { VectorCollection, SearchResult } from '../types.js';
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let kbModalEl: HTMLElement;
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let currentColId: string | null = null;
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let ragEnabled = false;
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let ragCollectionId: string | null = null;
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export function initKBModal(): void {
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kbModalEl = document.querySelector('#kbModal')!;
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document.querySelector('#btnKB')!.addEventListener('click', openKBModal);
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document.querySelector('#btnCloseKB')!.addEventListener('click', closeKBModal);
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kbModalEl.addEventListener('click', (e) => {
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if (e.target === kbModalEl) closeKBModal();
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});
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document.querySelector('#btnNewCollection')!.addEventListener('click', createCollection);
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document.querySelector('#btnUploadDoc')!.addEventListener('click', async () => {
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if (!currentColId) { showToast('请先选择一个知识库集合', 'warning'); return; }
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const bridge = (window as any).metonaDesktop;
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if (!bridge) return;
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const paths = await bridge.dialog.openFile({
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filters: [{ name: '文档', extensions: ['txt', 'md', 'py', 'js', 'ts', 'java', 'go', 'rs', 'cpp', 'c', 'h', 'hpp', 'rb', 'php', 'sh', 'html', 'css', 'json', 'yaml', 'yml', 'toml', 'xml', 'sql', 'csv'] }]
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});
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if (!paths || paths.length === 0) return;
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await handleDocUpload(paths);
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});
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document.querySelector('#toggleRAG')!.addEventListener('change', (e) => {
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ragEnabled = (e.target as HTMLInputElement).checked;
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if (ragEnabled && currentColId) {
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ragCollectionId = currentColId;
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showToast('RAG 知识检索已开启', 'success');
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} else if (ragEnabled && !currentColId) {
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(e.target as HTMLInputElement).checked = false;
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ragEnabled = false;
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showToast('请先选择一个知识库集合', 'warning');
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return;
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} else {
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ragCollectionId = null;
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showToast('RAG 知识检索已关闭', 'info');
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}
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updateRagBadge();
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});
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document.querySelector('#selectEmbedModel')!.addEventListener('change', async (e) => {
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if (!currentColId) return;
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const vs = getVectorStore();
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const col = await vs!.getCollection(currentColId);
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if (col) {
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col.embeddingModel = (e.target as HTMLSelectElement).value;
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await vs!.updateCollection(col);
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}
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});
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}
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export function isRagEnabled(): boolean {
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return ragEnabled && !!ragCollectionId;
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}
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export function getRagCollectionId(): string | null {
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return ragCollectionId;
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}
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export async function performRagRetrieval(query: string): Promise<{ context: string; results: SearchResult[]; ragPrompt: string } | null> {
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if (!isRagEnabled()) return null;
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try {
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const { context, results } = await retrieveContext(query, ragCollectionId!, 5);
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if (!context) return null;
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return { context, results, ragPrompt: buildRagSystemPrompt(context) };
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} catch (err) {
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logWarn('RAG 检索失败', (err as Error).message);
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return null;
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}
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}
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function updateRagBadge(): void {
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const badge = document.querySelector('#badgeRAG') as HTMLElement;
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if (!badge) return;
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badge.style.display = ragEnabled ? '' : 'none';
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}
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async function openKBModal(): Promise<void> {
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kbModalEl.style.display = '';
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logRAG('打开知识库管理');
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await refreshCollections();
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await populateEmbedModels();
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}
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function closeKBModal(): void {
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kbModalEl.style.display = 'none';
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}
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async function refreshCollections(): Promise<void> {
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const vs = await initVectorStore();
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const collections = await vs.getCollections();
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const listEl = document.querySelector('#kbCollectionList')!;
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if (collections.length === 0) {
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listEl.innerHTML = '<p class="text-muted" style="padding:16px;text-align:center;">暂无知识库，点击上方「新建集合」创建</p>';
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currentColId = null;
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refreshDocList();
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return;
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}
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listEl.innerHTML = collections.map(c => `
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<div class="kb-collection-item ${c.id === currentColId ? 'active' : ''}" data-id="${c.id}">
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<div class="kb-col-info">
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<span class="kb-col-name">${escapeHtml(c.name)}</span>
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<span class="kb-col-stats">${c.docCount || 0} 文档 · ${(c.chunkCount || 0)} 分块</span>
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</div>
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<button class="kb-col-delete icon-btn" data-id="${c.id}" title="删除集合">
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<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
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<polyline points="3 6 5 6 21 6"/><path d="M19 6v14a2 2 0 0 1-2 2H7a2 2 0 0 1-2-2V6m3 0V4a2 2 0 0 1 2-2h4a2 2 0 0 1 2 2v2"/>
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</svg>
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</button>
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</div>
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`).join('');
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listEl.querySelectorAll('.kb-collection-item').forEach(el => {
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el.addEventListener('click', async (e) => {
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if ((e.target as HTMLElement).closest('.kb-col-delete')) return;
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currentColId = (el as HTMLElement).dataset.id!;
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await refreshCollections();
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await refreshDocList();
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const vs = getVectorStore();
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const col = await vs!.getCollection(currentColId);
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if (col?.embeddingModel) {
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(document.querySelector('#selectEmbedModel') as HTMLSelectElement).value = col.embeddingModel;
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}
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});
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});
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listEl.querySelectorAll('.kb-col-delete').forEach(btn => {
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btn.addEventListener('click', async (e) => {
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e.stopPropagation();
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const id = (btn as HTMLElement).dataset.id!;
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if (!await showConfirm('确定删除此知识库集合？所有文档将被清除。', '删除知识库')) return;
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const vs = getVectorStore();
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await vs!.deleteCollection(id);
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if (currentColId === id) currentColId = null;
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if (ragCollectionId === id) {
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ragEnabled = false;
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ragCollectionId = null;
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(document.querySelector('#toggleRAG') as HTMLInputElement).checked = false;
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updateRagBadge();
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}
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showToast('集合已删除', 'success');
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await refreshCollections();
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await refreshDocList();
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});
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});
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}
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async function refreshDocList(): Promise<void> {
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const docListEl = document.querySelector('#kbDocList')!;
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if (!currentColId) {
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docListEl.innerHTML = '<p class="text-muted" style="padding:16px;text-align:center;">← 选择一个知识库集合</p>';
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return;
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}
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const docs = await getDocumentsInCollection(currentColId);
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if (docs.length === 0) {
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docListEl.innerHTML = '<p class="text-muted" style="padding:16px;text-align:center;">此集合暂无文档，上传文件开始构建知识库</p>';
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return;
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}
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docListEl.innerHTML = docs.map(d => `
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<div class="kb-doc-item">
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<span class="kb-doc-icon">📄</span>
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<div class="kb-doc-info">
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<span class="kb-doc-name">${escapeHtml(d.filename)}</span>
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<span class="kb-doc-meta">${d.chunkCount} 个分块</span>
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</div>
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<button class="kb-doc-delete icon-btn" data-docid="${d.docId}" title="删除文档">
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<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
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<line x1="18" y1="6" x2="6" y2="18"/><line x1="6" y1="6" x2="18" y2="18"/>
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</svg>
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</button>
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</div>
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`).join('');
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docListEl.querySelectorAll('.kb-doc-delete').forEach(btn => {
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btn.addEventListener('click', async () => {
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const docId = (btn as HTMLElement).dataset.docid!;
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if (!await showConfirm('确定删除此文档？', '删除文档')) return;
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await removeDocumentFromKB(currentColId!, docId);
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showToast('文档已删除', 'success');
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await refreshCollections();
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await refreshDocList();
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});
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});
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}
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async function createCollection(): Promise<void> {
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const nameInput = document.querySelector('#inputKBName') as HTMLInputElement;
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const name = nameInput.value.trim();
|
||||
if (!name) { showToast('请输入知识库名称', 'warning'); return; }
|
||||
const vs = await initVectorStore();
|
||||
const col = await vs.createCollection(name);
|
||||
currentColId = col.id;
|
||||
nameInput.value = '';
|
||||
showToast(`知识库「${name}」已创建`, 'success');
|
||||
logRAG(`创建集合: ${name}`);
|
||||
await refreshCollections();
|
||||
await refreshDocList();
|
||||
}
|
||||
|
||||
async function handleDocUpload(paths: string[]): Promise<void> {
|
||||
if (paths.length === 0) return;
|
||||
|
||||
const embedModel = (document.querySelector('#selectEmbedModel') as HTMLSelectElement).value;
|
||||
if (!embedModel) { showToast('请先选择一个嵌入模型', 'warning'); return; }
|
||||
|
||||
const bridge = (window as any).metonaDesktop;
|
||||
const progressEl = document.querySelector('#kbProgress') as HTMLElement;
|
||||
const progressTextEl = document.querySelector('#kbProgressText')!;
|
||||
|
||||
let successCount = 0;
|
||||
let failCount = 0;
|
||||
|
||||
for (const filePath of paths) {
|
||||
const name = filePath.split(/[/\\]/).pop() || filePath;
|
||||
|
||||
try {
|
||||
progressEl.style.display = '';
|
||||
progressTextEl.textContent = `正在处理 ${name}...`;
|
||||
|
||||
const result = await bridge.fs.readFile(filePath);
|
||||
if (!result.success) {
|
||||
showToast(`${name} 读取失败: ${result.error}`, 'error');
|
||||
failCount++;
|
||||
continue;
|
||||
}
|
||||
const content = result.content as string;
|
||||
const size = new TextEncoder().encode(content).length;
|
||||
|
||||
if (size > 5 * 1024 * 1024) {
|
||||
showToast(`${name} 超过 5MB 限制`, 'warning');
|
||||
failCount++;
|
||||
continue;
|
||||
}
|
||||
|
||||
const kbResult = await addDocumentToKB(
|
||||
currentColId!, name, content, embedModel,
|
||||
(done, total, msg) => {
|
||||
progressTextEl.textContent = `${name}: ${msg} (${done}/${total})`;
|
||||
}
|
||||
);
|
||||
|
||||
showToast(`${name} 已添加到知识库（${kbResult.chunkCount} 个分块）`, 'success');
|
||||
logRAG(`文档上传: ${name}`, `${kbResult.chunkCount} 个分块`);
|
||||
successCount++;
|
||||
} catch (err) {
|
||||
logError(`RAG 文档处理失败: ${name}`, (err as Error).message);
|
||||
showToast(`${name} 处理失败: ${(err as Error).message}`, 'error');
|
||||
failCount++;
|
||||
}
|
||||
}
|
||||
|
||||
progressEl.style.display = 'none';
|
||||
|
||||
if (fileArr.length > 1) {
|
||||
showToast(`上传完成：成功 ${successCount} 个，失败 ${failCount} 个`, failCount > 0 ? 'warning' : 'success');
|
||||
logRAG(`批量上传完成`, `成功 ${successCount}, 失败 ${failCount}`);
|
||||
}
|
||||
|
||||
await refreshCollections();
|
||||
await refreshDocList();
|
||||
}
|
||||
|
||||
async function populateEmbedModels(): Promise<void> {
|
||||
const select = document.querySelector('#selectEmbedModel') as HTMLSelectElement;
|
||||
const api = state.get<OllamaAPI>(KEYS.API);
|
||||
if (!api) return;
|
||||
|
||||
try {
|
||||
const data = await api.listModels();
|
||||
select.innerHTML = '<option value="">选择嵌入模型...</option>';
|
||||
if (!data.models || data.models.length === 0) {
|
||||
select.innerHTML = '<option value="">未安装任何模型</option>';
|
||||
return;
|
||||
}
|
||||
|
||||
const checks = await Promise.allSettled(
|
||||
data.models.map(async (m) => {
|
||||
const info = await api.showModel(m.name);
|
||||
const caps = info.capabilities || [];
|
||||
return { name: m.name, size: m.size, isEmbed: caps.includes('embedding') };
|
||||
})
|
||||
);
|
||||
|
||||
const embedModels = checks
|
||||
.filter(r => r.status === 'fulfilled' && r.value.isEmbed)
|
||||
.map(r => (r as PromiseFulfilledResult<{ name: string; size?: number; isEmbed: boolean }>).value);
|
||||
|
||||
embedModels.forEach(m => {
|
||||
const opt = document.createElement('option');
|
||||
opt.value = m.name;
|
||||
opt.textContent = m.name + (m.size ? ` (${formatSize(m.size)})` : '');
|
||||
select.appendChild(opt);
|
||||
});
|
||||
|
||||
if (embedModels.length === 0) {
|
||||
select.innerHTML = '<option value="">未检测到嵌入模型，请先 ollama pull</option>';
|
||||
}
|
||||
} catch (err) {
|
||||
logWarn('加载嵌入模型列表失败', (err as Error).message);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,343 @@
|
||||
/**
|
||||
* MemoryModal - Agent 记忆管理大模态框
|
||||
* 左右分栏布局：左侧分类筛选+统计，右侧搜索+列表+操作
|
||||
*/
|
||||
|
||||
import {
|
||||
getMemoryCache, searchMemories, addMemory, updateMemory, deleteMemory,
|
||||
clearAllMemories, isMemoryEnabled, setMemoryEnabled,
|
||||
getTypeIcon, getTypeName, isVectorMemoryEnabled, getEmbeddingModel
|
||||
} from '../services/memory-manager.js';
|
||||
import { showToast } from './toast.js';
|
||||
import { showPrompt, showConfirm } from './prompt-modal.js';
|
||||
import { debounce, escapeHtml, formatTime } from '../utils/utils.js';
|
||||
import type { MemoryEntry, MemoryType } from '../types.js';
|
||||
|
||||
let modalEl: HTMLElement;
|
||||
|
||||
// ── 初始化 ──
|
||||
|
||||
export function initMemoryModal(): void {
|
||||
// 创建模态框 DOM
|
||||
const overlay = document.createElement('div');
|
||||
overlay.className = 'modal-overlay';
|
||||
overlay.id = 'memoryModal';
|
||||
overlay.style.display = 'none';
|
||||
overlay.innerHTML = `
|
||||
<div class="modal">
|
||||
<div class="modal-header">
|
||||
<h3>🧠 Agent 记忆</h3>
|
||||
<div style="display:flex;align-items:center;gap:8px;">
|
||||
<label class="memory-toggle" title="自动记忆">
|
||||
<input type="checkbox" id="memoryModalToggle" checked>
|
||||
<span class="toggle-slider"></span>
|
||||
</label>
|
||||
<button class="icon-btn" id="btnCloseMemoryModal">
|
||||
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
|
||||
<line x1="18" y1="6" x2="6" y2="18"/><line x1="6" y1="6" x2="18" y2="18"/>
|
||||
</svg>
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
<div class="modal-body">
|
||||
<div class="memory-layout">
|
||||
<!-- 左侧边栏 -->
|
||||
<div class="memory-sidebar">
|
||||
<div class="memory-stats" id="memoryStats">
|
||||
<div class="memory-stats-item">
|
||||
<span>总计</span>
|
||||
<span class="memory-stats-count" id="memStatTotal">0</span>
|
||||
</div>
|
||||
<div class="memory-stats-item">
|
||||
<span>📌 事实</span>
|
||||
<span class="memory-stats-count" id="memStatFact">0</span>
|
||||
</div>
|
||||
<div class="memory-stats-item">
|
||||
<span>⚙️ 偏好</span>
|
||||
<span class="memory-stats-count" id="memStatPref">0</span>
|
||||
</div>
|
||||
<div class="memory-stats-item">
|
||||
<span>📏 规则</span>
|
||||
<span class="memory-stats-count" id="memStatRule">0</span>
|
||||
</div>
|
||||
</div>
|
||||
<div class="memory-categories" id="memoryCategories">
|
||||
<div class="memory-category-item active" data-type="">
|
||||
<span class="memory-category-icon">📋</span>
|
||||
<span>全部</span>
|
||||
<span class="memory-category-count" id="catCountAll">0</span>
|
||||
</div>
|
||||
<div class="memory-category-item" data-type="fact">
|
||||
<span class="memory-category-icon">📌</span>
|
||||
<span>事实</span>
|
||||
<span class="memory-category-count" id="catCountFact">0</span>
|
||||
</div>
|
||||
<div class="memory-category-item" data-type="preference">
|
||||
<span class="memory-category-icon">⚙️</span>
|
||||
<span>偏好</span>
|
||||
<span class="memory-category-count" id="catCountPref">0</span>
|
||||
</div>
|
||||
<div class="memory-category-item" data-type="rule">
|
||||
<span class="memory-category-icon">📏</span>
|
||||
<span>规则</span>
|
||||
<span class="memory-category-count" id="catCountRule">0</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<!-- 右侧主区域 -->
|
||||
<div class="memory-main">
|
||||
<div class="memory-toolbar">
|
||||
<div class="memory-search-wrap">
|
||||
<input type="text" id="memorySearchLg" class="memory-search-input-lg" placeholder="搜索记忆（支持关键词和语义搜索）...">
|
||||
</div>
|
||||
<span id="memoryVectorBadge" class="memory-vector-badge disabled">关键词模式</span>
|
||||
<button class="btn btn-sm btn-outline" id="btnAddMemoryLg">＋ 添加</button>
|
||||
<button class="btn btn-sm btn-danger-outline" id="btnClearMemoryLg">清空</button>
|
||||
</div>
|
||||
<div class="memory-list-lg" id="memoryListLg"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
`;
|
||||
|
||||
document.querySelector('#app')!.appendChild(overlay);
|
||||
modalEl = overlay;
|
||||
|
||||
// 绑定事件
|
||||
overlay.addEventListener('click', (e) => {
|
||||
if (e.target === overlay) closeMemoryModal();
|
||||
});
|
||||
overlay.querySelector('#btnCloseMemoryModal')!.addEventListener('click', closeMemoryModal);
|
||||
|
||||
// 自动记忆开关
|
||||
const toggle = overlay.querySelector('#memoryModalToggle') as HTMLInputElement;
|
||||
toggle.checked = isMemoryEnabled();
|
||||
toggle.addEventListener('change', () => {
|
||||
setMemoryEnabled(toggle.checked);
|
||||
showToast(toggle.checked ? '自动记忆已开启' : '自动记忆已关闭', 'info');
|
||||
});
|
||||
|
||||
// 搜索
|
||||
const searchInput = overlay.querySelector('#memorySearchLg') as HTMLInputElement;
|
||||
searchInput.addEventListener('input', debounce(() => renderList(), 300));
|
||||
|
||||
// 分类筛选
|
||||
overlay.querySelectorAll('.memory-category-item').forEach(el => {
|
||||
el.addEventListener('click', () => {
|
||||
overlay.querySelectorAll('.memory-category-item').forEach(c => c.classList.remove('active'));
|
||||
el.classList.add('active');
|
||||
renderList();
|
||||
});
|
||||
});
|
||||
|
||||
// 添加记忆
|
||||
overlay.querySelector('#btnAddMemoryLg')!.addEventListener('click', openAddDialog);
|
||||
|
||||
// 清空
|
||||
overlay.querySelector('#btnClearMemoryLg')!.addEventListener('click', async () => {
|
||||
if (await showConfirm('确定清空所有记忆？此操作不可恢复！', '清空记忆')) {
|
||||
await clearAllMemories();
|
||||
renderList();
|
||||
updateStats();
|
||||
showToast('已清空所有记忆', 'success');
|
||||
}
|
||||
});
|
||||
|
||||
// header 按钮
|
||||
document.querySelector('#btnMemory')?.addEventListener('click', openMemoryModal);
|
||||
|
||||
// 全局状态监听
|
||||
if (typeof (window as any).__memoryStateListener === 'undefined') {
|
||||
(window as any).__memoryStateListener = true;
|
||||
// 简单轮询：在模态框打开时监听变化
|
||||
}
|
||||
}
|
||||
|
||||
// ── 打开/关闭 ──
|
||||
|
||||
export function openMemoryModal(): void {
|
||||
modalEl.style.display = '';
|
||||
(modalEl.querySelector('#memoryModalToggle') as HTMLInputElement).checked = isMemoryEnabled();
|
||||
updateVectorBadge();
|
||||
updateStats();
|
||||
renderList();
|
||||
(modalEl.querySelector('#memorySearchLg') as HTMLInputElement).focus();
|
||||
}
|
||||
|
||||
export function closeMemoryModal(): void {
|
||||
modalEl.style.display = 'none';
|
||||
}
|
||||
|
||||
// ── 向量记忆状态 ──
|
||||
|
||||
function updateVectorBadge(): void {
|
||||
const badge = modalEl.querySelector('#memoryVectorBadge')!;
|
||||
if (isVectorMemoryEnabled()) {
|
||||
badge.textContent = `✅ 向量搜索 (${getEmbeddingModel()})`;
|
||||
badge.className = 'memory-vector-badge enabled';
|
||||
} else {
|
||||
badge.textContent = '关键词模式';
|
||||
badge.className = 'memory-vector-badge disabled';
|
||||
}
|
||||
}
|
||||
|
||||
// ── 统计 ──
|
||||
|
||||
function updateStats(): void {
|
||||
const entries = getMemoryCache();
|
||||
const counts = { total: entries.length, fact: 0, preference: 0, rule: 0 };
|
||||
for (const e of entries) {
|
||||
if (e.type in counts) (counts as any)[e.type]++;
|
||||
}
|
||||
const el = (id: string) => modalEl.querySelector(`#${id}`)!;
|
||||
el('memStatTotal').textContent = String(counts.total);
|
||||
el('memStatFact').textContent = String(counts.fact);
|
||||
el('memStatPref').textContent = String(counts.preference);
|
||||
el('memStatRule').textContent = String(counts.rule);
|
||||
el('catCountAll').textContent = String(counts.total);
|
||||
el('catCountFact').textContent = String(counts.fact);
|
||||
el('catCountPref').textContent = String(counts.preference);
|
||||
el('catCountRule').textContent = String(counts.rule);
|
||||
}
|
||||
|
||||
// ── 列表渲染 ──
|
||||
|
||||
function getActiveType(): string {
|
||||
const active = modalEl.querySelector('.memory-category-item.active') as HTMLElement;
|
||||
return active?.dataset.type || '';
|
||||
}
|
||||
|
||||
function renderList(): void {
|
||||
const listEl = modalEl.querySelector('#memoryListLg')!;
|
||||
const query = (modalEl.querySelector('#memorySearchLg') as HTMLInputElement).value.trim();
|
||||
const typeFilter = getActiveType();
|
||||
|
||||
let entries: MemoryEntry[];
|
||||
if (query) {
|
||||
entries = searchMemories(query, 50);
|
||||
} else {
|
||||
entries = getMemoryCache();
|
||||
}
|
||||
|
||||
if (typeFilter) {
|
||||
entries = entries.filter(e => e.type === typeFilter);
|
||||
}
|
||||
|
||||
entries.sort((a, b) => {
|
||||
if (a.importance !== b.importance) return b.importance - a.importance;
|
||||
return b.updatedAt - a.updatedAt;
|
||||
});
|
||||
|
||||
if (entries.length === 0) {
|
||||
listEl.innerHTML = `<div class="memory-empty-lg">${query ? '未找到匹配的记忆' : '暂无记忆，AI 会自动在对话中学习'}</div>`;
|
||||
return;
|
||||
}
|
||||
|
||||
listEl.innerHTML = entries.map(entry => {
|
||||
const icon = getTypeIcon(entry.type);
|
||||
const typeName = getTypeName(entry.type);
|
||||
const importanceStars = '★'.repeat(Math.min(entry.importance, 10)) + '☆'.repeat(Math.max(0, 10 - entry.importance));
|
||||
return `
|
||||
<div class="memory-item-lg" data-id="${entry.id}">
|
||||
<div class="memory-item-lg-header">
|
||||
<span>${icon}</span>
|
||||
<span class="memory-item-lg-type">${typeName}</span>
|
||||
<span class="memory-item-lg-importance">${importanceStars}</span>
|
||||
<button class="memory-item-lg-delete" data-id="${entry.id}" title="删除">✕</button>
|
||||
</div>
|
||||
<div class="memory-item-lg-content">${escapeHtml(entry.content)}</div>
|
||||
<div class="memory-item-lg-meta">
|
||||
${entry.tags.length > 0 ? `<span class="memory-item-lg-tags">${entry.tags.map(t => `<span class="memory-tag-lg">${escapeHtml(t)}</span>`).join('')}</span>` : ''}
|
||||
<span class="memory-item-lg-time">${formatTime(entry.updatedAt)}</span>
|
||||
</div>
|
||||
</div>
|
||||
`;
|
||||
}).join('');
|
||||
|
||||
// 删除按钮
|
||||
listEl.querySelectorAll('.memory-item-lg-delete').forEach(btn => {
|
||||
btn.addEventListener('click', async (e) => {
|
||||
e.stopPropagation();
|
||||
const id = (btn as HTMLElement).dataset.id!;
|
||||
await deleteMemory(id);
|
||||
renderList();
|
||||
updateStats();
|
||||
showToast('记忆已删除', 'info', 1500);
|
||||
});
|
||||
});
|
||||
|
||||
// 双击编辑
|
||||
listEl.querySelectorAll('.memory-item-lg-content').forEach(el => {
|
||||
el.addEventListener('dblclick', () => {
|
||||
const item = (el as HTMLElement).closest('.memory-item-lg')!;
|
||||
const id = item.getAttribute('data-id')!;
|
||||
const entry = getMemoryCache().find(e => e.id === id);
|
||||
if (entry) openEditDialog(entry);
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
// ── 添加对话框 ──
|
||||
|
||||
async function openAddDialog(): Promise<void> {
|
||||
const type = await showPrompt({
|
||||
title: '添加记忆',
|
||||
message: '选择记忆类型：',
|
||||
type: 'select',
|
||||
defaultValue: 'fact',
|
||||
options: [
|
||||
{ value: 'fact', label: '📌 事实 — 项目信息、身份背景' },
|
||||
{ value: 'preference', label: '⚙️ 偏好 — 语言风格、技术栈偏好' },
|
||||
{ value: 'rule', label: '📏 规则 — 编码规范、输出要求' }
|
||||
]
|
||||
});
|
||||
if (type === null) return;
|
||||
const memoryType = (type as MemoryType) || 'fact';
|
||||
|
||||
const content = await showPrompt({
|
||||
title: '添加记忆',
|
||||
message: '请输入记忆内容：',
|
||||
type: 'textarea',
|
||||
placeholder: '例如：用户正在开发一个 Electron 桌面应用'
|
||||
});
|
||||
if (!content?.trim()) return;
|
||||
|
||||
const importanceStr = await showPrompt({
|
||||
title: '添加记忆',
|
||||
message: '重要性（1-10，默认 5）：',
|
||||
defaultValue: '5',
|
||||
placeholder: '5'
|
||||
});
|
||||
const importance = Math.min(10, Math.max(1, parseInt(importanceStr || '5') || 5));
|
||||
|
||||
await addMemory({ type: memoryType, content: content.trim(), importance });
|
||||
renderList();
|
||||
updateStats();
|
||||
showToast('记忆已添加', 'success', 1500);
|
||||
}
|
||||
|
||||
// ── 编辑对话框 ──
|
||||
|
||||
async function openEditDialog(entry: MemoryEntry): Promise<void> {
|
||||
const newContent = await showPrompt({
|
||||
title: '编辑记忆',
|
||||
message: '修改记忆内容：',
|
||||
type: 'textarea',
|
||||
defaultValue: entry.content
|
||||
});
|
||||
if (newContent === null || newContent.trim() === entry.content) return;
|
||||
|
||||
const importanceStr = await showPrompt({
|
||||
title: '编辑记忆',
|
||||
message: '重要性（1-10）：',
|
||||
defaultValue: String(entry.importance),
|
||||
placeholder: String(entry.importance)
|
||||
});
|
||||
const importance = importanceStr ? Math.min(10, Math.max(1, parseInt(importanceStr) || entry.importance)) : entry.importance;
|
||||
|
||||
await updateMemory(entry.id, { content: newContent.trim(), importance });
|
||||
renderList();
|
||||
updateStats();
|
||||
showToast('记忆已更新', 'success', 1500);
|
||||
}
|
||||
@@ -1,253 +0,0 @@
|
||||
/**
|
||||
* MemoryPanel - Agent 记忆管理面板
|
||||
* 提供记忆的查看、搜索、编辑、删除功能
|
||||
*/
|
||||
|
||||
import { state, KEYS } from '../state/state.js';
|
||||
import {
|
||||
getMemoryCache, searchMemories, addMemory, updateMemory, deleteMemory,
|
||||
clearAllMemories, isMemoryEnabled, setMemoryEnabled,
|
||||
getTypeIcon, getTypeName
|
||||
} from '../services/memory-manager.js';
|
||||
import { showToast } from './toast.js';
|
||||
import { showPrompt, showConfirm } from './prompt-modal.js';
|
||||
import { debounce, escapeHtml, formatTime } from '../utils/utils.js';
|
||||
import type { MemoryEntry, MemoryType } from '../types.js';
|
||||
|
||||
let panelEl: HTMLElement;
|
||||
let isOpen = false;
|
||||
|
||||
export function initMemoryPanel(): void {
|
||||
// 绑定 header 中的记忆按钮
|
||||
const btnMemory = document.querySelector('#btnMemory');
|
||||
if (!btnMemory) return;
|
||||
|
||||
// 创建记忆面板
|
||||
const panel = document.createElement('div');
|
||||
panel.className = 'memory-panel';
|
||||
panel.id = 'memoryPanel';
|
||||
panel.style.display = 'none';
|
||||
panel.innerHTML = `
|
||||
<div class="memory-panel-header">
|
||||
<h3>🧠 Agent 记忆</h3>
|
||||
<div class="memory-panel-actions">
|
||||
<label class="memory-toggle" title="自动记忆">
|
||||
<input type="checkbox" id="toggleMemoryPanelEnabled" checked>
|
||||
<span class="toggle-slider-sm"></span>
|
||||
</label>
|
||||
<button class="icon-btn sm" id="btnCloseMemoryPanel" title="关闭">
|
||||
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
|
||||
<line x1="18" y1="6" x2="6" y2="18"/><line x1="6" y1="6" x2="18" y2="18"/>
|
||||
</svg>
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
<div class="memory-panel-search">
|
||||
<input type="text" id="memorySearchInput" placeholder="搜索记忆..." class="memory-search-input">
|
||||
<select id="memoryTypeFilter" class="memory-type-filter">
|
||||
<option value="">全部类型</option>
|
||||
<option value="fact">📌 事实</option>
|
||||
<option value="preference">⚙️ 偏好</option>
|
||||
<option value="rule">📏 规则</option>
|
||||
<option value="episode">📝 事件</option>
|
||||
</select>
|
||||
</div>
|
||||
<div class="memory-panel-actions-row">
|
||||
<button class="btn btn-sm btn-outline" id="btnAddMemory">＋ 添加记忆</button>
|
||||
<button class="btn btn-sm btn-danger-outline" id="btnClearMemories">清空</button>
|
||||
</div>
|
||||
<div class="memory-list" id="memoryList"></div>
|
||||
`;
|
||||
|
||||
document.querySelector('#app')!.appendChild(panel);
|
||||
panelEl = panel;
|
||||
|
||||
// 绑定事件
|
||||
btnMemory.addEventListener('click', togglePanel);
|
||||
panel.querySelector('#btnCloseMemoryPanel')!.addEventListener('click', () => setPanelOpen(false));
|
||||
|
||||
const toggleMemory = panel.querySelector('#toggleMemoryPanelEnabled') as HTMLInputElement;
|
||||
toggleMemory.checked = isMemoryEnabled();
|
||||
toggleMemory.addEventListener('change', () => {
|
||||
setMemoryEnabled(toggleMemory.checked);
|
||||
showToast(toggleMemory.checked ? '自动记忆已开启' : '自动记忆已关闭', 'info');
|
||||
});
|
||||
|
||||
const searchInput = panel.querySelector('#memorySearchInput') as HTMLInputElement;
|
||||
const typeFilter = panel.querySelector('#memoryTypeFilter') as HTMLSelectElement;
|
||||
searchInput.addEventListener('input', debounce(() => renderMemoryList(), 200));
|
||||
typeFilter.addEventListener('change', () => renderMemoryList());
|
||||
|
||||
panel.querySelector('#btnAddMemory')!.addEventListener('click', () => openAddMemoryDialog());
|
||||
panel.querySelector('#btnClearMemories')!.addEventListener('click', async () => {
|
||||
if (await showConfirm('确定清空所有记忆？此操作不可恢复！', '清空记忆')) {
|
||||
await clearAllMemories();
|
||||
renderMemoryList();
|
||||
updateMemoryCount();
|
||||
showToast('已清空所有记忆', 'success');
|
||||
}
|
||||
});
|
||||
|
||||
state.on('memoryEntries', () => {
|
||||
updateMemoryCount();
|
||||
if (isOpen) renderMemoryList();
|
||||
});
|
||||
|
||||
updateMemoryCount();
|
||||
}
|
||||
|
||||
function togglePanel(): void {
|
||||
setPanelOpen(!isOpen);
|
||||
}
|
||||
|
||||
function setPanelOpen(open: boolean): void {
|
||||
isOpen = open;
|
||||
panelEl.style.display = open ? '' : 'none';
|
||||
if (open) {
|
||||
renderMemoryList();
|
||||
(panelEl.querySelector('#memorySearchInput') as HTMLInputElement)?.focus();
|
||||
}
|
||||
}
|
||||
|
||||
function updateMemoryCount(): void {
|
||||
const count = getMemoryCache().length;
|
||||
const btn = document.querySelector('#btnMemory');
|
||||
if (btn) btn.setAttribute('title', count > 0 ? `Agent 记忆 (${count})` : 'Agent 记忆');
|
||||
}
|
||||
|
||||
function renderMemoryList(): void {
|
||||
const listEl = panelEl.querySelector('#memoryList')!;
|
||||
const searchInput = panelEl.querySelector('#memorySearchInput') as HTMLInputElement;
|
||||
const typeFilter = panelEl.querySelector('#memoryTypeFilter') as HTMLSelectElement;
|
||||
|
||||
const query = searchInput.value.trim();
|
||||
const type = typeFilter.value;
|
||||
|
||||
let entries: MemoryEntry[];
|
||||
|
||||
if (query) {
|
||||
entries = searchMemories(query, 20).map(r => ({
|
||||
...r,
|
||||
score: undefined
|
||||
}));
|
||||
} else {
|
||||
entries = getMemoryCache();
|
||||
}
|
||||
|
||||
if (type) {
|
||||
entries = entries.filter(e => e.type === type);
|
||||
}
|
||||
|
||||
entries.sort((a, b) => {
|
||||
if (a.importance !== b.importance) return b.importance - a.importance;
|
||||
return b.updatedAt - a.updatedAt;
|
||||
});
|
||||
|
||||
if (entries.length === 0) {
|
||||
listEl.innerHTML = `<div class="memory-empty">${query ? '未找到匹配的记忆' : '暂无记忆，AI 会自动在对话中学习'}</div>`;
|
||||
return;
|
||||
}
|
||||
|
||||
listEl.innerHTML = entries.map(entry => {
|
||||
const icon = getTypeIcon(entry.type);
|
||||
const typeName = getTypeName(entry.type);
|
||||
const importanceDots = '●'.repeat(Math.min(entry.importance, 10)) + '○'.repeat(Math.max(0, 10 - entry.importance));
|
||||
return `
|
||||
<div class="memory-item" data-id="${entry.id}">
|
||||
<div class="memory-item-header">
|
||||
<span class="memory-item-icon">${icon}</span>
|
||||
<span class="memory-item-type">${typeName}</span>
|
||||
<span class="memory-item-importance" title="重要性 ${entry.importance}/10">${importanceDots}</span>
|
||||
<button class="memory-item-delete" data-id="${entry.id}" title="删除">✕</button>
|
||||
</div>
|
||||
<div class="memory-item-content">${escapeHtml(entry.content)}</div>
|
||||
<div class="memory-item-meta">
|
||||
${entry.tags.length > 0 ? `<span class="memory-item-tags">${entry.tags.map(t => `<span class="memory-tag">${escapeHtml(t)}</span>`).join('')}</span>` : ''}
|
||||
<span class="memory-item-time">${formatTime(entry.updatedAt)}</span>
|
||||
</div>
|
||||
</div>
|
||||
`;
|
||||
}).join('');
|
||||
|
||||
// 绑定删除按钮
|
||||
listEl.querySelectorAll('.memory-item-delete').forEach(btn => {
|
||||
btn.addEventListener('click', async (e) => {
|
||||
e.stopPropagation();
|
||||
const id = (btn as HTMLElement).dataset.id!;
|
||||
await deleteMemory(id);
|
||||
renderMemoryList();
|
||||
updateMemoryCount();
|
||||
showToast('记忆已删除', 'info', 1500);
|
||||
});
|
||||
});
|
||||
|
||||
// 点击编辑
|
||||
listEl.querySelectorAll('.memory-item-content').forEach(el => {
|
||||
el.addEventListener('dblclick', () => {
|
||||
const item = (el as HTMLElement).closest('.memory-item')!;
|
||||
const id = item.getAttribute('data-id')!;
|
||||
const entry = getMemoryCache().find(e => e.id === id);
|
||||
if (entry) openEditMemoryDialog(entry);
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
async function openAddMemoryDialog(): Promise<void> {
|
||||
const type = await showPrompt({
|
||||
title: '添加记忆',
|
||||
message: '选择记忆类型：',
|
||||
type: 'select',
|
||||
defaultValue: 'fact',
|
||||
options: [
|
||||
{ value: 'fact', label: '📌 事实 — 项目信息、身份背景' },
|
||||
{ value: 'preference', label: '⚙️ 偏好 — 语言风格、技术栈偏好' },
|
||||
{ value: 'rule', label: '📏 规则 — 编码规范、输出要求' },
|
||||
{ value: 'episode', label: '📝 事件 — 完成的任务、结论' }
|
||||
]
|
||||
});
|
||||
if (type === null) return;
|
||||
const memoryType = (type as MemoryType) || 'fact';
|
||||
|
||||
const content = await showPrompt({
|
||||
title: '添加记忆',
|
||||
message: '请输入记忆内容：',
|
||||
type: 'textarea',
|
||||
placeholder: '例如：用户正在开发一个 Electron 桌面应用'
|
||||
});
|
||||
if (!content?.trim()) return;
|
||||
|
||||
const importanceStr = await showPrompt({
|
||||
title: '添加记忆',
|
||||
message: '重要性（1-10，默认 5）：',
|
||||
defaultValue: '5',
|
||||
placeholder: '5'
|
||||
});
|
||||
const importance = Math.min(10, Math.max(1, parseInt(importanceStr || '5') || 5));
|
||||
|
||||
await addMemory({ type: memoryType, content: content.trim(), importance });
|
||||
renderMemoryList();
|
||||
updateMemoryCount();
|
||||
showToast('记忆已添加', 'success', 1500);
|
||||
}
|
||||
|
||||
async function openEditMemoryDialog(entry: MemoryEntry): Promise<void> {
|
||||
const newContent = await showPrompt({
|
||||
title: '编辑记忆',
|
||||
message: '修改记忆内容：',
|
||||
type: 'textarea',
|
||||
defaultValue: entry.content
|
||||
});
|
||||
if (newContent === null || newContent.trim() === entry.content) return;
|
||||
|
||||
const importanceStr = await showPrompt({
|
||||
title: '编辑记忆',
|
||||
message: '重要性（1-10）：',
|
||||
defaultValue: String(entry.importance),
|
||||
placeholder: String(entry.importance)
|
||||
});
|
||||
const importance = importanceStr ? Math.min(10, Math.max(1, parseInt(importanceStr) || entry.importance)) : entry.importance;
|
||||
|
||||
await updateMemory(entry.id, { content: newContent.trim(), importance });
|
||||
renderMemoryList();
|
||||
showToast('记忆已更新', 'success', 1500);
|
||||
}
|
||||
@@ -5,7 +5,7 @@
|
||||
import { state, KEYS } from '../state/state.js';
|
||||
import { debounce } from '../utils/utils.js';
|
||||
import { encryptData, decryptData } from '../services/crypto.js';
|
||||
import { setMemoryEnabled } from '../services/memory-manager.js';
|
||||
import { setMemoryEnabled, setEmbeddingModel, getEmbeddingModel, isVectorMemoryEnabled, getMemoryCache } from '../services/memory-manager.js';
|
||||
import { setToolEnabled } from '../services/tool-registry.js';
|
||||
import { logSetting, logInfo, logWarn, logError, logSuccess } from '../services/log-service.js';
|
||||
import { checkConnection, updateConnectionInfo, updateRunningModels } from './header.js';
|
||||
@@ -153,6 +153,23 @@ export function initSettingsModal(): void {
|
||||
});
|
||||
}
|
||||
|
||||
// ── 向量记忆引擎设置 ──
|
||||
const selectEmbedModel = document.querySelector('#selectMemoryEmbedModel') as HTMLSelectElement;
|
||||
if (selectEmbedModel) {
|
||||
selectEmbedModel.addEventListener('change', async () => {
|
||||
const model = selectEmbedModel.value;
|
||||
await setEmbeddingModel(model);
|
||||
if (model) {
|
||||
showToast(`向量记忆已启用（${model}）`, 'success');
|
||||
logSetting('向量记忆引擎', `启用: ${model}`);
|
||||
} else {
|
||||
showToast('向量记忆已关闭，仅使用关键词匹配', 'info');
|
||||
logSetting('向量记忆引擎', '关闭');
|
||||
}
|
||||
updateMemoryVectorStatus();
|
||||
});
|
||||
}
|
||||
|
||||
// ── 工作空间目录设置 ──
|
||||
const inputWorkspaceDir = document.querySelector('#inputWorkspaceDir') as HTMLInputElement;
|
||||
const btnBrowseWorkspace = document.querySelector('#btnBrowseWorkspace') as HTMLButtonElement;
|
||||
@@ -206,6 +223,8 @@ export function openSettingsModal(): void {
|
||||
(document.querySelector('#inputServerUrl') as HTMLInputElement).value = state.get<OllamaAPI>(KEYS.API)?.baseUrl || '';
|
||||
updateConnectionInfo();
|
||||
updateRunningModels();
|
||||
populateMemoryEmbedModels();
|
||||
updateMemoryVectorStatus();
|
||||
}
|
||||
|
||||
export function closeSettingsModal(): void {
|
||||
@@ -269,6 +288,64 @@ async function exportAllSessions(): Promise<void> {
|
||||
}
|
||||
}
|
||||
|
||||
async function populateMemoryEmbedModels(): Promise<void> {
|
||||
const select = document.querySelector('#selectMemoryEmbedModel') as HTMLSelectElement;
|
||||
if (!select) return;
|
||||
const api = state.get<OllamaAPI>(KEYS.API);
|
||||
if (!api) return;
|
||||
|
||||
try {
|
||||
const data = await api.listModels();
|
||||
select.innerHTML = '<option value="">未选择（仅关键词模式）</option>';
|
||||
if (!data.models || data.models.length === 0) return;
|
||||
|
||||
// 获取每个模型的能力信息，筛选嵌入模型
|
||||
const checks = await Promise.allSettled(
|
||||
data.models.map(async (m) => {
|
||||
const info = await api.showModel(m.name);
|
||||
const caps = info.capabilities || [];
|
||||
return { name: m.name, size: m.size, isEmbed: caps.includes('embedding') };
|
||||
})
|
||||
);
|
||||
|
||||
const embedModels = checks
|
||||
.filter(r => r.status === 'fulfilled' && r.value.isEmbed)
|
||||
.map(r => (r as PromiseFulfilledResult<{ name: string; size?: number; isEmbed: boolean }>).value);
|
||||
|
||||
embedModels.forEach(m => {
|
||||
const opt = document.createElement('option');
|
||||
opt.value = m.name;
|
||||
opt.textContent = m.name;
|
||||
select.appendChild(opt);
|
||||
});
|
||||
|
||||
if (embedModels.length === 0) {
|
||||
select.innerHTML = '<option value="">未检测到嵌入模型，请先 ollama pull</option>';
|
||||
return;
|
||||
}
|
||||
|
||||
// 恢复已保存的选择
|
||||
const saved = getEmbeddingModel();
|
||||
if (saved) select.value = saved;
|
||||
} catch (err) {
|
||||
logWarn('加载嵌入模型列表失败', (err as Error).message);
|
||||
}
|
||||
}
|
||||
|
||||
function updateMemoryVectorStatus(): void {
|
||||
const statusEl = document.querySelector('#memoryVectorStatus');
|
||||
if (!statusEl) return;
|
||||
const model = getEmbeddingModel();
|
||||
if (model) {
|
||||
const memCount = getMemoryCache().length;
|
||||
statusEl.textContent = `✅ 向量搜索已启用（${model}）· ${memCount} 条记忆`;
|
||||
(statusEl as HTMLElement).style.color = 'var(--success)';
|
||||
} else {
|
||||
statusEl.textContent = 'ℹ️ 未选择嵌入模型，仅使用关键词匹配';
|
||||
(statusEl as HTMLElement).style.color = '';
|
||||
}
|
||||
}
|
||||
|
||||
async function importSessions(filePath: string): Promise<void> {
|
||||
const db = state.get<ChatDB | null>(KEYS.DB);
|
||||
if (!db) return;
|
||||
|
||||
+11
-65
@@ -46,14 +46,6 @@
|
||||
<line x1="12" y1="5" x2="12" y2="19"/><line x1="5" y1="12" x2="19" y2="12"/>
|
||||
</svg>
|
||||
</button>
|
||||
<button class="icon-btn" id="btnKB" title="知识库">
|
||||
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
|
||||
<path d="M4 19.5A2.5 2.5 0 0 1 6.5 17H20"/>
|
||||
<path d="M6.5 2H20v20H6.5A2.5 2.5 0 0 1 4 19.5v-15A2.5 2.5 0 0 1 6.5 2z"/>
|
||||
<line x1="8" y1="7" x2="16" y2="7"/>
|
||||
<line x1="8" y1="11" x2="14" y2="11"/>
|
||||
</svg>
|
||||
</button>
|
||||
<button class="icon-btn" id="btnMemory" title="Agent 记忆">
|
||||
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
|
||||
<path d="M12 2a7 7 0 0 1 7 7c0 2.38-1.19 4.47-3 5.74V17a1 1 0 0 1-1 1H9a1 1 0 0 1-1-1v-2.26C6.19 13.47 5 11.38 5 9a7 7 0 0 1 7-7z"/>
|
||||
@@ -93,7 +85,6 @@
|
||||
<span class="model-badge think-badge" id="badgeThink" style="display:none;">🧠 Think</span>
|
||||
<span class="model-badge vision-badge" id="badgeVision" style="display:none;">👁️ Vision</span>
|
||||
<span class="model-badge tools-badge" id="badgeTools" style="display:none;">🔧 Tools</span>
|
||||
<span class="model-badge rag-badge" id="badgeRAG" style="display:none;">📚 RAG</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -319,9 +310,16 @@
|
||||
<div class="tool-item"><span>📌 事实</span><span class="text-muted">项目信息、身份背景</span></div>
|
||||
<div class="tool-item"><span>⚙️ 偏好</span><span class="text-muted">语言风格、技术栈偏好</span></div>
|
||||
<div class="tool-item"><span>📏 规则</span><span class="text-muted">编码规范、输出格式要求</span></div>
|
||||
<div class="tool-item"><span>📝 事件</span><span class="text-muted">完成的任务、结论</span></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="setting-group">
|
||||
<label class="setting-label">🧠 向量记忆引擎</label>
|
||||
<p class="text-muted" style="font-size:11px;margin-bottom:8px;">选择嵌入模型以启用向量语义记忆。未选择时仅使用关键词匹配。</p>
|
||||
<select class="setting-input" id="selectMemoryEmbedModel">
|
||||
<option value="">未选择（仅关键词模式）</option>
|
||||
</select>
|
||||
<div id="memoryVectorStatus" class="text-muted" style="font-size:11px;margin-top:4px;"></div>
|
||||
</div>
|
||||
<div class="setting-group">
|
||||
<label class="setting-label">显存管理</label>
|
||||
<button class="btn btn-danger" id="btnReleaseVRAM">释放显存（卸载模型）</button>
|
||||
@@ -373,69 +371,17 @@
|
||||
</button>
|
||||
</div>
|
||||
<div class="modal-body">
|
||||
<div class="help-section"><h4>🚀 快速开始</h4><ol><li>确保 Ollama 已启动（默认 <code>http://127.0.0.1:11434</code>，可在设置中修改）</li><li>顶部模型栏选择一个模型，右侧会显示模型能力徽章（🧠 Think / 👁️ Vision / 🔧 Tools / 📚 RAG）</li><li>输入消息，按 <kbd>Enter</kbd> 发送，<kbd>Shift+Enter</kbd> 换行</li></ol></div>
|
||||
<div class="help-section"><h4>🚀 快速开始</h4><ol><li>确保 Ollama 已启动（默认 <code>http://127.0.0.1:11434</code>，可在设置中修改）</li><li>顶部模型栏选择一个模型，右侧会显示模型能力徽章（🧠 Think / 👁️ Vision / 🔧 Tools）</li><li>输入消息，按 <kbd>Enter</kbd> 发送，<kbd>Shift+Enter</kbd> 换行</li></ol></div>
|
||||
<div class="help-section"><h4>💬 聊天功能</h4><ul><li><strong>流式回复</strong> — 实时打字效果，随时点 ■ 停止</li><li><strong>Think 推理</strong> — 设置中开启，让模型展示深度思考过程（需模型支持，如 Qwen3）</li><li><strong>多模态</strong> — 上传图片，模型需支持 Vision 能力</li><li><strong>文件分析</strong> — 支持 50+ 种文本/代码格式，单文件 ≤500KB，内容以代码块发送给模型</li><li><strong>上下文长度</strong> — 设置中调整 <code>num_ctx</code>，越大记忆越长，消耗显存越多</li></ul></div>
|
||||
<div class="help-section"><h4>🔧 Tool Calling（AI 自主操作）</h4><ul><li>设置中开启后，AI 可以在对话中<strong>自主调用本地工具</strong>，像一个本地 Agent</li><li><strong>7 个工具</strong>：<code>read_file</code> / <code>write_file</code> / <code>list_directory</code> / <code>search_files</code> / <code>create_directory</code> / <code>delete_file</code> / <code>run_command</code></li><li>写入/删除/命令执行等高风险操作会弹出<strong>确认对话框</strong>，你可以批准或拒绝</li><li>危险命令（<code>rm -rf</code>、<code>mkfs</code>、反弹 shell 等）和系统路径（<code>/etc</code>、<code>~/.ssh</code> 等）被自动拦截</li><li>工具调用以<strong>可视化卡片</strong>展示状态（pending → running → success/error）</li><li>最多自动循环 <strong>10 轮</strong>工具调用，也可随时中断</li><li>⚠️ 需要模型支持 Tool Calling（推荐 Qwen3、Llama 3.1+、Mistral）</li></ul></div>
|
||||
<div class="help-section"><h4>🧠 Agent 记忆系统</h4><ul><li>设置中开启后，AI 从对话中<strong>自动提取关键信息</strong>（事实/偏好/规则/事件），跨会话持续积累</li><li>对话满 <strong>6 条消息</strong>后自动触发记忆提取</li><li>新对话时自动检索相关记忆注入上下文，让 AI "记住"你</li><li>点击顶部 🧠 按钮打开记忆面板：搜索、筛选、编辑、删除</li><li>记忆存储在本地 IndexedDB，不会上传到任何服务器</li></ul></div>
|
||||
<div class="help-section"><h4>📚 知识库 (RAG)</h4><ul><li>点击顶部 📚 按钮打开知识库管理</li><li>创建集合 → 选择嵌入模型（推荐 <code>nomic-embed-text</code>）</li><li>上传文档，自动分块并生成向量索引</li><li>开启 RAG 检索后，聊天时自动检索相关文档增强回答</li></ul></div>
|
||||
<div class="help-section"><h4>🧠 Agent 记忆系统</h4><ul><li>设置中开启后，AI 从对话中<strong>自动提取关键信息</strong>（事实/偏好/规则），跨会话持续积累</li><li>对话满 <strong>6 条消息</strong>后自动触发记忆提取</li><li>新对话时自动检索相关记忆注入上下文，让 AI "记住"你</li><li>点击顶部 🧠 按钮打开记忆面板：搜索、筛选、编辑、删除</li><li><strong>向量语义搜索</strong>：在设置中选择嵌入模型后，支持语义级别记忆检索（更精准）</li><li>未选择嵌入模型时，使用关键词匹配检索记忆</li><li>记忆存储在本地 IndexedDB，不会上传到任何服务器</li></ul></div>
|
||||
<div class="help-section"><h4>🖥️ 布局说明</h4><ul><li><strong>左侧面板</strong> — 执行日志，实时显示应用运行日志（连接、模型加载、工具调用等）</li><li><strong>中间区域</strong> — 聊天消息，顶部 Header + 模型栏，底部输入框</li><li><strong>右侧面板</strong> — 工作空间（常驻显示），包含：<ul><li><strong>💻 命令行 Tab</strong> — 终端界面，实时流式输出，支持长时间运行（无超时），多终端 Tab</li><li><strong>📁 文件 Tab</strong> — 浏览工作空间目录，点击文件预览内容</li></ul></li><li>工作空间面板宽度可通过拖拽左边缘调整（300–700px）</li><li>工作空间目录可在设置中修改</li></ul></div>
|
||||
<div class="help-section"><h4>🕐 历史记录</h4><ul><li>所有会话自动保存到本地 IndexedDB</li><li>点击顶部 🕐 按钮查看、搜索、恢复历史会话</li><li>支持导出 Markdown / HTML / TXT / <code>.metona</code> 加密备份</li></ul></div>
|
||||
<div class="help-section"><h4>⚡ 快捷键</h4><table class="help-shortcuts"><tr><td><kbd>Enter</kbd></td><td>发送消息</td></tr><tr><td><kbd>Shift + Enter</kbd></td><td>换行</td></tr><tr><td><kbd>Ctrl + N</kbd></td><td>新建会话</td></tr><tr><td><kbd>Ctrl + K</kbd></td><td>知识库管理</td></tr><tr><td><kbd>Ctrl + M</kbd></td><td>记忆面板</td></tr><tr><td><kbd>Esc</kbd></td><td>关闭弹窗</td></tr></table></div>
|
||||
<div class="help-section"><h4>⚡ 快捷键</h4><table class="help-shortcuts"><tr><td><kbd>Enter</kbd></td><td>发送消息</td></tr><tr><td><kbd>Shift + Enter</kbd></td><td>换行</td></tr><tr><td><kbd>Ctrl + N</kbd></td><td>新建会话</td></tr><tr><td><kbd>Ctrl + M</kbd></td><td>记忆管理</td></tr><tr><td><kbd>Esc</kbd></td><td>关闭弹窗</td></tr></table></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- ═══════════════ 知识库管理模态框 ═══════════════ -->
|
||||
<div class="modal-overlay" id="kbModal" style="display:none;">
|
||||
<div class="modal modal-lg">
|
||||
<div class="modal-header">
|
||||
<h3>📚 知识库 (RAG)</h3>
|
||||
<button class="icon-btn" id="btnCloseKB">
|
||||
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
|
||||
<line x1="18" y1="6" x2="6" y2="18"/><line x1="6" y1="6" x2="18" y2="18"/>
|
||||
</svg>
|
||||
</button>
|
||||
</div>
|
||||
<div class="modal-body">
|
||||
<div class="kb-layout">
|
||||
<div class="kb-sidebar">
|
||||
<div class="kb-new-collection">
|
||||
<input class="setting-input" id="inputKBName" type="text" placeholder="新建知识库名称...">
|
||||
<button class="btn btn-sm btn-primary" id="btnNewCollection">创建</button>
|
||||
</div>
|
||||
<div class="kb-collection-list" id="kbCollectionList">
|
||||
<p class="text-muted" style="padding:16px;text-align:center;">暂无知识库</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="kb-main">
|
||||
<div class="kb-toolbar">
|
||||
<button class="btn btn-sm btn-outline" id="btnUploadDoc">📄 上传文档</button>
|
||||
<span id="kbProgress" style="display:none;" class="kb-progress">
|
||||
<span class="spinner"></span>
|
||||
<span id="kbProgressText">处理中...</span>
|
||||
</span>
|
||||
<select class="kb-embed-select" id="selectEmbedModel">
|
||||
<option value="">嵌入模型...</option>
|
||||
</select>
|
||||
<div class="kb-rag-toggle">
|
||||
<label class="setting-label" style="margin:0;font-size:12px;white-space:nowrap;">RAG</label>
|
||||
<label class="toggle-label" style="margin:0;">
|
||||
<input type="checkbox" id="toggleRAG">
|
||||
<span class="toggle-slider"></span>
|
||||
</label>
|
||||
</div>
|
||||
</div>
|
||||
<div class="kb-doc-list" id="kbDocList">
|
||||
<p class="text-muted" style="padding:16px;text-align:center;">← 选择一个知识库集合</p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<!-- ═══════════════ 图片预览 Lightbox ═══════════════ -->
|
||||
<div class="lightbox-overlay" id="lightbox" style="display:none;">
|
||||
<button class="lightbox-close" id="lightboxClose">✕</button>
|
||||
|
||||
+4
-14
@@ -19,12 +19,10 @@ import { initChatArea, renderMessages, clearMessages, enableAutoScroll, updateTo
|
||||
import { initInputArea } from './components/input-area.js';
|
||||
import { initSettingsModal, closeSettingsModal } from './components/settings-modal.js';
|
||||
import { initHistoryModal, closeHistoryModal } from './components/history-modal.js';
|
||||
import { initKBModal } from './components/kb-modal.js';
|
||||
import { initVectorStore } from './services/rag.js';
|
||||
import { initMemoryModal } from './components/memory-modal.js';
|
||||
import { initMemoryManager } from './services/memory-manager.js';
|
||||
import { setToolEnabled } from './services/tool-registry.js';
|
||||
import { initToolConfirmModal } from './components/tool-confirm-modal.js';
|
||||
import { initMemoryPanel } from './components/memory-panel.js';
|
||||
import { initWorkspacePanel } from './components/workspace-panel.js';
|
||||
import { initLogPanel, addLog } from './services/log-service.js';
|
||||
import { logInfo, logSuccess, logError, logDebug, logInit, logWarn } from './services/log-service.js';
|
||||
@@ -204,10 +202,9 @@ async function init(): Promise<void> {
|
||||
initInputArea();
|
||||
initSettingsModal();
|
||||
initHistoryModal();
|
||||
initKBModal();
|
||||
initHelpModal();
|
||||
initToolConfirmModal();
|
||||
initMemoryPanel();
|
||||
initMemoryModal();
|
||||
initWorkspacePanel();
|
||||
setupDesktopIntegration();
|
||||
|
||||
@@ -215,7 +212,6 @@ async function init(): Promise<void> {
|
||||
|
||||
await checkConnection();
|
||||
await loadModels();
|
||||
await initVectorStore();
|
||||
await initMemoryManager();
|
||||
logInit('所有组件已就绪');
|
||||
|
||||
@@ -291,16 +287,10 @@ function bindGlobalEvents(): void {
|
||||
closeHistoryModal();
|
||||
closeLightbox();
|
||||
(document.querySelector('#helpModal') as HTMLElement).style.display = 'none';
|
||||
(document.querySelector('#kbModal') as HTMLElement).style.display = 'none';
|
||||
(document.querySelector('#memoryModal') as HTMLElement).style.display = 'none';
|
||||
}
|
||||
|
||||
// Ctrl+K — 知识库管理
|
||||
if (e.ctrlKey && e.key === 'k') {
|
||||
e.preventDefault();
|
||||
document.querySelector('#btnKB')?.dispatchEvent(new Event('click'));
|
||||
}
|
||||
|
||||
// Ctrl+M — 记忆面板
|
||||
// Ctrl+M — 记忆管理
|
||||
if (e.ctrlKey && e.key === 'm') {
|
||||
e.preventDefault();
|
||||
document.querySelector('#btnMemory')?.dispatchEvent(new Event('click'));
|
||||
|
||||
@@ -1,35 +1,41 @@
|
||||
/**
|
||||
* MemoryManager - Agent 记忆系统
|
||||
* MemoryManager - Agent 记忆系统（长效向量记忆）
|
||||
* 自动提取 → 存储 → 检索 → 注入上下文
|
||||
*
|
||||
* 记忆类型：
|
||||
* - fact: 用户告诉我的事实（项目信息、个人背景等）
|
||||
* - preference: 用户偏好（语言、风格、格式习惯）
|
||||
* - rule: 应遵守的规则（命名规范、输出格式要求）
|
||||
* - episode: 重要事件（完成了什么、得到了什么结论）
|
||||
*/
|
||||
|
||||
import { state, KEYS } from '../state/state.js';
|
||||
import { generateId } from '../utils/utils.js';
|
||||
import {
|
||||
initMemoryVectorStore, getOrCreateMemoryCollection,
|
||||
embedMemoryEntry, addMemoryVector, updateMemoryVector,
|
||||
deleteMemoryVector, searchMemoriesByVector, reindexAllMemories,
|
||||
getMemoryCollectionId
|
||||
} from './vector-memory.js';
|
||||
import { logMemory, logDebug, logWarn } from './log-service.js';
|
||||
import type { MemoryEntry, MemorySearchResult, MemoryExtractionResult, ChatDB, OllamaAPI } from '../types.js';
|
||||
|
||||
const TYPE_ICONS: Record<string, string> = {
|
||||
fact: '📌',
|
||||
preference: '⚙️',
|
||||
rule: '📏',
|
||||
episode: '📝'
|
||||
rule: '📏'
|
||||
};
|
||||
|
||||
const TYPE_NAMES: Record<string, string> = {
|
||||
fact: '事实',
|
||||
preference: '偏好',
|
||||
rule: '规则',
|
||||
episode: '事件'
|
||||
rule: '规则'
|
||||
};
|
||||
|
||||
let memoryCache: MemoryEntry[] = [];
|
||||
let memoryEnabled = true;
|
||||
let embeddingModel = '';
|
||||
|
||||
// ── 初始化 ──
|
||||
|
||||
export async function initMemoryManager(): Promise<void> {
|
||||
const db = state.get<ChatDB | null>(KEYS.DB);
|
||||
@@ -38,11 +44,69 @@ export async function initMemoryManager(): Promise<void> {
|
||||
memoryEnabled = await db.getSetting('memoryEnabled', true);
|
||||
state.set('memoryEnabled', memoryEnabled);
|
||||
|
||||
// 加载嵌入模型设置
|
||||
embeddingModel = await db.getSetting('embeddingModel', '');
|
||||
state.set('embeddingModel', embeddingModel);
|
||||
|
||||
memoryCache = await db.getAllMemories();
|
||||
state.set('memoryEntries', memoryCache);
|
||||
|
||||
logMemory(`初始化完成, 加载 ${memoryCache.length} 条`);
|
||||
// 如果有嵌入模型，初始化向量存储
|
||||
if (embeddingModel) {
|
||||
try {
|
||||
await initMemoryVectorStore();
|
||||
logMemory('向量存储已初始化');
|
||||
} catch (err) {
|
||||
logWarn('向量存储初始化失败', (err as Error).message);
|
||||
}
|
||||
}
|
||||
|
||||
logMemory(`初始化完成, 加载 ${memoryCache.length} 条${embeddingModel ? ', 向量记忆已启用' : ', 仅关键词模式'}`);
|
||||
}
|
||||
|
||||
// ── 嵌入模型管理 ──
|
||||
|
||||
export function getEmbeddingModel(): string {
|
||||
return embeddingModel;
|
||||
}
|
||||
|
||||
export async function setEmbeddingModel(model: string): Promise<void> {
|
||||
const oldModel = embeddingModel;
|
||||
embeddingModel = model;
|
||||
state.set('embeddingModel', model);
|
||||
|
||||
const db = state.get<ChatDB | null>(KEYS.DB);
|
||||
if (db) await db.saveSetting('embeddingModel', model);
|
||||
|
||||
if (model && model !== oldModel) {
|
||||
// 嵌入模型变化，重新索引所有记忆
|
||||
logMemory(`嵌入模型变更: ${oldModel || '(无)'} → ${model}`);
|
||||
await reindexMemories();
|
||||
}
|
||||
}
|
||||
|
||||
export function isVectorMemoryEnabled(): boolean {
|
||||
return !!embeddingModel;
|
||||
}
|
||||
|
||||
// ── 重新索引所有记忆 ──
|
||||
|
||||
export async function reindexMemories(): Promise<void> {
|
||||
if (!embeddingModel || memoryCache.length === 0) return;
|
||||
|
||||
try {
|
||||
await initMemoryVectorStore();
|
||||
const { id: colId } = await getOrCreateMemoryCollection(embeddingModel);
|
||||
await reindexAllMemories(memoryCache, embeddingModel, colId, (done, total) => {
|
||||
logMemory(`重新索引进度: ${done}/${total}`);
|
||||
});
|
||||
logMemory('向量索引重建完成');
|
||||
} catch (err) {
|
||||
logWarn('向量索引重建失败', (err as Error).message);
|
||||
}
|
||||
}
|
||||
|
||||
// ── 基础管理 ──
|
||||
|
||||
export function isMemoryEnabled(): boolean {
|
||||
return memoryEnabled;
|
||||
@@ -67,11 +131,52 @@ export function getTypeName(type: string): string {
|
||||
return TYPE_NAMES[type] || type;
|
||||
}
|
||||
|
||||
// ── 记忆检索（关键词 + 标签 + 重要性加权）──
|
||||
// ── 记忆检索（向量优先，关键词补充）──
|
||||
|
||||
export function searchMemories(query: string, limit = 8): MemorySearchResult[] {
|
||||
if (!memoryEnabled || memoryCache.length === 0) return [];
|
||||
|
||||
// 始终执行关键词搜索（即时结果）
|
||||
const keywordResults = searchMemoriesByKeyword(query, limit);
|
||||
|
||||
// 如果启用了向量记忆，异步触发向量搜索（结果在下次调用时更新）
|
||||
if (embeddingModel) {
|
||||
triggerVectorSearch(query, limit);
|
||||
}
|
||||
|
||||
return keywordResults;
|
||||
}
|
||||
|
||||
// 向量搜索异步缓存
|
||||
const vectorSearchCache = new Map<string, MemorySearchResult[]>();
|
||||
let vectorSearchTimer: ReturnType<typeof setTimeout> | null = null;
|
||||
|
||||
function triggerVectorSearch(query: string, limit: number): void {
|
||||
if (vectorSearchTimer) clearTimeout(vectorSearchTimer);
|
||||
vectorSearchTimer = setTimeout(async () => {
|
||||
try {
|
||||
const colId = getMemoryCollectionId();
|
||||
if (!colId) return;
|
||||
const results = await searchMemoriesByVector(query, colId, limit);
|
||||
const memoryResults: MemorySearchResult[] = results.map(r => {
|
||||
const entry = memoryCache.find(e => e.id === r.docId);
|
||||
if (!entry) return null;
|
||||
return { ...entry, score: r.score };
|
||||
}).filter(Boolean) as MemorySearchResult[];
|
||||
vectorSearchCache.set(query, memoryResults);
|
||||
} catch (err) {
|
||||
logWarn('向量搜索失败', (err as Error).message);
|
||||
}
|
||||
}, 100);
|
||||
}
|
||||
|
||||
// 导出：获取向量搜索结果（供 UI 使用）
|
||||
export function getVectorSearchResults(query: string): MemorySearchResult[] {
|
||||
return vectorSearchCache.get(query) || [];
|
||||
}
|
||||
|
||||
// 关键词搜索
|
||||
function searchMemoriesByKeyword(query: string, limit: number): MemorySearchResult[] {
|
||||
const queryLower = query.toLowerCase();
|
||||
const queryWords = queryLower.split(/[\s,，。！？、；：""''（）\[\]{}<>`~@#$%^&*+=|\\/.]+/).filter(w => w.length > 1);
|
||||
|
||||
@@ -155,6 +260,20 @@ export async function addMemory(data: {
|
||||
if (db) await db.saveMemory(entry);
|
||||
logMemory(`新增: ${data.type}`, entry.content.slice(0, 60));
|
||||
|
||||
// 向量存储
|
||||
if (embeddingModel) {
|
||||
try {
|
||||
await initMemoryVectorStore();
|
||||
const colId = getMemoryCollectionId() || (await getOrCreateMemoryCollection(embeddingModel)).id;
|
||||
const embedding = await embedMemoryEntry(entry, embeddingModel);
|
||||
entry.embedding = embedding;
|
||||
await addMemoryVector(entry, embedding, colId);
|
||||
if (db) await db.saveMemory(entry); // 保存包含 embedding 的版本
|
||||
} catch (err) {
|
||||
logWarn('向量存储失败', (err as Error).message);
|
||||
}
|
||||
}
|
||||
|
||||
return entry;
|
||||
}
|
||||
|
||||
@@ -169,6 +288,21 @@ export async function updateMemory(id: string, updates: Partial<MemoryEntry>): P
|
||||
|
||||
const db = state.get<ChatDB | null>(KEYS.DB);
|
||||
if (db) await db.saveMemory(memoryCache[idx]);
|
||||
|
||||
// 更新向量
|
||||
if (embeddingModel && (updates.content || updates.type || updates.tags)) {
|
||||
try {
|
||||
const colId = getMemoryCollectionId();
|
||||
if (colId) {
|
||||
const embedding = await embedMemoryEntry(memoryCache[idx], embeddingModel);
|
||||
memoryCache[idx].embedding = embedding;
|
||||
await updateMemoryVector(memoryCache[idx], embedding, colId);
|
||||
if (db) await db.saveMemory(memoryCache[idx]);
|
||||
}
|
||||
} catch (err) {
|
||||
logWarn('向量更新失败', (err as Error).message);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ── 记忆删除 ──
|
||||
@@ -179,6 +313,17 @@ export async function deleteMemory(id: string): Promise<void> {
|
||||
|
||||
const db = state.get<ChatDB | null>(KEYS.DB);
|
||||
if (db) await db.deleteMemory(id);
|
||||
|
||||
// 删除向量
|
||||
if (embeddingModel) {
|
||||
try {
|
||||
const colId = getMemoryCollectionId();
|
||||
if (colId) await deleteMemoryVector(id, colId);
|
||||
} catch (err) {
|
||||
logWarn('向量删除失败', (err as Error).message);
|
||||
}
|
||||
}
|
||||
|
||||
logMemory(`删除`, id);
|
||||
}
|
||||
|
||||
@@ -190,6 +335,25 @@ export async function clearAllMemories(): Promise<void> {
|
||||
|
||||
const db = state.get<ChatDB | null>(KEYS.DB);
|
||||
if (db) await db.clearAllMemories();
|
||||
|
||||
// 清空向量集合
|
||||
if (embeddingModel) {
|
||||
try {
|
||||
await initMemoryVectorStore();
|
||||
const { id: colId } = await getOrCreateMemoryCollection(embeddingModel);
|
||||
const { getMemoryVectorStore } = await import('./vector-memory.js');
|
||||
const vs = getMemoryVectorStore();
|
||||
if (vs) {
|
||||
await vs.deleteCollection(colId);
|
||||
// 重新创建空集合
|
||||
const { setMemoryCollectionId } = await import('./vector-memory.js');
|
||||
setMemoryCollectionId(null);
|
||||
await getOrCreateMemoryCollection(embeddingModel);
|
||||
}
|
||||
} catch (err) {
|
||||
logWarn('向量集合清空失败', (err as Error).message);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ── 标记记忆被使用 ──
|
||||
@@ -240,13 +404,12 @@ export async function extractMemoriesFromConversation(
|
||||
sessionTitle?: string
|
||||
): Promise<number> {
|
||||
if (!memoryEnabled) return 0;
|
||||
if (messages.length < 3) return 0; // 至少一轮完整对话
|
||||
if (messages.length < 3) return 0;
|
||||
|
||||
const api = state.get<OllamaAPI>(KEYS.API);
|
||||
const model = state.get<string>('_defaultModel', '');
|
||||
if (!api || !model) return 0;
|
||||
|
||||
// 取最近 20 条消息作为提取素材
|
||||
const recentMessages = messages.slice(-20);
|
||||
const conversationText = recentMessages
|
||||
.filter(m => m.role === 'user' || m.role === 'assistant')
|
||||
@@ -277,7 +440,6 @@ ${conversationText.slice(0, 4000)}
|
||||
- fact: 关于用户的事实（项目、身份、背景、习惯）
|
||||
- preference: 用户偏好（语言风格、输出格式、技术栈偏好）
|
||||
- rule: 用户要求遵守的规则（编码规范、输出要求）
|
||||
- episode: 重要事件（完成的任务、达成的结论）
|
||||
|
||||
提取规则：
|
||||
1. 只提取真正有价值、值得跨会话记住的信息
|
||||
@@ -308,8 +470,9 @@ ${conversationText.slice(0, 4000)}
|
||||
const currentSession = state.get(KEYS.CURRENT_SESSION);
|
||||
for (const entry of parsed.entries) {
|
||||
if (!entry.content || entry.content.length < 5) continue;
|
||||
const validType = (['fact', 'preference', 'rule'] as const).includes(entry.type as any) ? entry.type : 'fact';
|
||||
await addMemory({
|
||||
type: entry.type || 'fact',
|
||||
type: validType as any,
|
||||
content: entry.content,
|
||||
importance: Math.min(10, Math.max(1, entry.importance || 5)),
|
||||
tags: entry.tags || [],
|
||||
|
||||
@@ -1,152 +0,0 @@
|
||||
/**
|
||||
* 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 { logRAG, logDebug, logError } from './log-service.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();
|
||||
logRAG('向量存储已初始化');
|
||||
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;
|
||||
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)));
|
||||
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');
|
||||
logRAG(`分块完成: ${filename}`, `${textChunks.length} 个分块`);
|
||||
|
||||
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);
|
||||
logRAG(`删除文档`, `${docId} (${docVectors.length} 个分块)`);
|
||||
|
||||
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');
|
||||
|
||||
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: [] };
|
||||
logRAG(`检索完成`, `${results.length} 个相关片段`);
|
||||
|
||||
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;
|
||||
}
|
||||
@@ -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: ${entries.length} 条`);
|
||||
}
|
||||
@@ -54,7 +54,7 @@ export class VectorStore {
|
||||
|
||||
async createCollection(name: string, embeddingModel = ''): Promise<VectorCollection> {
|
||||
const col: VectorCollection = {
|
||||
id: `kb_${Date.now()}_${Math.random().toString(36).slice(2, 7)}`,
|
||||
id: `mem_${Date.now()}_${Math.random().toString(36).slice(2, 7)}`,
|
||||
name, embeddingModel,
|
||||
docCount: 0, chunkCount: 0,
|
||||
createdAt: Date.now(), updatedAt: Date.now()
|
||||
|
||||
+316
-538
@@ -500,12 +500,6 @@ html, body {
|
||||
border-color: rgba(108, 203, 95, 0.15);
|
||||
}
|
||||
|
||||
.rag-badge {
|
||||
color: #B388FF;
|
||||
background: rgba(179, 136, 255, 0.06);
|
||||
border-color: rgba(179, 136, 255, 0.15);
|
||||
}
|
||||
|
||||
.tools-badge {
|
||||
color: #FFB74D;
|
||||
background: rgba(255, 183, 77, 0.06);
|
||||
@@ -1013,49 +1007,6 @@ html, body {
|
||||
color: var(--accent);
|
||||
}
|
||||
|
||||
/* RAG 知识库来源 */
|
||||
.rag-sources {
|
||||
margin-top: 8px;
|
||||
padding: 8px 12px;
|
||||
background: var(--accent-subtle);
|
||||
border: 1px solid rgba(96, 205, 255, 0.1);
|
||||
border-radius: var(--radius-control);
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
.rag-sources-header {
|
||||
color: var(--accent);
|
||||
font-weight: 600;
|
||||
margin-bottom: 4px;
|
||||
font-size: 11px;
|
||||
}
|
||||
|
||||
.rag-source-item {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
padding: 3px 0;
|
||||
color: var(--text-tertiary);
|
||||
}
|
||||
|
||||
.rag-source-item + .rag-source-item {
|
||||
border-top: 1px solid var(--border-subtle);
|
||||
}
|
||||
|
||||
.rag-source-name {
|
||||
color: var(--text-secondary);
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.rag-source-score {
|
||||
color: var(--success);
|
||||
font-weight: 500;
|
||||
margin-left: 8px;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
/* ═══ 输入区域 ═══ */
|
||||
.input-area {
|
||||
flex-shrink: 0;
|
||||
@@ -1855,251 +1806,6 @@ html, body {
|
||||
background: var(--bg-layer-alt);
|
||||
}
|
||||
|
||||
/* ═══ 知识库管理 ═══ */
|
||||
#kbModal .modal {
|
||||
max-width: 95vw;
|
||||
width: 1100px;
|
||||
max-height: 88vh;
|
||||
}
|
||||
|
||||
#kbModal .modal-body {
|
||||
padding: 16px 20px;
|
||||
overflow: hidden;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
}
|
||||
|
||||
.kb-layout {
|
||||
display: flex;
|
||||
gap: 16px;
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.kb-sidebar {
|
||||
width: 240px;
|
||||
flex-shrink: 0;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 8px;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
padding-right: 16px;
|
||||
border-right: 1px solid var(--border-subtle);
|
||||
}
|
||||
|
||||
.kb-new-collection {
|
||||
display: flex;
|
||||
gap: 6px;
|
||||
}
|
||||
|
||||
.kb-new-collection .setting-input {
|
||||
flex: 1;
|
||||
margin-bottom: 0;
|
||||
font-size: 13px;
|
||||
padding: 6px 10px;
|
||||
}
|
||||
|
||||
.kb-collection-list {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 2px;
|
||||
min-height: 0;
|
||||
}
|
||||
|
||||
.kb-collection-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
padding: 10px 12px;
|
||||
border-radius: var(--radius-control);
|
||||
cursor: pointer;
|
||||
transition: var(--transition);
|
||||
}
|
||||
|
||||
.kb-collection-item:hover {
|
||||
background: var(--bg-layer);
|
||||
}
|
||||
|
||||
.kb-collection-item.active {
|
||||
background: var(--accent-subtle);
|
||||
border: 1px solid rgba(96, 205, 255, 0.12);
|
||||
}
|
||||
|
||||
.kb-col-info {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 2px;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.kb-col-name {
|
||||
font-size: 13px;
|
||||
font-weight: 500;
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
}
|
||||
|
||||
.kb-col-stats {
|
||||
font-size: 11px;
|
||||
color: var(--text-tertiary);
|
||||
}
|
||||
|
||||
.kb-col-delete {
|
||||
opacity: 0;
|
||||
transition: opacity var(--transition);
|
||||
}
|
||||
|
||||
.kb-collection-item:hover .kb-col-delete {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
.kb-main {
|
||||
flex: 1;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 10px;
|
||||
min-width: 0;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.kb-toolbar {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
flex-shrink: 0;
|
||||
flex-wrap: wrap;
|
||||
padding: 8px 12px;
|
||||
background: var(--bg-layer);
|
||||
border-radius: var(--radius-control);
|
||||
border: 1px solid var(--border-subtle);
|
||||
}
|
||||
|
||||
.kb-embed-select {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
max-width: 200px;
|
||||
padding: 5px 8px;
|
||||
font-size: 12px;
|
||||
background: var(--bg-layer-alt);
|
||||
color: var(--text-primary);
|
||||
border: 1px solid var(--border-default);
|
||||
border-radius: var(--radius-control);
|
||||
outline: none;
|
||||
cursor: pointer;
|
||||
font-family: var(--font);
|
||||
}
|
||||
|
||||
.kb-embed-select:focus {
|
||||
border-color: var(--border-focus);
|
||||
}
|
||||
|
||||
.kb-rag-toggle {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
margin-left: auto;
|
||||
white-space: nowrap;
|
||||
padding-left: 8px;
|
||||
border-left: 1px solid var(--border-subtle);
|
||||
}
|
||||
|
||||
.kb-progress {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
font-size: 12px;
|
||||
color: var(--text-tertiary);
|
||||
padding: 3px 8px;
|
||||
background: var(--accent-subtle);
|
||||
border-radius: var(--radius-control);
|
||||
}
|
||||
|
||||
.kb-doc-list {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 2px;
|
||||
min-height: 0;
|
||||
}
|
||||
|
||||
.kb-doc-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 10px;
|
||||
padding: 10px 12px;
|
||||
border-radius: var(--radius-control);
|
||||
transition: var(--transition);
|
||||
}
|
||||
|
||||
.kb-doc-item:hover {
|
||||
background: var(--bg-layer);
|
||||
}
|
||||
|
||||
.kb-doc-icon {
|
||||
font-size: 16px;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.kb-doc-info {
|
||||
flex: 1;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 1px;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.kb-doc-name {
|
||||
font-size: 13px;
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
}
|
||||
|
||||
.kb-doc-meta {
|
||||
font-size: 11px;
|
||||
color: var(--text-tertiary);
|
||||
}
|
||||
|
||||
.kb-doc-delete {
|
||||
opacity: 0;
|
||||
transition: opacity var(--transition);
|
||||
}
|
||||
|
||||
.kb-doc-item:hover .kb-doc-delete {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
/* ── 移动端响应式 ── */
|
||||
@media (max-width: 640px) {
|
||||
.kb-layout {
|
||||
flex-direction: column;
|
||||
}
|
||||
|
||||
.kb-sidebar {
|
||||
width: 100%;
|
||||
max-height: 160px;
|
||||
border-right: none;
|
||||
padding-right: 0;
|
||||
padding-bottom: 10px;
|
||||
border-bottom: 1px solid var(--border-subtle);
|
||||
}
|
||||
|
||||
.kb-main {
|
||||
min-height: 180px;
|
||||
}
|
||||
|
||||
.modal-lg {
|
||||
max-height: 92vh;
|
||||
}
|
||||
}
|
||||
|
||||
/* ═══ Toast 通知 ═══ */
|
||||
.toast-container {
|
||||
position: fixed;
|
||||
@@ -2436,250 +2142,6 @@ html, body {
|
||||
color: rgba(255, 255, 255, 0.75);
|
||||
}
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════════
|
||||
Agent 记忆系统
|
||||
═══════════════════════════════════════════════════════════════ */
|
||||
|
||||
/* ── 记忆面板 ── */
|
||||
.memory-panel {
|
||||
position: absolute;
|
||||
top: 100px;
|
||||
right: 16px;
|
||||
width: 380px;
|
||||
max-height: calc(100vh - 140px);
|
||||
background: var(--bg-primary, #2d2d2d);
|
||||
border: 1px solid rgba(255, 255, 255, 0.1);
|
||||
border-radius: 12px;
|
||||
box-shadow: 0 8px 32px rgba(0, 0, 0, 0.4);
|
||||
z-index: 100;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.memory-panel-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
padding: 12px 16px;
|
||||
border-bottom: 1px solid rgba(255, 255, 255, 0.06);
|
||||
}
|
||||
|
||||
.memory-panel-header h3 {
|
||||
margin: 0;
|
||||
font-size: 14px;
|
||||
font-weight: 600;
|
||||
}
|
||||
|
||||
.memory-panel-actions {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.memory-toggle {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.memory-toggle input {
|
||||
display: none;
|
||||
}
|
||||
|
||||
.toggle-slider-sm {
|
||||
width: 28px;
|
||||
height: 16px;
|
||||
background: rgba(255, 255, 255, 0.15);
|
||||
border-radius: 8px;
|
||||
position: relative;
|
||||
transition: background 0.2s;
|
||||
}
|
||||
|
||||
.toggle-slider-sm::after {
|
||||
content: '';
|
||||
position: absolute;
|
||||
top: 2px;
|
||||
left: 2px;
|
||||
width: 12px;
|
||||
height: 12px;
|
||||
background: #fff;
|
||||
border-radius: 50%;
|
||||
transition: transform 0.2s;
|
||||
}
|
||||
|
||||
.memory-toggle input:checked + .toggle-slider-sm {
|
||||
background: var(--accent-cyan, #00f5d4);
|
||||
}
|
||||
|
||||
.memory-toggle input:checked + .toggle-slider-sm::after {
|
||||
transform: translateX(12px);
|
||||
}
|
||||
|
||||
.memory-panel-search {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
padding: 8px 16px;
|
||||
}
|
||||
|
||||
.memory-search-input {
|
||||
flex: 1;
|
||||
background: rgba(0, 0, 0, 0.2);
|
||||
border: 1px solid rgba(255, 255, 255, 0.08);
|
||||
border-radius: 6px;
|
||||
padding: 6px 10px;
|
||||
color: var(--text-primary, #fff);
|
||||
font-size: 12px;
|
||||
outline: none;
|
||||
}
|
||||
|
||||
.memory-search-input:focus {
|
||||
border-color: var(--accent-cyan, #00f5d4);
|
||||
}
|
||||
|
||||
.memory-type-filter {
|
||||
background: rgba(0, 0, 0, 0.2);
|
||||
border: 1px solid rgba(255, 255, 255, 0.08);
|
||||
border-radius: 6px;
|
||||
padding: 6px 8px;
|
||||
color: var(--text-primary, #fff);
|
||||
font-size: 12px;
|
||||
outline: none;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.memory-panel-actions-row {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
padding: 0 16px 8px;
|
||||
}
|
||||
|
||||
.btn-danger-outline {
|
||||
background: none;
|
||||
border: 1px solid rgba(255, 82, 82, 0.3);
|
||||
color: #ff6b6b;
|
||||
border-radius: 6px;
|
||||
padding: 4px 10px;
|
||||
cursor: pointer;
|
||||
font-size: 12px;
|
||||
transition: all 0.2s;
|
||||
}
|
||||
|
||||
.btn-danger-outline:hover {
|
||||
background: rgba(255, 82, 82, 0.1);
|
||||
border-color: rgba(255, 82, 82, 0.5);
|
||||
}
|
||||
|
||||
.memory-list {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
padding: 0 16px 16px;
|
||||
}
|
||||
|
||||
.memory-empty {
|
||||
text-align: center;
|
||||
padding: 24px 16px;
|
||||
color: var(--text-secondary, rgba(255, 255, 255, 0.4));
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
.memory-item {
|
||||
border: 1px solid rgba(255, 255, 255, 0.06);
|
||||
border-radius: 8px;
|
||||
padding: 8px 10px;
|
||||
margin-bottom: 6px;
|
||||
background: rgba(255, 255, 255, 0.02);
|
||||
transition: border-color 0.2s;
|
||||
}
|
||||
|
||||
.memory-item:hover {
|
||||
border-color: rgba(255, 255, 255, 0.12);
|
||||
}
|
||||
|
||||
.memory-item-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
margin-bottom: 4px;
|
||||
}
|
||||
|
||||
.memory-item-icon {
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
.memory-item-type {
|
||||
font-size: 11px;
|
||||
color: var(--text-secondary, rgba(255, 255, 255, 0.5));
|
||||
font-weight: 500;
|
||||
}
|
||||
|
||||
.memory-item-importance {
|
||||
font-size: 8px;
|
||||
color: var(--accent-cyan, #00f5d4);
|
||||
letter-spacing: 1px;
|
||||
margin-left: auto;
|
||||
}
|
||||
|
||||
.memory-item-delete {
|
||||
background: none;
|
||||
border: none;
|
||||
color: var(--text-secondary, rgba(255, 255, 255, 0.3));
|
||||
cursor: pointer;
|
||||
font-size: 12px;
|
||||
padding: 0 4px;
|
||||
line-height: 1;
|
||||
transition: color 0.2s;
|
||||
}
|
||||
|
||||
.memory-item-delete:hover {
|
||||
color: #ff6b6b;
|
||||
}
|
||||
|
||||
.memory-item-content {
|
||||
font-size: 12px;
|
||||
color: var(--text-primary, rgba(255, 255, 255, 0.85));
|
||||
line-height: 1.5;
|
||||
cursor: default;
|
||||
}
|
||||
|
||||
.memory-item-meta {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
margin-top: 4px;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
.memory-item-tags {
|
||||
display: flex;
|
||||
gap: 4px;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
.memory-tag {
|
||||
background: rgba(0, 245, 212, 0.08);
|
||||
color: var(--accent-cyan, #00f5d4);
|
||||
padding: 0 6px;
|
||||
border-radius: 4px;
|
||||
font-size: 10px;
|
||||
}
|
||||
|
||||
.memory-item-time {
|
||||
font-size: 10px;
|
||||
color: var(--text-secondary, rgba(255, 255, 255, 0.3));
|
||||
margin-left: auto;
|
||||
}
|
||||
|
||||
/* 记忆注入状态 */
|
||||
.memory-status {
|
||||
text-align: center;
|
||||
padding: 4px;
|
||||
font-size: 11px;
|
||||
color: var(--accent-cyan, #00f5d4);
|
||||
opacity: 0.7;
|
||||
}
|
||||
|
||||
/* ═══════════════ 工作空间面板 ═══════════════ */
|
||||
|
||||
.workspace-panel {
|
||||
width: 480px;
|
||||
@@ -3090,4 +2552,320 @@ html, body {
|
||||
/* ── 主内容区自适应 ── */
|
||||
|
||||
/* main-wrap.with-workspace 不再需要 margin-right，
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════════
|
||||
记忆管理模态框
|
||||
═══════════════════════════════════════════════════════════════ */
|
||||
|
||||
/* ═══ 记忆管理模态框 ═══ */
|
||||
#memoryModal .modal {
|
||||
max-width: 95vw;
|
||||
width: 1000px;
|
||||
max-height: 88vh;
|
||||
}
|
||||
|
||||
#memoryModal .modal-body {
|
||||
padding: 16px 20px;
|
||||
overflow: hidden;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
}
|
||||
|
||||
.memory-layout {
|
||||
display: flex;
|
||||
gap: 16px;
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.memory-sidebar {
|
||||
width: 200px;
|
||||
flex-shrink: 0;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 8px;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
padding-right: 16px;
|
||||
border-right: 1px solid var(--border-subtle);
|
||||
}
|
||||
|
||||
.memory-stats {
|
||||
padding: 12px;
|
||||
background: var(--bg-layer);
|
||||
border-radius: var(--radius-control);
|
||||
border: 1px solid var(--border-subtle);
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
.memory-stats-item {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
padding: 3px 0;
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
.memory-stats-count {
|
||||
color: var(--accent);
|
||||
font-weight: 600;
|
||||
}
|
||||
|
||||
.memory-categories {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 2px;
|
||||
}
|
||||
|
||||
.memory-category-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
padding: 8px 12px;
|
||||
border-radius: var(--radius-control);
|
||||
cursor: pointer;
|
||||
transition: var(--transition);
|
||||
font-size: 13px;
|
||||
color: var(--text-secondary);
|
||||
border: 1px solid transparent;
|
||||
}
|
||||
|
||||
.memory-category-item:hover {
|
||||
background: var(--bg-layer);
|
||||
color: var(--text-primary);
|
||||
}
|
||||
|
||||
.memory-category-item.active {
|
||||
background: var(--accent-subtle);
|
||||
color: var(--accent);
|
||||
border-color: rgba(96, 205, 255, 0.12);
|
||||
}
|
||||
|
||||
.memory-category-icon {
|
||||
font-size: 14px;
|
||||
}
|
||||
|
||||
.memory-category-count {
|
||||
margin-left: auto;
|
||||
font-size: 11px;
|
||||
color: var(--text-tertiary);
|
||||
}
|
||||
|
||||
.memory-main {
|
||||
flex: 1;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 10px;
|
||||
min-width: 0;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.memory-toolbar {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
flex-shrink: 0;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
.memory-search-wrap {
|
||||
flex: 1;
|
||||
position: relative;
|
||||
}
|
||||
|
||||
.memory-search-input-lg {
|
||||
width: 100%;
|
||||
padding: 8px 12px;
|
||||
background: var(--bg-layer);
|
||||
border: 1px solid var(--border-default);
|
||||
border-radius: var(--radius-control);
|
||||
color: var(--text-primary);
|
||||
font-size: 13px;
|
||||
font-family: var(--font);
|
||||
outline: none;
|
||||
transition: var(--transition);
|
||||
}
|
||||
|
||||
.memory-search-input-lg:focus {
|
||||
border-color: var(--border-focus);
|
||||
box-shadow: 0 0 0 1px var(--border-focus);
|
||||
}
|
||||
|
||||
.memory-vector-badge {
|
||||
font-size: 11px;
|
||||
padding: 3px 8px;
|
||||
border-radius: var(--radius-control);
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.memory-vector-badge.enabled {
|
||||
color: var(--success);
|
||||
background: var(--success-bg);
|
||||
border: 1px solid rgba(108, 203, 95, 0.15);
|
||||
}
|
||||
|
||||
.memory-vector-badge.disabled {
|
||||
color: var(--text-tertiary);
|
||||
background: var(--bg-layer);
|
||||
border: 1px solid var(--border-subtle);
|
||||
}
|
||||
|
||||
.memory-list-lg {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 4px;
|
||||
min-height: 0;
|
||||
}
|
||||
|
||||
.memory-item-lg {
|
||||
border: 1px solid var(--border-subtle);
|
||||
border-radius: var(--radius-control);
|
||||
padding: 10px 14px;
|
||||
background: var(--bg-layer);
|
||||
transition: var(--transition);
|
||||
}
|
||||
|
||||
.memory-item-lg:hover {
|
||||
border-color: var(--border-strong);
|
||||
background: var(--bg-layer-alt);
|
||||
}
|
||||
|
||||
.memory-item-lg-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
margin-bottom: 6px;
|
||||
}
|
||||
|
||||
.memory-item-lg-type {
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
.memory-item-lg-importance {
|
||||
font-size: 8px;
|
||||
color: var(--accent);
|
||||
letter-spacing: 1px;
|
||||
margin-left: auto;
|
||||
}
|
||||
|
||||
.memory-item-lg-delete {
|
||||
background: none;
|
||||
border: none;
|
||||
color: var(--text-tertiary);
|
||||
cursor: pointer;
|
||||
font-size: 14px;
|
||||
padding: 0 4px;
|
||||
line-height: 1;
|
||||
transition: color 0.2s;
|
||||
}
|
||||
|
||||
.memory-item-lg-delete:hover {
|
||||
color: var(--critical);
|
||||
}
|
||||
|
||||
.memory-item-lg-content {
|
||||
font-size: 13px;
|
||||
color: var(--text-primary);
|
||||
line-height: 1.5;
|
||||
cursor: default;
|
||||
}
|
||||
|
||||
.memory-item-lg-meta {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
margin-top: 6px;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
.memory-item-lg-tags {
|
||||
display: flex;
|
||||
gap: 4px;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
.memory-tag-lg {
|
||||
background: rgba(96, 205, 255, 0.08);
|
||||
color: var(--accent);
|
||||
padding: 1px 8px;
|
||||
border-radius: 4px;
|
||||
font-size: 11px;
|
||||
}
|
||||
|
||||
.memory-item-lg-time {
|
||||
font-size: 11px;
|
||||
color: var(--text-tertiary);
|
||||
margin-left: auto;
|
||||
}
|
||||
|
||||
.memory-empty-lg {
|
||||
text-align: center;
|
||||
padding: 40px 20px;
|
||||
color: var(--text-tertiary);
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
/* 记忆面板 toggle 在模态框 header 内 */
|
||||
.memory-toggle {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
cursor: pointer;
|
||||
user-select: none;
|
||||
}
|
||||
|
||||
.memory-toggle input {
|
||||
display: none;
|
||||
}
|
||||
|
||||
/* btn-danger-outline (记忆用) */
|
||||
.btn-danger-outline {
|
||||
background: none;
|
||||
border: 1px solid rgba(255, 82, 82, 0.3);
|
||||
color: #ff6b6b;
|
||||
border-radius: var(--radius-control);
|
||||
padding: 4px 10px;
|
||||
cursor: pointer;
|
||||
font-size: 12px;
|
||||
font-family: var(--font);
|
||||
transition: all 0.2s;
|
||||
}
|
||||
|
||||
.btn-danger-outline:hover {
|
||||
background: rgba(255, 82, 82, 0.1);
|
||||
border-color: rgba(255, 82, 82, 0.5);
|
||||
}
|
||||
|
||||
/* 记忆注入状态 */
|
||||
.memory-status {
|
||||
text-align: center;
|
||||
padding: 4px;
|
||||
font-size: 11px;
|
||||
color: var(--accent);
|
||||
opacity: 0.7;
|
||||
}
|
||||
|
||||
/* 响应式 */
|
||||
@media (max-width: 640px) {
|
||||
.memory-layout {
|
||||
flex-direction: column;
|
||||
}
|
||||
.memory-sidebar {
|
||||
width: 100%;
|
||||
max-height: 120px;
|
||||
border-right: none;
|
||||
padding-right: 0;
|
||||
padding-bottom: 10px;
|
||||
border-bottom: 1px solid var(--border-subtle);
|
||||
}
|
||||
.memory-categories {
|
||||
flex-direction: row;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
}
|
||||
|
||||
工作空间面板已改为 flex 子元素，自然占据宽度 */
|
||||
|
||||
Vendored
+6
-15
@@ -92,12 +92,6 @@ export interface FileContent {
|
||||
content: string;
|
||||
}
|
||||
|
||||
export interface RagSource {
|
||||
filename: string;
|
||||
score: number;
|
||||
text: string;
|
||||
}
|
||||
|
||||
export interface ChatMessage {
|
||||
role: 'user' | 'assistant';
|
||||
content: string;
|
||||
@@ -109,7 +103,6 @@ export interface ChatMessage {
|
||||
images?: string[];
|
||||
files?: ChatFile[];
|
||||
_fileContents?: FileContent[];
|
||||
ragSources?: RagSource[];
|
||||
stopped?: boolean;
|
||||
toolCalls?: ToolCallRecord[];
|
||||
}
|
||||
@@ -125,7 +118,7 @@ export interface ChatSession {
|
||||
|
||||
// ── Agent 记忆系统类型 ──
|
||||
|
||||
export type MemoryType = 'fact' | 'preference' | 'rule' | 'episode';
|
||||
export type MemoryType = 'fact' | 'preference' | 'rule';
|
||||
|
||||
export interface MemoryEntry {
|
||||
id: string;
|
||||
@@ -135,6 +128,7 @@ export interface MemoryEntry {
|
||||
tags: string[]; // 用于快速匹配
|
||||
source?: string; // 来源描述（如会话标题）
|
||||
sessionId?: string; // 关联的会话 ID
|
||||
embedding?: number[]; // 向量嵌入
|
||||
createdAt: number;
|
||||
updatedAt: number;
|
||||
lastUsedAt: number; // 最后一次被回忆的时间
|
||||
@@ -154,7 +148,7 @@ export interface MemoryExtractionResult {
|
||||
}>;
|
||||
}
|
||||
|
||||
// ── 知识库类型 ──
|
||||
// ── 向量存储类型（用于记忆向量索引）──
|
||||
|
||||
export interface VectorCollection {
|
||||
id: string;
|
||||
@@ -181,11 +175,7 @@ export interface SearchResult extends VectorItem {
|
||||
score: number;
|
||||
}
|
||||
|
||||
export interface DocInfo {
|
||||
docId: string;
|
||||
filename: string;
|
||||
chunkCount: number;
|
||||
}
|
||||
|
||||
|
||||
// ── 桌面 Bridge 类型 ──
|
||||
|
||||
@@ -286,7 +276,8 @@ export type StateKey =
|
||||
| 'toolCallingEnabled'
|
||||
| 'runCommandEnabled'
|
||||
| 'memoryEnabled'
|
||||
| 'memoryEntries';
|
||||
| 'memoryEntries'
|
||||
| 'embeddingModel';
|
||||
|
||||
// ═══════════════════════════════════════════════════════════
|
||||
// Tool Calling 类型
|
||||
|
||||
Reference in New Issue
Block a user