feat: /compress 上下文压缩 + Skill 渐进式加载

src/renderer/components/input-area.ts

@@ -16,6 +16,7 @@ import { addToolCard, updateToolCard, clearToolCardsExternal, clearTerminalExter
 import { ChatDB } from '../db/chat-db.js';
 import { OllamaAPI } from '../api/ollama.js';
 import { runAgentLoop } from '../services/agent-engine.js';
+import { estimateTokens } from '../services/context-manager.js';
 import { searchMemories, buildMemoryContext, markMemoryUsed, extractMemoriesFromConversation, isMemoryEnabled } from '../services/memory-manager.js';
 import { showToolConfirm } from './tool-confirm-modal.js';
 import { logInfo, logStream, logError, logSuccess, logWarn } from '../services/log-service.js';
@@ -448,7 +449,7 @@ async function handleCompress(): Promise<void> {
 
   logInfo('上下文压缩: 开始');
 
-  // 取中间的消息做摘要（保留首尾各 2 条）
+  // 取中间的消息做摘要（保留首尾各 2 条，跳过已压缩的）
   const messages = currentSession.messages;
   const keepStart = 2;
   const keepEnd = 2;
@@ -456,10 +457,16 @@ async function handleCompress(): Promise<void> {
   const tail = messages.slice(-keepEnd);
   const middle = messages.slice(keepStart, messages.length - keepEnd);
 
-  const conversationText = middle.map(m => {
+  // 过滤掉已压缩的消息，避免重复压缩
+  const uncompressedMiddle = middle.filter(m => !m.compressed);
+  if (uncompressedMiddle.length === 0) {
+    showToast('中间消息已全部压缩，无需再次压缩', 'info');
+    return;
+  }
+
+  const conversationText = uncompressedMiddle.map(m => {
     const role = m.role === 'user' ? '用户' : 'AI';
     let content = m.content || '';
-    // 截断过长内容
     if (content.length > 500) content = content.slice(0, 500) + '...';
     if (m.toolCalls?.length) content += ` [工具调用: ${m.toolCalls.map(t => t.name).join(', ')}]`;
     return `${role}: ${content}`;
@@ -473,7 +480,7 @@ async function handleCompress(): Promise<void> {
       model,
       messages: [{
         role: 'user',
-        content: `请将以下对话摘要为一段简洁的上下文总结。保留关键信息：讨论的主题、得出的结论、用户的偏好、未完成的任务。用中文输出，不超过 500 字。\n\n对话记录：\n${conversationText}`
+        content: `请将以下对话摘要为一段简洁的上下文总结。保留关键信息：讨论的主题、得出的结论、用户的偏好、未完成的任务、关键工具调用结果。用中文输出，不超过 500 字。\n\n对话记录：\n${conversationText}`
       }],
       stream: true,
       think: false,
@@ -489,14 +496,17 @@ async function handleCompress(): Promise<void> {
       return;
     }
 
-    // 构建压缩后的消息列表
+    // 构建压缩后的摘要消息（标记 compressed）
     const summaryMsg: ChatMessage = {
       role: 'system',
-      content: `📋 以下是对之前对话的摘要（已压缩 ${middle.length} 条消息）：\n\n${summary}`,
-      timestamp: Date.now()
+      content: `📋 以下是对之前对话的摘要（已压缩 ${uncompressedMiddle.length} 条消息）：\n\n${summary}`,
+      timestamp: Date.now(),
+      compressed: true
     };
 
-    currentSession.messages = [...head, summaryMsg, ...tail];
+    // 保留已压缩的中间消息 + 新摘要
+    const alreadyCompressed = middle.filter(m => m.compressed);
+    currentSession.messages = [...head, ...alreadyCompressed, summaryMsg, ...tail];
 
     const db = state.get<ChatDB | null>(KEYS.DB);
     if (db) await db.saveSession(currentSession);
@@ -504,8 +514,8 @@ async function handleCompress(): Promise<void> {
     clearMessagesDOM();
     renderMessages();
 
-    logInfo(`上下文压缩完成: ${middle.length} 条 → 1 条摘要`);
-    showToast(`已压缩 ${middle.length} 条消息为摘要`, 'success');
+    logInfo(`上下文压缩完成: ${uncompressedMiddle.length} 条 → 1 条摘要`);
+    showToast(`已压缩 ${uncompressedMiddle.length} 条消息为摘要`, 'success');
   } catch (err) {
     logError('上下文压缩失败', (err as Error).message);
     showToast(`压缩失败: ${(err as Error).message}`, 'error');

src/renderer/services/agent-engine.ts

@@ -17,7 +17,7 @@ import { showToast } from '../components/toast.js';
 import { logInfo, logWarn, logError, logToolStart, logToolResult, logAgentLoop, logModelResponse } from './log-service.js';
 import { getWorkspaceDirPath } from '../components/workspace-panel.js';
 import { generateId } from '../utils/utils.js';
-import { buildContext, estimateTokens } from './context-manager.js';
+import { buildContext, estimateTokens, shouldAutoCompress, compressWithLLM, AUTO_COMPRESS_THRESHOLD } from './context-manager.js';
 import type {
   OllamaMessage,
   OllamaStreamChunk,
@@ -497,6 +497,22 @@ export async function runAgentLoop(
   messages.length = 0;
   messages.push(...contextResult);
 
+  // 自动压缩：当上下文 token 超过 context window 的 50% 时，调用 LLM 摘要压缩
+  if (shouldAutoCompress(messages, numCtx)) {
+    logInfo(`自动上下文压缩触发: tokens≈${estimateTokens(messages.map(m => m.content || '').join(''))} > ${Math.floor(numCtx * AUTO_COMPRESS_THRESHOLD)} (50% of ${numCtx})`);
+    try {
+      const compressed = await compressWithLLM(messages, api, model, { abortController });
+      if (compressed.length < messages.length || estimateTokens(compressed.map(m => m.content || '').join('')) < estimateTokens(messages.map(m => m.content || '').join(''))) {
+        messages.length = 0;
+        messages.push(...compressed);
+        logSuccess(`自动上下文压缩完成: 剩余 ${messages.length} 条消息`);
+      }
+    } catch (err) {
+      if ((err as Error).name === 'AbortError') throw err;
+      logWarn('自动上下文压缩失败，继续使用当前上下文', (err as Error).message);
+    }
+  }
+
   logInfo(`ReAct Agent Loop 启动: ${model}`, `工具: ${useTools ? '开启' : '关闭'}, 记忆: ${isMemoryEnabled() ? '开启' : '关闭'}, tokens≈${estimateTokens(messages.map(m => m.content || '').join(''))}`);
 
   // 迭代预算：从 state 读取，默认 85

src/renderer/services/context-manager.ts

@@ -1,10 +1,11 @@
 /**
- * Context Manager - 智能上下文管理 (v4.0)
- * 三层策略：滑动窗口 + 消息摘要压缩 + 记忆注入
+ * Context Manager - 智能上下文管理 (v5.0)
+ * 三层策略：滑动窗口 + LLM 摘要压缩 + 记忆注入
+ * 支持自动压缩与手动 /compress 触发
  */
 
-import type { OllamaMessage } from '../types.js';
-import { logInfo, logWarn } from './log-service.js';
+import type { OllamaMessage, OllamaStreamChunk } from '../types.js';
+import { logInfo, logWarn, logSuccess, logError } from './log-service.js';
 
 /** 粗略估算 token 数 */
 export function estimateTokens(text: string): number {
@@ -19,6 +20,13 @@ export function estimateTokens(text: string): number {
   return Math.ceil(chineseChars / 1.5 + otherChars / 4);
 }
 
+/** 自动压缩阈值：当消息 token 占 context window 比例超过此值时触发自动压缩 */
+export const AUTO_COMPRESS_THRESHOLD = 0.5;
+
+/** 压缩后保留首尾消息数 */
+const COMPRESS_KEEP_HEAD = 2;
+const COMPRESS_KEEP_TAIL = 2;
+
 export interface ContextBuildOptions {
   /** 滑动窗口大小（最近 N 条消息完整保留） */
   windowSize?: number;
@@ -41,10 +49,10 @@ const DEFAULT_OPTIONS: Required<ContextBuildOptions> = {
 };
 
 /**
- * 构建发送给模型的 messages
+ * 构建发送给模型的 messages（同步，滑动窗口）
  * 三层策略：
  *   a. 滑动窗口：最近 N 条消息完整保留
- *   b. 更早的消息：每 N 条压缩为一段摘要
+ *   b. 更早的消息：每 N 条压缩为一段摘要（快速文本截取）
  *   c. 系统 prompt 注入记忆上下文
  */
 export function buildContext(
@@ -64,9 +72,9 @@ export function buildContext(
   }
 
   // 提取已有的 system 消息
-  const existingSystem = allMessages.find(m => m.role === 'system');
-  if (existingSystem) {
-    systemContent += existingSystem.content;
+  const existingSystem = allMessages.filter(m => m.role === 'system');
+  for (const sys of existingSystem) {
+    systemContent += sys.content + '\n';
   }
 
   if (systemContent.trim()) {
@@ -77,7 +85,6 @@ export function buildContext(
   const nonSystemMessages = allMessages.filter(m => m.role !== 'system');
 
   if (nonSystemMessages.length <= opts.windowSize) {
-    // 消息不多，全部保留
     result.push(...nonSystemMessages);
     return result;
   }
@@ -85,11 +92,21 @@ export function buildContext(
   // 滑动窗口：最近 N 条
   const recentMessages = nonSystemMessages.slice(-opts.windowSize);
 
-  // 更早的消息：压缩为摘要
+  // 更早的消息：已压缩标记的保留原样，未压缩的做快速摘要
   const olderMessages = nonSystemMessages.slice(0, -opts.windowSize);
-  const summaries = summarizeOlderMessages(olderMessages, opts.summaryBatchSize);
+  const compressedMsgs = olderMessages.filter(m => m.compressed);
+  const uncompressedMsgs = olderMessages.filter(m => !m.compressed);
 
-  result.push(...summaries, ...recentMessages);
+  // 已压缩的消息直接保留
+  result.push(...compressedMsgs);
+
+  // 未压缩的消息做快速摘要
+  if (uncompressedMsgs.length > 0) {
+    const summaries = summarizeOlderMessages(uncompressedMsgs, opts.summaryBatchSize);
+    result.push(...summaries);
+  }
+
+  result.push(...recentMessages);
 
   // Token 估算和裁剪
   const trimmed = trimByTokenLimit(result, opts.maxTokens);
@@ -101,7 +118,132 @@ export function buildContext(
 }
 
 /**
- * 将较早的消息每 batchSize 条压缩为一段摘要
+ * 判断是否需要自动压缩
+ * 当总 token 数超过 context window 的 AUTO_COMPRESS_THRESHOLD 比例时返回 true
+ */
+export function shouldAutoCompress(messages: OllamaMessage[], numCtx: number): boolean {
+  const totalTokens = estimateTokens(messages.map(m => m.content || '').join(''));
+  const threshold = numCtx * AUTO_COMPRESS_THRESHOLD;
+  return totalTokens > threshold;
+}
+
+/**
+ * LLM 摘要压缩：调用模型对中间消息生成摘要
+ * 保留首尾各 keepHead/keepTail 条消息，中间用 LLM 摘要替换
+ *
+ * @returns 压缩后的消息列表（包含 compressed 标记的摘要消息）
+ */
+export async function compressWithLLM(
+  messages: OllamaMessage[],
+  api: { chatStream: (params: Record<string, unknown>, onChunk: (chunk: OllamaStreamChunk) => void, ac?: AbortController) => Promise<void> },
+  model: string,
+  options: {
+    keepHead?: number;
+    keepTail?: number;
+    maxSummaryTokens?: number;
+    abortController?: AbortController;
+  } = {}
+): Promise<OllamaMessage[]> {
+  const keepHead = options.keepHead ?? COMPRESS_KEEP_HEAD;
+  const keepTail = options.keepTail ?? COMPRESS_KEEP_TAIL;
+  const maxSummaryTokens = options.maxSummaryTokens ?? 500;
+
+  // 分离 system 和非 system 消息
+  const systemMsgs = messages.filter(m => m.role === 'system');
+  const nonSystemMsgs = messages.filter(m => m.role !== 'system');
+
+  if (nonSystemMsgs.length <= keepHead + keepTail + 2) {
+    logInfo('上下文压缩: 消息太少，跳过压缩');
+    return messages;
+  }
+
+  const head = nonSystemMsgs.slice(0, keepHead);
+  const tail = nonSystemMsgs.slice(-keepTail);
+  const middle = nonSystemMsgs.slice(keepHead, nonSystemMsgs.length - keepTail);
+
+  // 过滤掉已经压缩过的消息（避免重复压缩）
+  const uncompressedMiddle = middle.filter(m => !m.compressed);
+  if (uncompressedMiddle.length === 0) {
+    logInfo('上下文压缩: 中间消息已全部压缩，跳过');
+    return messages;
+  }
+
+  // 构建对话文本
+  const conversationText = uncompressedMiddle.map(m => {
+    const role = m.role === 'user' ? '用户' : 'AI';
+    let content = m.content || '';
+    if (content.length > 500) content = content.slice(0, 500) + '...';
+    if (m.tool_calls?.length) {
+      const toolNames = m.tool_calls.map(t => t.function.name).join(', ');
+      content += ` [工具调用: ${toolNames}]`;
+    }
+    return `${role}: ${content}`;
+  }).join('\n');
+
+  logInfo(`上下文压缩: 开始 LLM 摘要，${uncompressedMiddle.length} 条消息待压缩`);
+
+  let summary = '';
+  try {
+    await api.chatStream(
+      {
+        model,
+        messages: [{
+          role: 'user',
+          content: `请将以下对话摘要为一段简洁的上下文总结。保留关键信息：讨论的主题、得出的结论、用户的偏好、未完成的任务、关键工具调用结果。用中文输出，不超过 ${maxSummaryTokens} 字。\n\n对话记录：\n${conversationText}`
+        }],
+        stream: true,
+        think: false,
+        options: { num_ctx: 8192, temperature: 0.3 }
+      },
+      (chunk: OllamaStreamChunk) => {
+        if (chunk.message?.content) {
+          summary += chunk.message.content;
+        }
+      },
+      options.abortController
+    );
+  } catch (err) {
+    if ((err as Error).name === 'AbortError') {
+      logWarn('上下文压缩: LLM 调用被中止');
+      return messages;
+    }
+    logError('上下文压缩: LLM 调用失败', (err as Error).message);
+    return messages;
+  }
+
+  if (!summary.trim()) {
+    logWarn('上下文压缩: 模型未返回摘要内容');
+    return messages;
+  }
+
+  // 构建压缩后的摘要消息
+  const summaryMsg: OllamaMessage = {
+    role: 'system',
+    content: `📋 以下是对之前对话的摘要（已压缩 ${uncompressedMiddle.length} 条消息）：\n\n${summary}`,
+    compressed: true
+  };
+
+  // 保留已压缩的中间消息 + 新摘要
+  const alreadyCompressed = middle.filter(m => m.compressed);
+
+  const result: OllamaMessage[] = [
+    ...systemMsgs,
+    ...head,
+    ...alreadyCompressed,
+    summaryMsg,
+    ...tail
+  ];
+
+  const beforeTokens = estimateTokens(messages.map(m => m.content || '').join(''));
+  const afterTokens = estimateTokens(result.map(m => m.content || '').join(''));
+
+  logSuccess(`上下文压缩完成: ${messages.length} 条 → ${result.length} 条, tokens: ${beforeTokens} → ${afterTokens}`);
+
+  return result;
+}
+
+/**
+ * 将较早的消息每 batchSize 条压缩为一段摘要（快速文本截取，不调用 LLM）
  */
 function summarizeOlderMessages(messages: OllamaMessage[], batchSize: number): OllamaMessage[] {
   const summaries: OllamaMessage[] = [];
@@ -111,7 +253,8 @@ function summarizeOlderMessages(messages: OllamaMessage[], batchSize: number): O
     const summary = createQuickSummary(batch);
     summaries.push({
       role: 'system',
-      content: `【更早的对话摘要（第 ${Math.floor(i / batchSize) + 1} 部分）】\n${summary}`
+      content: `【更早的对话摘要（第 ${Math.floor(i / batchSize) + 1} 部分）】\n${summary}`,
+      compressed: true
     });
   }
 
@@ -127,12 +270,10 @@ function createQuickSummary(messages: OllamaMessage[]): string {
   for (const msg of messages) {
     const role = msg.role === 'user' ? '用户' : 'AI';
     const content = msg.content || '';
-    // 取前 100 字符作为摘要
     const preview = content.length > 100 ? content.slice(0, 100) + '...' : content;
     if (preview.trim()) {
       parts.push(`${role}: ${preview}`);
     }
-    // 如果有工具调用，记录工具名
     if (msg.tool_calls?.length) {
       const toolNames = msg.tool_calls.map(t => t.function.name).join(', ');
       parts.push(`  [工具: ${toolNames}]`);
@@ -149,7 +290,6 @@ function trimByTokenLimit(messages: OllamaMessage[], maxTokens: number): OllamaM
   let totalTokens = 0;
   const result: OllamaMessage[] = [];
 
-  // 从后往前添加（优先保留最近的消息）
   for (let i = messages.length - 1; i >= 0; i--) {
     const msg = messages[i];
     const msgTokens = estimateTokens(msg.content || '') +
@@ -164,7 +304,7 @@ function trimByTokenLimit(messages: OllamaMessage[], maxTokens: number): OllamaM
     }
 
     if (totalTokens + msgTokens > maxTokens && result.length > 2) {
-      break; // 已到限制，保留至少 system + 1 条消息
+      break;
     }
 
     totalTokens += msgTokens;

src/renderer/services/skill-manager.ts

@@ -13,6 +13,8 @@ export interface Skill {
   id: string;
   name: string;
   description: string;
+  /** Level 0 简短摘要（~100 tokens），用于索引展示 */
+  summary: string;
   trigger_keywords: string | null;
   tool_chain: string; // JSON: ToolChainStep[]
   success_count: number;
@@ -83,10 +85,13 @@ export async function extractSkillsFromToolRecords(
   const name = generateSkillName(chain, userMessage);
   const description = generateSkillDescription(chain, sessionTitle);
 
+  const summary = generateSkillSummary(chain, userMessage);
+
   const skill: Skill = {
     id: `skill_${generateId()}`,
     name,
     description,
+    summary,
     trigger_keywords: keywords,
     tool_chain: JSON.stringify(chain),
     success_count: 1,
@@ -157,28 +162,21 @@ export async function matchSkills(userMessage: string, limit = 3): Promise<Skill
 }
 
 /**
- * 从匹配的技能构建 system prompt 上下文
+ * 从匹配的技能构建 system prompt 上下文（Level 0：仅名称 + 描述索引）
+ * 完整工具链通过 skill_view 工具按需加载（Level 1）
  */
 export function buildSkillContext(skills: Skill[]): string {
   if (skills.length === 0) return '';
 
   const parts = skills.map(skill => {
-    let chain: ToolChainStep[] = [];
-    try { chain = JSON.parse(skill.tool_chain); } catch { return ''; }
-
-    const steps = chain.map((s, i) =>
-      `  ${i + 1}. ${s.name}（${s.description}）${s.args_hint ? ` — 参数: ${s.args_hint}` : ''}`
-    ).join('\n');
-
     const total = skill.success_count + skill.fail_count;
     const rate = total > 0 ? Math.round(skill.success_count / total * 100) : 100;
-
-    return `【技能】${skill.name}\n${skill.description}\n成功执行 ${skill.success_count} 次（成功率 ${rate}%）\n工具链：\n${steps}`;
+    return `• ${skill.name} — ${skill.description}（成功率 ${rate}%）`;
   }).filter(Boolean);
 
   if (parts.length === 0) return '';
 
-  return `【可用技能提示 — 以下技能曾成功完成过类似任务，可作为参考】\n\n${parts.join('\n\n')}\n\n提示：如果当前任务与某个技能匹配，可以参考其工具链步骤，但根据实际情况调整参数。`;
+  return `【可用技能 — 以下技能曾成功完成过类似任务】\n${parts.join('\n')}\n\n提示：使用 skill_view 工具查看某个技能的完整工具链和参数提示。`;
 }
 
 // ── 辅助函数 ──
@@ -269,12 +267,20 @@ function generateSkillDescription(chain: ToolChainStep[], sessionTitle: string):
   return `从会话「${sessionTitle}」中提取。步骤：${steps}`;
 }
 
+/** 生成技能简短摘要（Level 0，~100 tokens） */
+function generateSkillSummary(chain: ToolChainStep[], userMessage: string): string {
+  const toolNames = chain.map(s => s.name).join(' → ');
+  const msgPreview = userMessage.slice(0, 30).replace(/[\r\n]/g, ' ');
+  return `工具链: ${toolNames}。场景: ${msgPreview}`;
+}
+
 // ── 渐进式技能加载 ──
 
-/** 列出所有技能（渐进式 Level 0：只返回名称、描述、成功率） */
+/** 列出所有技能（渐进式 Level 0：只返回名称、描述摘要、成功率） */
 export async function listSkills(): Promise<Array<{
   name: string;
   description: string;
+  summary: string;
   success_rate: string;
   chain_preview: string;
 }>> {
@@ -292,6 +298,7 @@ export async function listSkills(): Promise<Array<{
     return {
       name: skill.name,
       description: skill.description,
+      summary: skill.summary || skill.description,
       success_rate: `${rate}%`,
       chain_preview: chainPreview
     };

src/renderer/types.d.ts

@@ -8,6 +8,8 @@ export interface OllamaMessage {
   reasoning_content?: string;
   tool_calls?: ToolCall[];
   tool_name?: string;
+  /** 标记此消息为 LLM 压缩摘要生成 */
+  compressed?: boolean;
 }
 
 export interface OllamaChatParams {
@@ -94,7 +96,7 @@ export interface FileContent {
 }
 
 export interface ChatMessage {
-  role: 'user' | 'assistant';
+  role: 'user' | 'assistant' | 'system';
   content: string;
   timestamp: number;
   model?: string;
@@ -107,6 +109,8 @@ export interface ChatMessage {
   _fileContents?: FileContent[];
   stopped?: boolean;
   toolCalls?: ToolCallRecord[];
+  /** 标记此消息为 LLM 压缩摘要生成 */
+  compressed?: boolean;
 }
 
 export interface ChatSession {