feat(suggest): 改造 createCompleteHandler 并行化 + generateLLMSuggestions 增强

- createCompleteHandler: 记忆提取+上下文拉取 Promise.all 并行
- generateLLMSuggestions: 新增 SuggestionContext 参数，构建增强 user message
- llmSuggestViaSaaS: 删除 2s 人为延迟（并行化后不再需要）
- 变量重命名 context→conversationContext 避免与 SuggestionContext 冲突

desktop/src/store/chat/streamStore.ts

@@ -36,6 +36,7 @@ import { useMessageStore } from './messageStore';
 import { useArtifactStore } from './artifactStore';
 import { llmSuggest } from '../../lib/llm-service';
 import { detectNameSuggestion, detectAgentNameSuggestion } from '../../lib/cold-start-mapper';
+import { fetchSuggestionContext, type SuggestionContext } from '../../lib/suggestion-context';
 
 const log = createLogger('StreamStore');
 
@@ -399,17 +400,24 @@ function createCompleteHandler(
       }
     }
 
-    // Async memory extraction (independent — failures don't block name detection)
+    // Parallel: memory extraction + intelligence context fetch
     const filtered = msgs
       .filter(m => m.role === 'user' || m.role === 'assistant')
       .map(m => ({ role: m.role, content: m.content }));
     const convId = useConversationStore.getState().currentConversationId;
-    getMemoryExtractor().extractFromConversation(filtered, agentId, convId ?? undefined)
+    const lastUserContent = typeof lastContent === 'string' ? lastContent : '';
-      .catch(err => log.warn('Memory extraction failed:', err));
 
-    intelligenceClient.reflection.recordConversation().catch(err => {
+    const suggestionContextPromise = fetchSuggestionContext(agentId, lastUserContent);
-      log.warn('Recording conversation failed:', err);
+
-    });
+    // Fire-and-forget background tasks
+    Promise.all([
+      getMemoryExtractor().extractFromConversation(filtered, agentId, convId ?? undefined)
+        .catch(err => log.warn('Memory extraction failed:', err)),
+      intelligenceClient.reflection.recordConversation()
+        .catch(err => log.warn('Recording conversation failed:', err)),
+      suggestionContextPromise,
+    ]).then(([, , context]) => {
+      // Conditional reflection (after context is ready)
       intelligenceClient.reflection.shouldReflect().then(shouldReflect => {
         if (shouldReflect) {
           intelligenceClient.reflection.reflect(agentId, []).catch(err => {
@@ -418,17 +426,18 @@ function createCompleteHandler(
         }
       });
 
-    // Follow-up suggestions (async LLM call with keyword fallback)
+      // Follow-up suggestions with enriched context
       const latestMsgs = chat.getMessages() || [];
       const conversationMessages = latestMsgs
         .filter(m => m.role === 'user' || m.role === 'assistant')
         .filter(m => !m.streaming)
         .map(m => ({ role: m.role, content: m.content }));
 
-    generateLLMSuggestions(conversationMessages, set).catch(err => {
+      generateLLMSuggestions(conversationMessages, set, context).catch(err => {
         log.warn('Suggestion generation error:', err);
         set({ suggestionsLoading: false });
       });
+    });
   };
 }
 
@@ -559,15 +568,32 @@ function parseSuggestionResponse(raw: string): string[] {
 async function generateLLMSuggestions(
   messages: Array<{ role: string; content: string }>,
   set: (partial: Partial<StreamState>) => void,
+  context?: SuggestionContext,
 ): Promise<void> {
   set({ suggestionsLoading: true });
 
   try {
     const recentMessages = messages.slice(-6);
-    const context = recentMessages
+    const conversationContext = recentMessages
       .map(m => `${m.role === 'user' ? '用户' : '助手'}: ${m.content}`)
       .join('\n\n');
 
+    // Build dynamic user message with intelligence context
+    const ctx = context ?? { userProfile: '', painPoints: '', experiences: '', skillMatch: '' };
+    const hasContext = ctx.userProfile || ctx.painPoints || ctx.experiences || ctx.skillMatch;
+    let userMessage: string;
+    if (hasContext) {
+      const sections: string[] = ['以下是用户的背景信息，请在生成建议时参考：\n'];
+      if (ctx.userProfile) sections.push(`## 用户画像\n${ctx.userProfile}`);
+      if (ctx.painPoints) sections.push(`## 活跃痛点\n${ctx.painPoints}`);
+      if (ctx.experiences) sections.push(`## 相关经验\n${ctx.experiences}`);
+      if (ctx.skillMatch) sections.push(`## 可用技能\n${ctx.skillMatch}`);
+      sections.push(`\n最近对话：\n${conversationContext}`);
+      userMessage = sections.join('\n\n');
+    } else {
+      userMessage = `以下是对话中最近的消息：\n\n${conversationContext}\n\n请生成 3 个后续问题。`;
+    }
+
     const connectionMode = typeof localStorage !== 'undefined'
       ? localStorage.getItem('zclaw-connection-mode')
       : null;
@@ -575,9 +601,9 @@ async function generateLLMSuggestions(
     let raw: string;
 
     if (connectionMode === 'saas') {
-      raw = await llmSuggestViaSaaS(context);
+      raw = await llmSuggestViaSaaS(userMessage);
     } else {
-      raw = await llmSuggest(context);
+      raw = await llmSuggest(userMessage);
     }
 
     const suggestions = parseSuggestionResponse(raw);
@@ -601,7 +627,7 @@ async function generateLLMSuggestions(
  * with non-streaming requests. Collects the full response from SSE deltas,
  * then parses the suggestion JSON from the accumulated text.
  */
-async function llmSuggestViaSaaS(context: string): Promise<string> {
+async function llmSuggestViaSaaS(userMessage: string): Promise<string> {
   const { saasClient } = await import('../../lib/saas-client');
   const { useConversationStore } = await import('./conversationStore');
   const { useSaaSStore } = await import('../saasStore');
@@ -611,9 +637,6 @@ async function llmSuggestViaSaaS(context: string): Promise<string> {
   const model = currentModel || (availableModels.length > 0 ? availableModels[0]?.id : undefined);
   if (!model) throw new Error('No model available for suggestions');
 
-  // Delay to avoid concurrent relay requests with memory extraction
-  await new Promise(r => setTimeout(r, 2000));
-
   const controller = new AbortController();
   const timeoutId = setTimeout(() => controller.abort(), 60000);
 
@@ -623,7 +646,7 @@ async function llmSuggestViaSaaS(context: string): Promise<string> {
         model,
         messages: [
           { role: 'system', content: LLM_PROMPTS_SYSTEM },
-          { role: 'user', content: `以下是对话中最近的消息：\n\n${context}\n\n请生成 3 个后续问题。` },
+          { role: 'user', content: userMessage },
         ],
         max_tokens: 500,
         temperature: 0.7,