feat: 第四轮功能增强 - LLM 思维记忆优化、DevTools 记忆UI、9个新工具、5分钟自我思考

- 优化 LLM 思维方式和记忆方法（类别/重要性/关键词/相似度合并/衰减）
- DevTools 记忆查询 UI 重新设计（类别筛选/排序/星标/搜索）
- 新增 9 个 LLM 工具：calculator, datetime, file_ops, http_request, json_ops, text, random, crypto, markdown
- 管理员主对话 5 分钟自我思考增强（工具调用/记忆提取/记忆维护）

backend/ai-core/internal/memory/retriever.go

@@ -55,7 +55,7 @@ func NewRetriever(store *Store, embedder Embedder) *Retriever {
 }
 
 // Retrieve 检索与查询相关的记忆
-// 策略: 向量相似度 + 关键词匹配混合
+// 策略: 向量相似度 + 关键词匹配混合 → 按重要性降序返回
 func (r *Retriever) Retrieve(ctx context.Context, userID string, query string) ([]MemoryEntry, error) {
 	var allEntries []MemoryEntry
 	seen := make(map[string]bool)
@@ -63,7 +63,7 @@ func (r *Retriever) Retrieve(ctx context.Context, userID string, query string) (
 	// 1. 向量相似度检索
 	embedding, err := r.embedder.Embed(ctx, query)
 	if err == nil {
-		vecEntries, err := r.store.SearchByVector(ctx, userID, embedding, 5)
+		vecEntries, err := r.store.SearchByVector(ctx, userID, embedding, 8)
 		if err == nil {
 			for _, e := range vecEntries {
 				if !seen[e.ID] {
@@ -74,7 +74,7 @@ func (r *Retriever) Retrieve(ctx context.Context, userID string, query string) (
 		}
 	}
 
-	// 2. 关键词匹配检索（核心/重要记忆优先）
+	// 2. 关键词匹配检索（包含关键词标签匹配）
 	keywordEntries, err := r.keywordSearch(ctx, userID, query)
 	if err == nil {
 		for _, e := range keywordEntries {
@@ -90,13 +90,19 @@ func (r *Retriever) Retrieve(ctx context.Context, userID string, query string) (
 		recentEntries, err := r.store.Query(ctx, model.MemoryQuery{
 			UserID:   userID,
 			Priority: model.MemoryImportant,
-			Limit:    3,
+			Limit:    5,
 		})
 		if err == nil {
 			allEntries = recentEntries
 		}
 	}
 
+	// 4. 去重合并：对高度相似的记忆只保留Importance更高的
+	allEntries = r.deduplicate(allEntries)
+
+	// 5. 按重要性降序排列
+	sortByImportance(allEntries)
+
 	// 限制返回数量
 	if len(allEntries) > 10 {
 		allEntries = allEntries[:10]
@@ -105,7 +111,19 @@ func (r *Retriever) Retrieve(ctx context.Context, userID string, query string) (
 	return allEntries, nil
 }
 
-// keywordSearch 关键词匹配检索
+// RetrieveByCategory 按分类检索记忆
+func (r *Retriever) RetrieveByCategory(ctx context.Context, userID string, category model.MemoryCategory, limit int) ([]MemoryEntry, error) {
+	if limit <= 0 {
+		limit = 20
+	}
+	return r.store.Query(ctx, model.MemoryQuery{
+		UserID:   userID,
+		Category: category,
+		Limit:    limit,
+	})
+}
+
+// keywordSearch 关键词匹配检索（包含关键词标签匹配）
 func (r *Retriever) keywordSearch(ctx context.Context, userID string, query string) ([]MemoryEntry, error) {
 	// 查询最近的核心和重要记忆
 	entries, err := r.store.Query(ctx, model.MemoryQuery{
@@ -117,15 +135,27 @@ func (r *Retriever) keywordSearch(ctx context.Context, userID string, query stri
 		return nil, err
 	}
 
-	// 简单的关键词匹配过滤
+	// 关键词匹配过滤
 	var matched []MemoryEntry
 	queryLower := strings.ToLower(query)
 
 	for _, entry := range entries {
 		contentLower := strings.ToLower(entry.Content)
 		summaryLower := strings.ToLower(entry.Summary)
+
+		// 内容/摘要匹配
 		if strings.Contains(contentLower, queryLower) || strings.Contains(summaryLower, queryLower) {
 			matched = append(matched, entry)
+			continue
+		}
+
+		// 关键词标签匹配
+		for _, kw := range entry.Keywords {
+			if strings.Contains(queryLower, strings.ToLower(kw)) ||
+				strings.Contains(strings.ToLower(kw), queryLower) {
+				matched = append(matched, entry)
+				break
+			}
 		}
 	}
 
@@ -141,6 +171,14 @@ func (r *Retriever) keywordSearch(ctx context.Context, userID string, query stri
 			summaryLower := strings.ToLower(entry.Summary)
 			if strings.Contains(contentLower, queryLower) || strings.Contains(summaryLower, queryLower) {
 				matched = append(matched, entry)
+				continue
+			}
+			for _, kw := range entry.Keywords {
+				if strings.Contains(queryLower, strings.ToLower(kw)) ||
+					strings.Contains(strings.ToLower(kw), queryLower) {
+					matched = append(matched, entry)
+					break
+				}
 			}
 		}
 	}
@@ -148,5 +186,54 @@ func (r *Retriever) keywordSearch(ctx context.Context, userID string, query stri
 	return matched, nil
 }
 
+// deduplicate 去重合并：对高度相似的记忆只保留 Importance 更高的
+func (r *Retriever) deduplicate(entries []MemoryEntry) []MemoryEntry {
+	if len(entries) < 2 {
+		return entries
+	}
+
+	result := make([]MemoryEntry, 0, len(entries))
+	discarded := make(map[int]bool)
+
+	for i := 0; i < len(entries); i++ {
+		if discarded[i] {
+			continue
+		}
+		for j := i + 1; j < len(entries); j++ {
+			if discarded[j] {
+				continue
+			}
+			score := entries[i].SimilarityScore(&entries[j])
+			if score >= deDupThreshold {
+				// 保留更重要的那条
+				if entries[j].Importance > entries[i].Importance ||
+					(entries[j].Importance == entries[i].Importance && entries[j].Priority > entries[i].Priority) {
+					discarded[i] = true
+					break
+				} else {
+					discarded[j] = true
+				}
+			}
+		}
+		if !discarded[i] {
+			result = append(result, entries[i])
+		}
+	}
+
+	return result
+}
+
+// sortByImportance 按 Importance 降序, Priority 降序排列
+func sortByImportance(entries []MemoryEntry) {
+	for i := 0; i < len(entries); i++ {
+		for j := i + 1; j < len(entries); j++ {
+			if entries[j].Importance > entries[i].Importance ||
+				(entries[j].Importance == entries[i].Importance && entries[j].Priority > entries[i].Priority) {
+				entries[i], entries[j] = entries[j], entries[i]
+			}
+		}
+	}
+}
+
 // Ensure fmt is used
 var _ = fmt.Sprintf